Mobile Data Offloading May 2012 1/13

Home , Mobile data offloading, Spectral efficiency

Mobile Data Oﬄoading: A Tutorial

Jianwei Huang

Network Communications and Economics Lab (NCEL) Department of Information Engineering The Chinese University of Hong Kong (CUHK)

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 1 / 147 Slides Available Online

Google “Jianwei Huang”

http://jianwei.ie.cuhk.edu.hk/Files/MDO-Tutorial.pdf

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 2 / 147

Global Mobile Data Traffic, 2013 to 2018 Global Mobile DataOverall mobile Traﬃc data traffic is expected to grow to 15.9 exabytes per month by 2018, nearly an 11-fold increase over 2013. Mobile data traffic will grow at a CAGR of 61 percent from 2013 to 2018 (Figure 1).

Figure 1. Cisco Forecasts 15.9 Exabytes per Month of Mobile Data Traffic by 2018

Global Mobile Data Traﬃc Growth Projection (source: Cisco VNI Mobile 2014) The Asia Pacific and North America regions will account for almost two-thirds of global mobile traffic by 2018, as shown in Figure 2. Middle East and Africa will experience the highest CAGR of 70 percent, increasing 14-fold over the forecast period. Central and Eastern Europe will have the second highest CAGR of 68 percent, increasing Annual growth rate13-fold over61% the forecast period. The emerging market regions of Asia Pacific and Latin America will have CAGRs of 67∼ percent and 66 percent respectively. I Expected to reach 15.9 exabytes per month by 2018 I A 11-fold increase over 2013

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 3 / 147

Cellular MobileFigure Network 3: Historical Increases Capacity in Spectral Efficiency 16

If availableHistorical spectrum Increases is increasing in Spectral at 8% perEfficiency year and the (source:number of Femtoforum)cell sites is increasing at 7% per year and technology performance is improving at 12% per year then operators can expect their network capacities to increase – on average – at 29% Annualper grow year (1.08 rate x 1.07 x 1.12).36% If network capacity is growing at 29% per year and demand is growing currently∼ at 108% per year, then there is a significant gap, which begs for I Available spectrum band growth: 8% per year further innovation. I Cell site increase: 7% per year What other options exist? One possibility is architectural innovation. What if the I Spectrum efficiency growth: < 18% per year (2007 – 2013) definition of a “cell site” were radically changed, in such a way that the number of “sites” dramatically increased and the cost per unit of capacity (after adjusting for the inevitable lower utilisation of smaller108% sites)10 sign7%ificantly118 decreased?% = 1 36Similar% innovation has occurred before in the cellular industry.· Decad·es ago omni-directional sites were sectorised. Operators began adding “down tilt” to their urban site designs. Operators began introducing underlay and overlay sites. Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 4 / 147 The architects of GSM put in place a hierarchical cell structure, allowing macro, micro, and picocells to hand up or down a hierarchical chain of command to one another, so as to best serve the customer and most effectively carry traffic. Technologists and infrastructure manufacturers developed smart antenna solutions that extend coverage and increase capacity. Marty Cooper, developer of the Motorola Dyna-Tac, the first handheld cellular phone, observed that the number of radio conversations that are theoretically possible per square mile in all spectrum has doubled every two and half years for the past 104 years 17 . Femtocells represent the next step in a long history of architectural innovation.

Page 10 www.femtoforum.org Background Widening Supply-Demand Gap Network capacity growth vs Data traffic growth

29% vs 66% 36% vs. 61%

Slow networkNetwork Capacity capacity growth vs. Fast data traﬃcData growth Traffic

Lin Gao (NCEL, IE@CUHK) Mobile Data Offloading May 2012 1/13

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 5 / 147 Challenges: need to be cost eﬀective and easy to deploy.

How to Narrow the Gap: “Hard” Approaches

Expanding the network capacity through technology innovations

I Acquiring new spectrum bands

I More eﬃcient interference management through cooperations

I Developing high-frequency wireless technology

I Upgrading access technology (e.g., WCDMA LTE LTE-A) → → I Building more pico/micro/macro cell sites

I ...

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 6 / 147 How to Narrow the Gap: “Hard” Approaches

Expanding the network capacity through technology innovations

I Acquiring new spectrum bands

I More eﬃcient interference management through cooperations

I Developing high-frequency wireless technology

I Upgrading access technology (e.g., WCDMA LTE LTE-A) → → I Building more pico/micro/macro cell sites

I ... Challenges: need to be cost eﬀective and easy to deploy.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 6 / 147 Challenges: need to be user-friendly and network neutral.

How to Narrow the Gap: “Soft” Approaches

Reshaping the demand through economics and software

I Tired data pricing

I Capped or throttling (e.g., 128kbps if monthly usage >5GB)

I Time/Location/Congestion dependent pricing (e.g., delay coupons)

I Application speciﬁc optimization (e.g., network-friendly implem.)

I On device software client (e.g., “fuel gauge” meters)

I Content speciﬁc control (e.g., two-sided 1-800 pricing)

I ...

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 7 / 147 How to Narrow the Gap: “Soft” Approaches

Reshaping the demand through economics and software

I Tired data pricing

I Capped or throttling (e.g., 128kbps if monthly usage >5GB)

I Time/Location/Congestion dependent pricing (e.g., delay coupons)

I Application speciﬁc optimization (e.g., network-friendly implem.)

I On device software client (e.g., “fuel gauge” meters)

I Content speciﬁc control (e.g., two-sided 1-800 pricing)

I ... Challenges: need to be user-friendly and network neutral.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 7 / 147 Today’s Focus: Mobile Data Oﬄoading

Basic idea: deliver cellular traﬃc over Wi-Fi or Femtocell.

MU13 BS2

BS1 AP1

MU11 MU21 AP2

MU24 AP4 MU14

MU32 AP3

BS3 MU33 MU31

MU11 & MU21 AP1, MU24 AP2, MU31 & MU33 AP3, MU14 & MU32 AP4. → → → →

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 8 / 147 A Reality Check

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 9 / 147

As a percentage of total mobile data traffic from all mobile-connected devices, mobile offload increases from 45 percent (1.2 exabytes/month) in 2013 to 52 percent (17.3 exabytes/month) by 2018 (Figure 14). Without offload, Global mobile data traffic would grow at a CAGR of 65 percent instead of 61 percent. Offload volume is determined by smartphone penetration, dual-mode share of handsets, percentage of home-based mobile Internet use, and Global Mobilepercentage of dual Data-mode smartphone Oﬄoading owners with Wi-Fi fixed Internet access at home.

Figure 14. 52 Percent of Total Mobile Data Traffic Will Be Offloaded by 2018

Mobile Traﬃc Oﬄoading Prediction (source: Cisco VNI Mobile 2014)

The amount of traffic offloaded from smartphones will be 51 percent by 2018, and the amount of traffic offloaded from tablets will be 69 percent by 2018. Mobile oﬄoading will increase from 45% in 2013 to 52% in 2018 A supporting trend is the growth of cellular connectivity for devices such as tablets which in their earlier generation were limited to Wi-Fi connectivity only. With increased desire for mobility and mobile carriers offer of data plans catering to multi-device owners, we find that the cellular connectivity is on a rise albeit cautiously as the end users are testing the waters. As a point in case, we estimate that by 2018, 42 percent of all tablets will have a cellular Jianwei Huangconnection (CUHK) up from 34 percentMobile in 2013 Data (Figure Oﬄoading 15). (Tutorial) June 2015 10 / 147

OﬄoadingFigure Increases16. Mobile Data Traffic and withOffload Traffic, Technology2018

Mobile and Offloaded Traffic from Mobile-Connected Devices (source: Cisco VNI Mobile 2014) Trend 6: Comparing Mobile Network Speeds Globally, the average mobile network connection speed in 2013 was 1,387 Kbps. The average speed will grow at 4G networksa compound will annual attract growth rate of high-usage 13 percent, and will devices.exceed 2.5 Mbps by 2018. Smartphone speeds, generally third-generation (3G) and higher, are currently almost three times higher than the overall average. Smartphone The offloadingspeeds will nearly ratio double onby 2018, 4G reaching will 7 beMbps. the highest.

There is anecdotal evidence to support the idea that usage increases when speed increases, although there is often a delay between the increase in speed and the increased usage, which can range from a few months to several years. The Cisco VNI Forecast relates application bit rates to the average speeds in each country. Many Jianwei Huangof the (CUHK) trends in the resulting trafficMobile forecast Data can Oﬄoading be seen (Tutorial)in the speed forecast, such as the highJune growth 2015 rates for 11 / 147 developing countries and regions relative to more developed areas (Table 5).

Cellular small cells provide a uniform and reliable capacity layer and better coverage.

WiFi provides a more powerful capacity boost.

Technology co-location reduces Capex and Opex for oﬄoading.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 12 / 147 Case Study: AT&T in US

2.7B

1.23B

382.1M 85.5M

2009 2010 2011 2012

AT&T Annual Wi-Fi Connections (source: AT&T) Statistics of 2012

I 32, 000 Wi-Fi hotspots I 2.7 billion Wi-Fi connections (80% from AT&T’s mobiles) I 12.9 billion MB Wi-Fi data (5.2 billon MB from mobile devices) Seamless oﬄoading

I Auto-login of AT&T Wi-Fi hotspots I Auto-roaming to Fon Wi-Fi hotspots supported by Hotspot 2.0

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 13 / 147 AT&T Small Cell Strategy

AT&T Small Cell Strategies (source: AT&, Senza Fili)

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 14 / 147 Case Study: Sprint in US

Spring Boingo WiFi collaboration announced April 2015 (source: Internet)

Sprint customers access Boingo hotspots for free in 35 major US airports 40 out of 56 millions of Sprint’s customer devices can auto-authenticate with Boingo Wi-Fi hotspot connections

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 15 / 147 Case Study: China Mobile

China Mobile Cellular and WiFi Traﬃc (source: China Mobile, Senza Fili)

4.2 millions Wi-Fi APs deployed in 2012 Reach 6 millions in the next three years

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 16 / 147 Nanocell Architecture China Mobile NanoCell Strategy

Access to Cellular Core network and WLAN Core Network

MME EPC Nanocell Nanocell OAM HSS LTE

Nanocell Nanocell S-GW P-GW Backhaul GW

Internet WLAN WLAN Portal AC AAA

LTE and WLAN SoC Chip Trusted Secured Inference Flexible Unified Coordination Architecture Backhaul Environment Authentication

(source: China Mobile) 6

Combine LTE femtocell and carrier-grade Wi-Fi in the same box

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 17 / 147 Our Focus on Wi-Fi White paper Carrier Wi-Fi® for mobile operators

Wi-Fi traffic in cellular devices. We distinguish among four different types of Wi-Fi traffic for devices with cellular connectivity (Figure 3). Residential and enterprise are by far the prevalent forms of Wi-Fi access for mobile devices. Wi-Fi traffic on mobile devices – often referred to as Wi-Fi offload – is very valuable to mobile operators because, although they do not gain revenue from it, neither does it add any cost, because Wi-Fi access relies on access and backhaul infrastructure that the mobile operators do not own or operate. The subscriber and the enterprise pay for the offload, while mobile operators may see, and benefit from, lower traffic levels in their cellular networks.

Of course, only a part of mobile users’ data traffic is shifted from cellular to Wi-Fi networks, because the amount of traffic that subscribers generate is affected by the availability of Wi-Fi. Mobile users who cannot access Wi-Fi on their devices or in their home/office generate less mobile traffic on average, and instead shift their traffic away from mobile altogether: they rely more on fixed networks and devices. For this reason, in this report we do not refer to Wi-Fi traffic from mobile devices as offload, because only a portion of it can be defined as offload – i.e., data traffic shifted from cellular networks to Wi-Fi because of cost or performance advantages – and because it is difficult to estimate how big this portion is from the subscriber perspective. Figure 3. Cellular and Wi-Fi traffic from mobile devices. Public Wi-Fi hotspots are typically operated by cities, public or transportation agencies, coffee Source:Four Senza Types Fili of Wi-Fi (source: Senza Fili) shops, hotels and airports, and provide access for free or for a fee. These hotspots are highly valuable to mobile subscribers and operators alike, because they provide fast, cost-effective connectivity in areas with a high density of subscribers. However, they account for a much smaller percentage of traffic than residential and enterprise Wi-Fi traffic. Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 18 / 147

Defining carrier Wi-Fi. Carrier Wi-Fi is rapidly expanding as a new category of Wi-Fi access for mobile devices. In carrier Wi-Fi, a mobile operator or a service provider, such as a cable operator or an ISP, owns and operates the Wi-Fi infrastructure, manages access from users, and shares the infrastructure with roaming partners. Mobile operators may choose to integrate Wi-Fi within the cellular network, both within the RAN by co-locating cellular and Wi-Fi small cells, and in the core network by integrating authentication, subscriber management, billing, policy, and traffic management.

included, and cast them as a percentage of 4G (LTE) peak data rate (Figure 6). We do not expect that mobile operators will see peak rate throughput in any deployment – and in fact we expect average throughput to be substantially lower than peak rates. But we make the assumption that throughput for all technologies will equally depend on the peak rate. As the peak rate for Wi-Fi n (40 MHz channel, one spatial stream) is 43% higher than for 4G (10 MHz channel, 2x2 MIMO), we expect the Wi-Fi average throughput to be 43% higher than 4G. The per-bit TCO is calculated on the basis of the five-year capex and opex assumptions presented earlier in the paper (Figure 7).

3G. Not surprisingly, 3G is the most expensive technology, with a per-bit TCO more than four times as high as that of 4G – and ten times as high as Wi-Fi. The difference is only in part due to the lower spectral efficiency of HSPA compared to LTE. To a larger extent, the higher costs are due to the fact that we assume a deployment in 5 MHz channels without MIMO, which is a typical configuration for 3G small cells.

Wi-Fi. The per-bit cost for carrier Wi-Fi is less than half (43%) that for 4G. As in the 3G case, it Per-bit TCO of diﬀerent technology choices (source: Senza Fili) is not spectral efficiency that accounts for the lower cost (the spectral efficiency for 4G is higher), but the fact that Wi-Fi can typically use more spectrum. Although 4G can use wider channels, 10 MHz channels are commonly used, because of the limitations in spectrum availability that most operators face. On the other hand, a Wi-Fi AP can use both the 2.4 GHz and 5 GHz bands, and multiple channels. (However, in our analysis we assume that the Wi-Fi Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 19 / 147 AP uses only one channel.) If we were to use the same channel width for both Wi-Fi and 4G, the ratio of the per-bit costs would be reversed, but 4G small-cell deployments in 40 GHz are not likely to happen in the short term.

Adding 3G to Wi-Fi. The addition of 3G to Wi-Fi increases the TCO more than it increases capacity, so the per-bit costs are higher (52% of 4G’s, versus 43% of 4G costs for Wi-Fi only). But the combination makes the case for 3G small cells much more compelling, because that 52% for 3G and Wi-Fi is much lower than the per-bit cost of 3G only (435% of 4G).

Adding 4G to Wi-Fi. Wi-Fi and 4G is the winning combination, because the increase in per-bit TCO is less than the increase in capacity – although the savings over using Wi-Fi alone is small Figure 7. Per-bit TCO over a five-year period. Source: Senza Fili when measured against our benchmark of 4G-only per-bit TCO (42% for the Wi-Fi and 4G combination, versus 43% for Wi-Fi only). More notably, that 42% means operators can have 4G plus Wi-Fi for less than half the per-bit TCO of 4G alone. The TCO analysis predicts that adding 4G to Wi-Fi in Year 5 reduces the per-bit costs even further, to 39%.

THE BUSINESS CASE: WHAT IS THE VALUE OF WI-FI OFFLOAD? 6 Operatortechnology of choice. Beneﬁts

The Wi-Fi of!oad business opportunity map for mobile carriers

Relieve congestion Lower network CAPEX/OPEX Defensive: Cut costs Retain customers Seamless & automatic: + Reduce churn Value of everyday convenience Opportunistic: Additional services International roaming: Boost revenues Lifestyle & lower costs Attract new customers

Mobile operator’s business opportunities due to Wi-Fi oﬄoad (source: Aptilo) NEW MASS-MARKET WI-FI OFFLOAD SERVICE TYPES service provisioning point of view Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 20 / 147

vastly improve.

hotspots

10 User Beneﬁts

Faster connections Lower battery drain (when close to AP) Easier to use Reduced TCP handshake

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 21 / 147 Different Offloading Types

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 22 / 147 Two Types of Data Oﬄoading

User-initiated oﬄoading

I User decides when and how to oﬄoad

I When automatic oﬄoading is not possible or users’ judgements needed

Network-initiated oﬄoading

I Mobile operator makes the oﬄoading decision

I Seamless Wi-Fi

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 23 / 147 Seamless Wi-Fi

Near-term: user transparence

I Automatic handover from cellular to Wi-Fi

I Automatic authentication by Wi-Fi

I Traﬃc reroute to local Internet

Long-term: carrier-grade Wi-Fi

I Large bandwidth and high throughout based on latest Wi-Fi standards

I Tight integration with cellular network through new standards

I Traﬃc reroute to cellular operator’s core network

I Cellular operator has control over quality and service experiences

I Goes beyond data oﬄoad

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 24 / 147 Challenges of Wi-Fi Data Oﬄoading

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 25 / 147 Challenges of Wi-Fi Data Oﬄoading

Maturity of Wi-Fi-cellular integrations

Pricing of cellular and Wi-Fi services

Quality of Wi-Fi experiences

Deployment of Wi-Fi hotspots

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 26 / 147 Challenge 1: Maturity of Wi-Fi-Cellular Integrations

Manual Wi-Fi network selection and input of username/password

Tedious, time-consuming, and inconvenient

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 27 / 147 SEAMLESS WI-FI OFFLOAD: A BUSINESS OPPORTUNITY TODAY

CAPITALIZING ON THE POPULARITY OF WI-FI 1

3 per

SEAMLESS WI-FI OFFLOAD: ENHANCING THE USER EXPERIENCE Solutions Operator-speciﬁc on-device conﬁgurations (AT&T) Standards: HotSpot 2.0 (a video), NGH, ANDSF in 3GPP I Automatic Wi-Fi selection and login with strong security I Already supported by Apple iOS

Wi-Fi Sessions the network of a commercially network. Measurements show of Wi-Fi sessions in the course sessions grows steeply.

Increase of SIM-based Wi-Fi Oﬄoading (source: Aptilo) 3

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 28 / 147 3 Several possibilities

I Combined cellular and Wi-Fi service with a total monthly data cap

I Volume-capped cellular service with unlimited free Wi-Fi access

I Low cost Wi-Fi data service for customers without a cellular data plan

I Session-based or subscription-based Wi-Fi service only

Key Questions: how should the cellular operator

I Jointly design cellular and Wi-Fi pricing plans?

I Balance additional revenue and oﬄoading beneﬁts of Wi-Fi?

Challenge 2: Pricing of Cellular and Wi-Fi Services

Is Wi-Fi free? Flat-fee? Usage-based?

How is cellular charged?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 29 / 147 Key Questions: how should the cellular operator

I Jointly design cellular and Wi-Fi pricing plans?

I Balance additional revenue and oﬄoading beneﬁts of Wi-Fi?

Challenge 2: Pricing of Cellular and Wi-Fi Services

Is Wi-Fi free? Flat-fee? Usage-based?

How is cellular charged?

Several possibilities

I Combined cellular and Wi-Fi service with a total monthly data cap

I Volume-capped cellular service with unlimited free Wi-Fi access

I Low cost Wi-Fi data service for customers without a cellular data plan

I Session-based or subscription-based Wi-Fi service only

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 29 / 147 Challenge 2: Pricing of Cellular and Wi-Fi Services

Is Wi-Fi free? Flat-fee? Usage-based?

How is cellular charged?

Several possibilities

I Combined cellular and Wi-Fi service with a total monthly data cap

I Volume-capped cellular service with unlimited free Wi-Fi access

I Low cost Wi-Fi data service for customers without a cellular data plan

I Session-based or subscription-based Wi-Fi service only

Key Questions: how should the cellular operator

I Jointly design cellular and Wi-Fi pricing plans?

I Balance additional revenue and oﬄoading beneﬁts of Wi-Fi?

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 29 / 147 Key Question: When and how to offload traffic to Wi-Fi considering network conditions and application QoS requirements?

Challenge 3: Quality of Wi-Fi Experiences

Not all Wi-Fi hotspots are created equal

I 802.11b (11 Mbps) vs. 802.11n (150 Mbps)

Neither are the cellular networks

I 3G (EDGE Evolution, 1.6 Mbps) vs. 4G (HSPA/LTE, 300 Mbps) I Half of North American mobile connections on HSPA/LTE (2013)

Real world data rates vary based on time and location.

I Cellular can be more predicable or even sometimes faster than Wi-Fi I Also need to consider delay, consistency of delay, etc.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 30 / 147 Challenge 3: Quality of Wi-Fi Experiences

Not all Wi-Fi hotspots are created equal

I 802.11b (11 Mbps) vs. 802.11n (150 Mbps)

Neither are the cellular networks

I 3G (EDGE Evolution, 1.6 Mbps) vs. 4G (HSPA/LTE, 300 Mbps) I Half of North American mobile connections on HSPA/LTE (2013)

Real world data rates vary based on time and location.

I Cellular can be more predicable or even sometimes faster than Wi-Fi I Also need to consider delay, consistency of delay, etc.

Key Question: When and how to oﬄoad traﬃc to Wi-Fi considering network conditions and application QoS requirements?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 30 / 147 Key Question: How multiple cellular operators interact in a competitive oﬄoading market?

Challenge 4: Wi-Fi Hotspots Develoyment

Historically mobile operators do not own large Wi-Fi hotspots

Approach 1: Direct deployment

I AT&T (in US), China Mobile, PCCW I Pros: Easier to control and integrate I Cons: Costly and time consuming to deploy, diﬃculty in ﬁnding deployment locations, providing backhaul, and managing multiple service provisions.

Approach 2: Collaborations with Wi-Fi operators

I Sprint and Boingo, T-Mobile and iPass, DT/BT and FON I Pros: Fast and ﬂexible I Cons: Complicated to manage integration and revenue sharing

Approach 3: Dynamic network sharing and expansion

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 31 / 147 Challenge 4: Wi-Fi Hotspots Develoyment

Historically mobile operators do not own large Wi-Fi hotspots

Approach 1: Direct deployment

Approach 2: Collaborations with Wi-Fi operators

I Sprint and Boingo, T-Mobile and iPass, DT/BT and FON I Pros: Fast and ﬂexible I Cons: Complicated to manage integration and revenue sharing

Approach 3: Dynamic network sharing and expansion

Key Question: How multiple cellular operators interact in a competitive oﬄoading market?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 31 / 147 Recent Results

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 32 / 147 Recent Results

Technology issues:

I Delayed-aware oﬄoading

I Congestion-aware oﬄoading

I Predictive oﬄoading

Economics issues:

I Operator bargaining

I Oﬄoading market

I User-centric oﬄoading and onloading

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 33 / 147 Technology Issues for Wi-Fi Oﬄoading

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 34 / 147 Delayed Wi-Fi Oﬄoading

Wi-Fi have small coverages, and may not always be available.

Delayed Wi-Fi oﬄoading: delay a data transfer until the user enters a Wi-Fi hotspot.

Delay-tolerant applications (e.g., movie download and software update): Tolerate certain delays without sacriﬁcing user satisfactions.

Exploiting human mobility and traﬃc diversity

Save network cost and device energy

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 35 / 147 On-the-spot oﬄoading without delay

I Save 65% of network capacity I Save 55% of energy

Oﬄoad with 100 secs delay (insigniﬁcant)

I Save additional 2-3% of network capacity I Save additional 3% energy

Oﬄoad with 1 hour delay (signiﬁcant)

I Save additional 29% of network capacity I Save additional 20% energy I Suitable scenarios: software update, large ﬁle transfer, ...

Key Question: How to optimize the delayed-based oﬄoading?

How Much Can Wi-Fi Oﬄoad?

A recent study in Korean (LLYRC, “Mobile Data Oﬄoading: How Much Can WiFi Deliver?” ToN’13)

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 36 / 147 Offload with 100 secs delay (insignificant)

I Save additional 2-3% of network capacity I Save additional 3% energy

Oﬄoad with 1 hour delay (signiﬁcant)

I Save additional 29% of network capacity I Save additional 20% energy I Suitable scenarios: software update, large ﬁle transfer, ...

Key Question: How to optimize the delayed-based oﬄoading?

How Much Can Wi-Fi Oﬄoad?

A recent study in Korean (LLYRC, “Mobile Data Oﬄoading: How Much Can WiFi Deliver?” ToN’13)

On-the-spot oﬄoading without delay

I Save 65% of network capacity I Save 55% of energy

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 36 / 147 Offload with 1 hour delay (significant)

I Save additional 29% of network capacity I Save additional 20% energy I Suitable scenarios: software update, large ﬁle transfer, ...

Key Question: How to optimize the delayed-based oﬄoading?

How Much Can Wi-Fi Oﬄoad?

A recent study in Korean (LLYRC, “Mobile Data Oﬄoading: How Much Can WiFi Deliver?” ToN’13)

On-the-spot oﬄoading without delay

I Save 65% of network capacity I Save 55% of energy

Oﬄoad with 100 secs delay (insigniﬁcant)

I Save additional 2-3% of network capacity I Save additional 3% energy

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 36 / 147 Key Question: How to optimize the delayed-based oﬄoading?

How Much Can Wi-Fi Oﬄoad?

A recent study in Korean (LLYRC, “Mobile Data Oﬄoading: How Much Can WiFi Deliver?” ToN’13)

On-the-spot oﬄoading without delay

I Save 65% of network capacity I Save 55% of energy

Oﬄoad with 100 secs delay (insigniﬁcant)

I Save additional 2-3% of network capacity I Save additional 3% energy

Oﬄoad with 1 hour delay (signiﬁcant)

I Save additional 29% of network capacity I Save additional 20% energy I Suitable scenarios: software update, large ﬁle transfer, ...

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 36 / 147 How Much Can Wi-Fi Oﬄoad?

A recent study in Korean (LLYRC, “Mobile Data Oﬄoading: How Much Can WiFi Deliver?” ToN’13)

On-the-spot oﬄoading without delay

I Save 65% of network capacity I Save 55% of energy

Oﬄoad with 100 secs delay (insigniﬁcant)

I Save additional 2-3% of network capacity I Save additional 3% energy

Oﬄoad with 1 hour delay (signiﬁcant)

I Save additional 29% of network capacity I Save additional 20% energy I Suitable scenarios: software update, large ﬁle transfer, ...

Key Question: How to optimize the delayed-based oﬄoading?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 36 / 147 Delay Optimal WiFi Oﬄoading

Joint work with Man Hon Cheung (CUHK)

IEEE WiOpt 2013, IEEE JSAC 2015

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 37 / 147 Wi-Fi availability is location-dependent: (1) (0) (1) I = 4, 11, 13, 16 , = . L { } L L\L Mobility pattern: User moves from location l to l with prob p(l l). 0 0 | Deadline: A ﬁle of K bits must be sent by time T .

System Model

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A total of = 1,..., L locations L { } I Cellular is available at all locations

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 38 / 147 Mobility pattern: User moves from location l to l with prob p(l l). 0 0 | Deadline: A ﬁle of K bits must be sent by time T .

System Model

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A total of = 1,..., L locations L { } I Cellular is available at all locations Wi-Fi availability is location-dependent: (1) (0) (1) I = 4, 11, 13, 16 , = . L { } L L\L

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 38 / 147 Deadline: A ﬁle of K bits must be sent by time T .

System Model

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A total of = 1,..., L locations L { } I Cellular is available at all locations Wi-Fi availability is location-dependent: (1) (0) (1) I = 4, 11, 13, 16 , = . L { } L L\L Mobility pattern: User moves from location l to l with prob p(l l). 0 0 |

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 38 / 147 System Model

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Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 38 / 147 Question: Given the user’s mobility pattern and the WiFi availability, should the user remain idle, use cellular, or use Wi-Fi (if available) in each time slot?

Tradeoﬀ

Objective: To achieve a good tradeoﬀ between

I Reducing cellular usage: Wait till entering WiFi hotspots.

I Satisfying user’s QoS requirement: Transmit through cellular now if not meeting WiFi soon.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 39 / 147 Tradeoﬀ

Objective: To achieve a good tradeoﬀ between

I Reducing cellular usage: Wait till entering WiFi hotspots.

I Satisfying user’s QoS requirement: Transmit through cellular now if not meeting WiFi soon.

Question: Given the user’s mobility pattern and the WiFi availability, should the user remain idle, use cellular, or use Wi-Fi (if available) in each time slot?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 39 / 147 Markov Decision Process

Decision epochs: t = 1,..., T . ∈ T { } State: s = (k, l)

I k: remaining ﬁle size I l: location index

Action: a

I a = 0 (idle), 1 (cellular), 2 (Wi-Fi). (l) (0) I a = 0, 1 , if l (Wi-Fi is not available). ∈ A { } ∈ L (l) (1) I a = 0, 1, 2 , if l (Wi-Fi is available). ∈ A { } ∈ L

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 40 / 147 Markov Decision Process

Cellular usage cost at time t ∈ T ( 1, if a = 1 (using cellular), ct (k, l, a) = 0, otherwise.

Penalty for incomplete ﬁle transfer at T + 1:

cˆT +1(k, l) = h(k).

I Nondecreasing in k with h(0) = 0.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 41 / 147 Markov Decision Process

State transition probability: p(k , l ) (k, l), a 0 0 | p (k0, l0) (k, l), a = p(l0 l) p k0 (k, l), a , | | |

I Probability

( + (l) 1, if k0 = [k µ(l, a)] and a , p k0 (k, l), a = − ∈ A | 0, otherwise.

I µ(l, a): Data rate at location l with action a. I p(l 0 l): Obtain based on the mobility pattern of the MU. |

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 42 / 147 Markov Decision Process

Optimization problem: To minimize the expected total cellular usage plus the penalty for incomplete ﬁle transfer

" T # π X π π π min Es ct (st , δt (st )) +c ˆT +1(sT +1) . π Π ∈ t=1

I Policy π = (δt (k, l), k , l , t ): Decision rules at all the states and time slots.∀ ∈ K ∈ L ∈ T

We solve the problem using ﬁnite-horizon dynamic programming.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 43 / 147 Optimal Algorithm

We propose an optimal delayed Wi-Fi oﬄoading algorithm.

It does not have closed-form in general.

Diﬃcult to derive engineering insights.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 44 / 147 Theorem

The optimal policy π∗ has a single location-dependent threshold in k:

I At a location without WiFi: transmit using cellular if k kt∗(l), otherwise remain idle. ≥

I At a location with WiFi:

F If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗(l), otherwise transmit using WiFi.

F If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.

Special Case: Single Threshold Optimal Policy

Assume

I h(k) is a convex and nondecreasing, I The cellular and Wi-Fi data rates are location independent.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 45 / 147 I At a location without WiFi: transmit using cellular if k kt∗(l), otherwise remain idle. ≥

I At a location with WiFi:

F If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗(l), otherwise transmit using WiFi.

F If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.

Special Case: Single Threshold Optimal Policy

Assume

I h(k) is a convex and nondecreasing, I The cellular and Wi-Fi data rates are location independent.

Theorem

The optimal policy π∗ has a single location-dependent threshold in k:

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 45 / 147 I At a location with WiFi:

F If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗(l), otherwise transmit using WiFi.

F If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.

Special Case: Single Threshold Optimal Policy

Assume

I h(k) is a convex and nondecreasing, I The cellular and Wi-Fi data rates are location independent.

Theorem

The optimal policy π∗ has a single location-dependent threshold in k:

I At a location without WiFi: transmit using cellular if k kt∗(l), otherwise remain idle. ≥

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 45 / 147 F If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗(l), otherwise transmit using WiFi.

F If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.

Special Case: Single Threshold Optimal Policy

Assume

I h(k) is a convex and nondecreasing, I The cellular and Wi-Fi data rates are location independent.

Theorem

The optimal policy π∗ has a single location-dependent threshold in k:

I At a location without WiFi: transmit using cellular if k kt∗(l), otherwise remain idle. ≥

I At a location with WiFi:

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 45 / 147 F If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.

Special Case: Single Threshold Optimal Policy

Assume

I h(k) is a convex and nondecreasing, I The cellular and Wi-Fi data rates are location independent.

Theorem

The optimal policy π∗ has a single location-dependent threshold in k:

I At a location without WiFi: transmit using cellular if k kt∗(l), otherwise remain idle. ≥

I At a location with WiFi:

F If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗(l), otherwise transmit using WiFi.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 45 / 147 Special Case: Single Threshold Optimal Policy

Assume

I h(k) is a convex and nondecreasing, I The cellular and Wi-Fi data rates are location independent.

Theorem

The optimal policy π∗ has a single location-dependent threshold in k:

I At a location without WiFi: transmit using cellular if k kt∗(l), otherwise remain idle. ≥

I At a location with WiFi:

F If Wi-Fi rate is smaller than cellular rate: transmit using cellular if k ≥ kt∗(l), otherwise transmit using WiFi.

F If Wi-Fi rate is larger than cellular rate: always transmit using WiFi.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 45 / 147 Special Case: Single Threshold Optimal Policy

Convex penalty and location-independent data rates

20 20

18 18

16 16

(Mbits) 14 (Mbits) 14 k k 12 12

10 10

8 8

6 6

4 4 Remaining File Size Remaining File Size 2 2

0 0 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 Time slot t Time slot t Idle ( ), cellular ( ), Wi-Fi (+). ◦ •

Left: Location without Wi-Fi: µ1 = 2 Mbps (cellular).

Right: Location with Wi-Fi: µ1 = 2 Mbps (cellular), µ2 = 1 Mbps (WiFi).

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 46 / 147 General: Multi-threshold Optimal Policy Step penalty and location-dependent data rates

20 20

18 18

16 16

(Mbits) 14 (Mbits) 14 k k 12 12

10 10

8 8

6 6

4 4 Remaining File Size Remaining File Size 2 2

0 0 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 Time slot t Time slot t

Idle ( ), cellular ( ), Wi-Fi (+). ◦ •

Left: Location without Wi-Fi: µ(l, 1) = 2.1 Mbps.

Right: Location with Wi-Fi: µ(l, 1) = 3.1 Mbps > µ(l, 2) = 2.1 Mbps.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 47 / 147 Performance Evaluations

Compare three schemes:

1 ODWO: Our proposed Optimal delayed Wi-Fi oﬄoading.

2 On-the-spot oﬄoading: Oﬄoad to the Wi-Fi network whenever Wi-Fi is available.

3 Wiﬄer: prediction-based oﬄoading [Balasubramanian MobiSys’10].

Setting:

I Cellular and Wi-Fi data rate: random with mean = 3 Mbps and standard deviation = 1 Mbps.

I Probability that Wi-Fi is available = 0.7.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 48 / 147 Performance Evaluations

Setting: Deadline T = 3 min, penalty h(k) = k2, k . ∀ ∈ K

120 2.5 On−the−spot ODWO Wiffler Wiffler On−the−spot 100 ODWO 2

80 1.5

Total Cost 1 40 File Transfer Efficiency 0.5 20

0 0 20 25 30 35 40 45 50 55 60 20 30 40 50 60 70 File Size K (Mbytes) File Size K (Mbytes)

Deﬁne

probability of completing ﬁle transfer File transfer eﬃciency = average number of cellular time slots used

ODWO achieves the minimal cost and the highest ﬁle transfer eﬃciency.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 49 / 147 Summary

Algorithm design: General and threshold-based ODWO.

Analysis: Threshold optimal policy with convex penalty function and location-independent data rates.

Performance evaluation: ODWO achieves the minimal total cost and the highest ﬁle transfer eﬃciency.

Next Step: What about multiple users making decisions?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 50 / 147 Congestion-Aware Network Selection and Data Oﬄoading

Joint work with Man Hon Cheung and Richard Southwell (CUHK)

CISS 2014

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 51 / 147 A Much More General Model

Multiple users

Wi-Fi availability:

I Location-dependent: Limited Wi-Fi coverage. I User-dependent: Diﬀerent subscriptions and plans (e.g., Skype Wi-Fi). I Time-dependent: open or closed access mode at diﬀerent time.

Network-dependent switching time and switching cost:

I Switching time: Delay during handoﬀ. I Switching cost: Additional power consumption and QoS disruption.

Usage-based pricing

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 52 / 147 Cellular network is always available. Wi-Fi availability is user/location/time dependent: (i, l, t) . M ⊆ N

System Model

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Users = 1,..., I , networks = 1,..., N . I { } N { } Locations = 1,..., L , time slots = 1,..., T . L { } T { }

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 53 / 147 System Model

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Users = 1,..., I , networks = 1,..., N . I { } N { } Locations = 1,..., L , time slots = 1,..., T . L { } T { } Cellular network is always available. Wi-Fi availability is user/location/time dependent: (i, l, t) . M ⊆ N

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 53 / 147 Trajectory of a user determines its resource blocks (unshaded ones). Example:

I User 1’s trajectory: (14, 15, 16, 16) six resource blocks. ⇒ I User 2’s trajectory: (4, 8, 12, 16) ﬁve resource blocks. ⇒

Resource Block

MU 1 1 234 Time ϭϮ ϯ ϰ DhϮ Network 1

ϱ ϲϳϴ EĞƚǁŽƌŬϭ Network 2 ;ĞůůƵůĂƌͿ ϵ ϭϬ ϭϭ ϭϮ 1 234 MU 2 ϭϯ ϭϰ ϭϱ ϭϲ Time EĞƚǁŽƌŬϮ ;tŝͲ&ŝͿ Network 1 Dhϭ

Network 2

Resource block: a network available at a particular time.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 54 / 147 Example:

I User 1’s trajectory: (14, 15, 16, 16) six resource blocks. ⇒ I User 2’s trajectory: (4, 8, 12, 16) ﬁve resource blocks. ⇒

Resource Block

MU 1 1 234 Time ϭϮ ϯ ϰ DhϮ Network 1

ϱ ϲϳϴ EĞƚǁŽƌŬϭ Network 2 ;ĞůůƵůĂƌͿ ϵ ϭϬ ϭϭ ϭϮ 1 234 MU 2 ϭϯ ϭϰ ϭϱ ϭϲ Time EĞƚǁŽƌŬϮ ;tŝͲ&ŝͿ Network 1 Dhϭ

Network 2

Resource block: a network available at a particular time. Trajectory of a user determines its resource blocks (unshaded ones).

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 54 / 147 Resource Block

MU 1 1 234 Time ϭϮ ϯ ϰ DhϮ Network 1

ϱ ϲϳϴ EĞƚǁŽƌŬϭ Network 2 ;ĞůůƵůĂƌͿ ϵ ϭϬ ϭϭ ϭϮ 1 234 MU 2 ϭϯ ϭϰ ϭϱ ϭϲ Time EĞƚǁŽƌŬϮ ;tŝͲ&ŝͿ Network 1 Dhϭ

Network 2

Resource block: a network available at a particular time. Trajectory of a user determines its resource blocks (unshaded ones). Example:

I User 1’s trajectory: (14, 15, 16, 16) six resource blocks. ⇒ I User 2’s trajectory: (4, 8, 12, 16) ﬁve resource blocks. ⇒

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 54 / 147 Selection of Resource Blocks

MU 1 1 234 1 234 Time Time t Network 1 1 Route 1 MU 1 Route 3 Route 2 Network 2 2 network n 1 234 1 234 MU 2 Time Time t

Network 1 1 Route 1 Route 2 MU 2 Network 2 2 network n

Selection of resource blocks feasible route in the graph. ⇒ I Resource block: vertex in the graph

I Network selection between two time slots: edge in the graph

Examples: switching time = 1.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 55 / 147 (n) Utility= a µ (network throughput). m+x(n,t) (n) I µ : Capacity of network n . ∈ N I m: Congestion level. (n,t) I x : Background traﬃc of network n at time t . ∈ N ∈ T I a: Scaling weight.

(n) Payment= (n) µ ∆t (usage-based pricing). γ m+x(n,t)

I γ(n): Unit price of network n . ∈ N Surplus = utility - payment (in a single time slot):

µ(n) σ(n,t)(m) = (a γ(n)∆t) . − m + x(n,t)

I This depends on the resource block( n, t) one vertex v in route ri ⇒

User Suplus = Utility - Payment

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 56 / 147 (n) Payment= (n) µ ∆t (usage-based pricing). γ m+x(n,t)

I γ(n): Unit price of network n . ∈ N Surplus = utility - payment (in a single time slot):

µ(n) σ(n,t)(m) = (a γ(n)∆t) . − m + x(n,t)

I This depends on the resource block( n, t) one vertex v in route ri ⇒

User Suplus = Utility - Payment

(n) Utility= a µ (network throughput). m+x(n,t) (n) I µ : Capacity of network n . ∈ N I m: Congestion level. (n,t) I x : Background traﬃc of network n at time t . ∈ N ∈ T I a: Scaling weight.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 56 / 147 Surplus = utility - payment (in a single time slot):

µ(n) σ(n,t)(m) = (a γ(n)∆t) . − m + x(n,t)

I This depends on the resource block( n, t) one vertex v in route ri ⇒

User Suplus = Utility - Payment

(n) Payment= (n) µ ∆t (usage-based pricing). γ m+x(n,t)

I γ(n): Unit price of network n . ∈ N

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 56 / 147 User Suplus = Utility - Payment

(n) Payment= (n) µ ∆t (usage-based pricing). γ m+x(n,t)

I γ(n): Unit price of network n . ∈ N Surplus = utility - payment (in a single time slot):

µ(n) σ(n,t)(m) = (a γ(n)∆t) . − m + x(n,t)

I This depends on the resource block( n, t) one vertex v in route ri ⇒ Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 56 / 147 Switching Cost: computed based on edges in the route ri

I No channel switching: zero cost I Channel switching: positive cost

Payoﬀ = total surplus - total switching cost:

X v v X e ρi (r) = σ m (r) g . − v (ri ) e (ri ) ∈V ∈E

v v v I Surplus σ (m ): depends on vertex v, coupled across users in m . e I Switching cost g : depends on edge e, decoupled across users.

User Payoﬀ = Total Surplus - Total Switching Cost

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 57 / 147 Payoﬀ = total surplus - total switching cost:

X v v X e ρi (r) = σ m (r) g . − v (ri ) e (ri ) ∈V ∈E

v v v I Surplus σ (m ): depends on vertex v, coupled across users in m . e I Switching cost g : depends on edge e, decoupled across users.

User Payoﬀ = Total Surplus - Total Switching Cost

Switching Cost: computed based on edges in the route ri

I No channel switching: zero cost I Channel switching: positive cost

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 57 / 147 User Payoﬀ = Total Surplus - Total Switching Cost

Switching Cost: computed based on edges in the route ri

I No channel switching: zero cost I Channel switching: positive cost

Payoﬀ = total surplus - total switching cost:

X v v X e ρi (r) = σ m (r) g . − v (ri ) e (ri ) ∈V ∈E

v v v I Surplus σ (m ): depends on vertex v, coupled across users in m . e I Switching cost g : depends on edge e, decoupled across users.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 57 / 147 Network Selection Game

1 234 Time t

1 Route 1 MU 1 Route 3 Route 2 2 network n 1 234 Time t

1 Route 1 Route 2 MU 2 2 network n

Players: users.

Strategies: feasible routes

Each player chooses a route to maximize its payoﬀ

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 58 / 147 Key Questions

How should a user make a route choice?

Will users’ choices converge to a network equilibrium?

How fast does convergence happen?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 59 / 147 Result: The complexity of computing a better response is polynomial in terms of number of users (O(I 2)).

I Key idea: ﬁnding a better response update is equivalent of computing a shortest path in a graph.

How Should a User Choose its Route?

Better response update: a user chooses a new route to improve his payoﬀ, assuming that other users’ route choices are ﬁxed.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 60 / 147 How Should a User Choose its Route?

Better response update: a user chooses a new route to improve his payoﬀ, assuming that other users’ route choices are ﬁxed.

Result: The complexity of computing a better response is polynomial in terms of number of users (O(I 2)).

I Key idea: ﬁnding a better response update is equivalent of computing a shortest path in a graph.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 60 / 147 It would be nice to have the Finite improvement property (FIP), where better response updates always converge to a pure NE.

Result: every network selection game has the FIP.

I Key idea: show that the game is equivalent to a congestion game.

Will Users’ Greedy Choices Converge?

We want to achieve the Pure Nash equilibrium (NE):

I A route choice proﬁle r = ri , i where no user can perform a better response update { ∀ ∈ I}

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 61 / 147 Result: every network selection game has the FIP.

I Key idea: show that the game is equivalent to a congestion game.

Will Users’ Greedy Choices Converge?

We want to achieve the Pure Nash equilibrium (NE):

I A route choice proﬁle r = ri , i where no user can perform a better response update { ∀ ∈ I}

It would be nice to have the Finite improvement property (FIP), where better response updates always converge to a pure NE.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 61 / 147 Will Users’ Greedy Choices Converge?

We want to achieve the Pure Nash equilibrium (NE):

I A route choice proﬁle r = ri , i where no user can perform a better response update { ∀ ∈ I}

It would be nice to have the Finite improvement property (FIP), where better response updates always converge to a pure NE.

Result: every network selection game has the FIP.

I Key idea: show that the game is equivalent to a congestion game.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 61 / 147 How Fast Does Convergence Happen?

160

140

120

100

Total Number of Better Response Updates 0 5 10 15 20 25 30 Total Number of MUs I

The convergence scales well with the number of users.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 62 / 147 Performance Evaluations

Compare three schemes:

I NSG: Network selection game.

I On-the-spot oﬄoading (OTSO): Oﬄoad to the Wi-Fi network whenever Wi-Fi is available.

I Cellular-only: Use the cellular network all the time.

Setting:

I Grid topology with L = 16 possible locations.

I Cellular data rate: 300 Mbps (shared among L locations).

I Wi-Fi data rate: 54 Mbps (for one location).

I Cellular price > 0 and Wi-Fi price = 0.

I Switching time = 1.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 63 / 147 Impact of the Number of Users

Setting: Switching cost cswitch = 1 and cellular price = US $3/Gbyte.

80 NSG OTSO 70 Cellular−Only

30 Average Payoff per MU

10 5 10 15 20 25 30 Total Number of MUs I

Average payoff decreases with the level of contention. Cellular-only performs well under low traffic load. OTSO performs well under high traffic load and low cswitch.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 64 / 147 Impact of the Wi-Fi Availability Setting: I = 30 MUs and cellular price = US $6/Gbyte.

600 Class 1 Class 2 500 Class 3

400

300

200 Average Payoff per MU 100

0 50 250 450 650 850 Wi−Fi Data Rate (Mbps)

Class 1: Can access all the networks all the time. Class 2: Can access the cellular network all the time, and a Wi-Fi hotspot 50% of the time. Class 3: Can only access the cellular network.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 65 / 147 Summary

Network selection and data oﬄoading: First study on the interactions of multiple heterogeneous MUs.

Explicit modelling of user mobility, Wi-Fi availability, switching time and cost, and pricing.

Network Selection Game Analysis: FIP Convergence to a pure NE. ⇒ Key Question: what about incomplete network information?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 66 / 147 Delay-Aware Predictive Network Selection in Data Oﬄoading

Joint work with Haoran Yu, Man Hon Cheung (CUHK), Longbo Huang (Tsinghua)

IEEE GLOBECOM 2014

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 67 / 147 Network Selection with Cost-Delay Tradeoﬀ

An operator’s perspective: how to dynamically select networks for users to balance the long-term operation cost and traﬃc delay?

I Carrier-grade WiFi

Challenge: limited information on system randomness

We consider two cases:

1 Only having current slot information

2 Having both current and predicted future information

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 68 / 147 Ql (t): the amount of user l’ unserved traﬃc (queue length)

System Model

1 2 3

4 5 6

7 8 9

Multiple networks, locations, and users (similar as before)

I Network availability is location-dependent I Users randomly move across the locations with random traﬃc arrivals

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 69 / 147 System Model

1 2 3

4 5 6

7 8 9

Multiple networks, locations, and users (similar as before)

I Network availability is location-dependent I Users randomly move across the locations with random traﬃc arrivals

Ql (t): the amount of user l’ unserved traﬃc (queue length)

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 69 / 147 Case 1: Only Knowing Current Network Information

Operator only knows current user locations and queue lengths

I No statistic knowledge of users’ mobilities and traﬃc patterns

Queue length based optimization based on Lyapunov method

Intuition:

I When Ql (t) is small, suspending service does not lead to severe delay. Strategy: wait till enter Wi-Fi area

I When Ql (t) is large, suspending service incurs severe delay. Strategy: serve user l immediately even with a high operational cost

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 70 / 147 Delay-Aware Network Selection (DNS)

Delay-Aware Network Selection (DNS) Algorithm At each time slot t, the operator: Chooses the network selection vector α (t) that solves

L h X i minimize Ql (t)rl (α (t)) + Vc (α (t)) − l=1 variables αl (t) 0 , l . ∈ NSl (t) ∪ { } ∀ ∈ L Updates the queueing vector Q (t + 1) accordingly.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 71 / 147 Performance of DNS

Achieve[ O (1/V ) , O (V )] cost-delay tradeoﬀ (V : control parameter)

Conclusion: The operation cost can be pushed arbitrarily close to the optimal value, but at the expense of an increase in the traﬃc delay.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 72 / 147 Case 2: Predictive Network Selection

How to improve DNS with predicted future information?

I When we know the statistics of user mobility and traﬃc patterns

Solution: propose a novel frame-based Lyapunov optimization technique and design the GP-DNS algorithm

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 73 / 147 6

Algorithm 1 Greedy Network Selection for the k-th frame 290

Initialization: 280

Set i =1and initialize the network selection vectors for the k-th frame, DNS 1 270 i.e. β (t)=0, t k; GP−DNS (T=5) ∀ ∈T GP−DNS (T=10) Iteration: GP−DNS (T=25) 260 1: while i =1or βi (t) = βi 1 (t) for any t do # − ∈Tk 2: for τ = kT to kT + T 1 do 250 3: Update the network selection− vector βi+1 (τ): Average Operation Cost 4: Number all feasible network selection vectors for time slot τ as 240 α1 (τ) , α2 (τ) ,...,αM (τ); 230 5: for m =1to M do 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 Average Queue Length 4 6: Calculate the value of (21) under the network selectionGP-DNS vec- Algorithm x 10 tors βi+1 (kT) , βi+1 (kT +1),...,βi+1 (τ 1), αm (τ), βi (τ +1), βi (τ +2),...,βi (kT + T 1);− Fig. 3: Cost-Delay Tradeoff of DNS and GP-DNS. − 7: end for 16000 i+1 8: Choose β (τ)=α" (τ), where α" (τ) is the vector that 39.0% 14000 minimizes (21) in line 6 (If multiple vectors result in the same DNS GP−DNS (T=5) 12000 minimum value, choose the vector with the smallest index m); GP−DNS (T=10) 9: GP−DNS (T=25) end for 10000 10: i i +1; 40.5% ← 11: end while 8000 i 40.3% 12: α∗ (t) β (t) , t k; 6000

← ∀ ∈T Average Queue Length 13: return α∗ (t) , t k. 40.4% ∀ ∈T 4000

2000

0 240250 260 270 D. Greedy predictive delay-aware network selection Average Operation Cost

We observe that in problem (21)-(23) the set of feasible Fig.Cost-Delay 4: Delay Tradeoff Reduction of DNS through and GP-DNS Prediction. solutions has a size that is exponentially large in T . This is due to the fact that the state space of the random events grows updating rule and the fact that the number of feasible network exponentially with the size of the information window T .ToFutureselection information vectors improves is finite. the The cost-delay details are tradeoff given in [17]. reduce the computational complexity of P-DNS, we propose I If theWe operator observe pursues that the an complexity operation cost of of Algorithm 250, GP-DNS 1 is with onlyT = 25 a greedy algorithm, GP-DNS. In every frame, GP-DNS polynomial(prediction in windowT . Insize) particular, saves 40 a concrete.5% traffic computation-friendly delay over DNS. approximately solves problem (21)-(23) and its complexity is way of solving line 6 is also given in [17]. shown to be polynomial in T . Jianwei Huang (CUHK) V.Mobile N UMERICAL Data Offloading (Tutorial)RESULTS June 2015 74 / 147 The basic idea of the greedy algorithm is that, instead of globally searching for the optimal solution in problem (21)- In this section, we compare the performance of DNS (23), the operator iteratively updates the network selection and GP-DNS in terms of the average operation cost and vectors for different time slots until the values of all vectors the average queue length. We also study the amount of data converge. For example, when updating vector α (t), t , offloaded under these two algorithms. ∈Tk the operator treats all other vectors α (t!), t! k, t! = t, as We simulate DNS and GP-DNS in MATLAB with = ∈T " given constants, and chooses the feasible α (t) that minimizes 10 users, =8networks, and = 64 locations.|L| In the objective function in (21). particular, we|N | use network 1 to represent|S| the cellular network, The greedy algorithm is proposed as follows: which has the highest data rate, 672 Mbps (4G HSPA+), Greedy Predictive Delay-Aware Network Selection (G- and covers all the locations. The other networks are Wi-Fi networks, and the data rates are normally distributed random P-DNS): At time slot t = kT, k 0, 1,... , the operator: ∈{ } variables with means equal to 150 Mbps (IEEE 802.11n) and 50 Chooses network selection vectors α∗ (τ) , τ k, standard deviations equal to Mbps. These Wi-Fi networks • according to Algorithm 1. { } ∈T are randomly distributed spatially. Each Wi-Fi network covers at least one location and has a maximum coverage of four con- Updates the vector Q (kT + T ) according to (6). nected locations. We consider the transmission rate function • In order to avoid confusions, when updating the network rl (α (t)) defined in (4) and operation cost function c (α (t)) selection vector for time τ in the i-th iteration (line 3 to 8), we defined in (8). The unit operation cost of cellular network is 1, use αm (τ) (line 4) to represent the m-th feasible vector, and and those of other networks are uniformly distributed between use βi+1 (τ) (line 8) to represent the optimal network selection 0 and 1. Markovian dynamics is used to model users’ traffic vector updated in this iteration. The condition for ending the arrivals and locations. We run the experiment for 100000 slots iteration is that all network selection vectors converge, which and obtain the following results. In Figure 3, we plot the average operation cost against the is always achievable as shown in the following lemma. 13 Lemma 5: In Algorithm 1, for any Q (kT) and ω (t), t average queue length for DNS and GP-DNS. We obtain ∈ these cost-delay tradeoff curves by varying the values of V .14 k, there always exists a finite I such that for any i I we haveT βi (t)=βI (t) , t . ≥ As V increases, the average operation costs of the network ∀ ∈Tk The proof follows from the increment property12 of the 13T is the information window size defined in Section IV-A, which corresponds to the prediction capability of the operator. 12Note that the numbering rule in line 4 is the same for all rounds of 14Here, we choose θ =30to run GP-DNS. This value is related to other iteration. parameters’ settings. Summary

Prediction can signiﬁcantly improve network performance.

Need to carefully tradeoﬀ algorithm complexity and network performance.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 75 / 147 Economics Issues for Wi-Fi Oﬄoading

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 76 / 147 Bargaining-based Mobile Data Oﬄoading

Joint work with Lin Gao& Duozhe Li (CUHK) George Iosiﬁdis& Leandros Tassiulas (Yale University)

IEEE JSAC 2014

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 77 / 147 Mobile Data Oﬄoading

MU13 BS2

BS1 AP1

MU11 MU21 AP2

MU24 AP4 MU14

MU32 AP3

BS3 MU33 MU31

One mobile network operator (MNO) oﬄoads to multiple Access Point (APs).

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 78 / 147 Key Problems

Efficiency: How to offload traffic efficiently?

Fairness: How to share the beneﬁt among the MNO and APs fairly?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 79 / 147 Bargaining-based Solution

Bargaining is useful for resolving situations where

I Players have a common desirable to reach a mutual agreement.

I Players have individual payoﬀs.

I Allowing disagreement: no agreement may be forced on any player.

I There is a conﬂict of interest among players about the agreement.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 80 / 147 An Illustrative Example

Scenario: Player 1 sells a book to Player 2 at a price p

I Problem: Two players bargain for the price p.

Players’ payoﬀs: u1 = p, u2 = 1 p. − I Assumption: the book is worth 1 to player 2. I The set of feasible agreements: U = (u1, u2) u1 + u2 = 1 { | } The disagreement: D = (d1, d2) = (0, 0)

I Assumption: the book is worth 0 to player 1.

A bargaining solution is an outcome( v1, v2) U D ∈ ∪ What will be a proper bargaining solution?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 81 / 147 Nash Bargaining Theory

Nash bargaining theory: An axiom-based theory

I Pareto Eﬃciency

I Symmetry

I Invariant to Aﬃne Transformations

I Independence of Irrelevant Alternatives

Nash bargaining solution

I Unique solution that satisﬁes the Nash’s 4 axioms.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 82 / 147 When (d1, d2) = (0, 0): NBS is (v1, v2) = (0.5, 0.5);

When (d1, d2) = (0, 0.4): NBS is (v1, v2) = (0.3, 0.7);

Nash Bargaining Solution

Nash Bargaining Solution (NBS) Nash bargaining solution solves the optimization problem:

max (v1 d1) (v2 d2) v1,v2 − · − subject to (v1, v2) U D ∈ ∪ v1 d1, v2 d2 ≥ ≥

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 83 / 147 Nash Bargaining Solution

Nash Bargaining Solution (NBS) Nash bargaining solution solves the optimization problem:

max (v1 d1) (v2 d2) v1,v2 − · − subject to (v1, v2) U D ∈ ∪ v1 d1, v2 d2 ≥ ≥

When (d1, d2) = (0, 0): NBS is (v1, v2) = (0.5, 0.5);

When (d1, d2) = (0, 0.4): NBS is (v1, v2) = (0.3, 0.7);

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 83 / 147 System Model One Mobile Network Operator (MNO)

I Operating multiple macrocell base stations; I Serving many mobile users (MUs); N Access Point (APs)

I APs are geographically non-overlapping with each other;

MU13 BS2

BS1 AP1

MU11 MU21 AP2

MU24 AP4 MU14

MU32 AP3

BS3 MU33 MU31

Example: N = 4 APs.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 84 / 147 Decisions Variables

Variables:

I The traﬃc oﬄoaded to each AP; I The payment to each AP;

Traffic Offloading Profile: x = (x1,..., xN ) I xn: the traffic offloaded to AP n;

Payment Proﬁle: z = (z1,..., zN ) I zn: the payment to AP n;

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 85 / 147 Payoﬀs

MNO’s Payoﬀ: net cost reduction

N X U(x; z) = J(x) zn − n=1

I J(x): the MNO’s beneﬁt in terms of operational cost reduction PN I n=1 zn: the MNO’s total payment to APs

AP’s Payoﬀ: net proﬁt increase

Vn(xn; zn) = Qn(xn) + zn −

I Qn(xn): the AP n’s cost due to oﬀoloading I zn: the AP n’s proﬁt from serving the MNO

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 86 / 147 An efficient offloading decision maximizes the social welfare.

Social Welfare

Social Welfare: sum of the MNO’s and all APs’ payoﬀs

N X Ψ(x) = J(x) Qn(xn) − n=1

I The payment between the MNO and each AP is internal transfer and is canceled out.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 87 / 147 Social Welfare

Social Welfare: sum of the MNO’s and all APs’ payoﬀs

N X Ψ(x) = J(x) Qn(xn) − n=1

I The payment between the MNO and each AP is internal transfer and is canceled out.

An eﬃcient oﬄoading decision maximizes the social welfare.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 87 / 147 Starting Point: One-to-One Bargaining

One MNO and one AP One-to-One Bargaining Problem

max U(x; z) Vn(x; z) (x,z) · s.t. U(x; z) U0, V(x; z) V0 ≥ ≥

0 I U = 0: the disagreement of the MNO; 0 I V = 0: the disagreement of the AP;

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 88 / 147 Solving this allows us to see the relationship between AP’s payoﬀ (π) and the social welfare (Ψ(x)) clearly.

An Equivalent Formulation

Social Welfare = MNO’s Payoﬀ + AP’s Payoﬀ

Ψ(x) = U(x; z) + V(x; z)

Deﬁne AP’s payoﬀ as π = V(x; z)

Then MNO’s payoﬀ U(x; z) = Ψ(x) π − An Equivalent Bargaining max (Ψ(x) π) π (x,π) − · s.t. Ψ(x) π 0, π 0 − ≥ ≥

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 89 / 147 An Equivalent Formulation

Social Welfare = MNO’s Payoﬀ + AP’s Payoﬀ

Ψ(x) = U(x; z) + V(x; z)

Deﬁne AP’s payoﬀ as π = V(x; z)

Then MNO’s payoﬀ U(x; z) = Ψ(x) π − An Equivalent Bargaining max (Ψ(x) π) π (x,π) − · s.t. Ψ(x) π 0, π 0 − ≥ ≥

Solving this allows us to see the relationship between AP’s payoﬀ (π) and the social welfare (Ψ(x)) clearly.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 89 / 147 NBS for One-to-One Bargaining

One-to-One NBS

The NBS (xn∗, πn∗) for the one-to-one bargaining is

o 1 o x = x = arg maxx Ψ(xn), and π = Ψ(x ) n∗ n n n∗ 2 · n

o xn = arg maxxn Ψ(xn): Bargaining solution maximizes social welfare; π = 1 Ψ(xo): AP gets half of the generated social welfare; n∗ 2 · n o 1 o U = Ψ(xn ) πn∗ = 2 Ψ(xn ): the MNO gets half of the generated social welfare;− ·

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 90 / 147 General One-to-Many Bargaining

Sequential Bargaining: MNO bargains with all APs sequentially (in a predeﬁned order). Concurrent Bargaining: MNO bargains with all APs concurrently.

Completed Bargaining On-going Bargaining Future Bargaining

MNO MNO

AP 1 AP 5 AP 1 AP 5 AP 2 AP 4 AP 2 AP 3 AP 3 AP 4 (a) Sequential Bargaining (b) Concurrent Bargaining

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 91 / 147 AP Grouping

APs may form groups to bargain with the MNO jointly.

How will grouping aﬀect the payoﬀs of MNO and APs’?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 92 / 147 Sequential Bargaining

Bargain from user 1 to user N Sequential Nash Bargaining Solution (NBS)

x∗, π∗ = (xn∗, πn∗) n { } { } ∈N Sequential NBS The NBS x , π under the sequential bargaining is { ∗ ∗} ¯ o ∆n x∗ = x , π∗ = , n = 1, ..., N n n n 2 ∀

o I x = arg maxx Ψ(x): bargaining solution maximizes social welfare; I ∆¯ n: the virtual marginal social welfare generated by AP n;

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 93 / 147 Virtual Marginal Social Welfare

Virtual Marginal Social Welfare generated by AP n

1 1 ¯ X X ∆n(In+1; ...; IN ) ∆n = ... N n 2 − In+1=0 IN =0

Represents average marginal social welfare by AP n, assuming

I MNO has reached agreements with all APs 1, ...., n 1 (before n); − I MNO will reach agreement with each AP in n + 1, ..., N (after n) with a probability of 0.5. { }

Consider all possibilities (through the values of In+1 to IN )

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 94 / 147 MNO: Invariance to the Order Under the sequential bargaining, the bargaining order of APs does not affect the MNO’s payoff.

Impact of AP Ordering in Sequential Bargaining

AP: Early-Mover Advantage Under the sequential bargaining, an AP will obtain a higher payoﬀ if it bargains with the MNO earlier.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 95 / 147 Impact of AP Ordering in Sequential Bargaining

AP: Early-Mover Advantage Under the sequential bargaining, an AP will obtain a higher payoﬀ if it bargains with the MNO earlier.

MNO: Invariance to the Order Under the sequential bargaining, the bargaining order of APs does not aﬀect the MNO’s payoﬀ.

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 95 / 147 Inter-Grouping Benefit Under the sequential bargaining, group bargaining Improves the payoffs of all APs bargaining before the group, No impact on the APs bargaining after the group.

Impact of Grouping in Sequential Bargaining

Intra-Grouping Beneﬁt Under the sequential bargaining, group bargaining always beneﬁts the APs in the group.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 96 / 147 Impact of Grouping in Sequential Bargaining

Intra-Grouping Beneﬁt Under the sequential bargaining, group bargaining always beneﬁts the APs in the group.

Inter-Grouping Beneﬁt Under the sequential bargaining, group bargaining Improves the payoﬀs of all APs bargaining before the group, No impact on the APs bargaining after the group.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 96 / 147 Concurrent Bargaining

Bargain concurrently with user 1 to user N Concurrent Nash Bargaining Solution (NBS)

x∗, π∗ = (xn∗, πn∗) n { } { } ∈N Concurrent NBS The NBS x , π under the concurrent bargaining is { ∗ ∗}

o ∆e n x∗ = x , π∗ = , n = 1, ..., N n n n 2 ∀

o I x = arg maxx Ψ(x): bargaining solution maximizes social welfare;

I ∆e n = Ψ(x ∗ n, xn∗) Ψ(x ∗ n, 0): the actual marginal social welfare generated− by AP n−; −

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 97 / 147 Property of Concurrent NBS

Concurrently Moving Tragedy The payoﬀ of each AP under the concurrent bargaining equals to the worst-case payoﬀ that it can achieve under the sequential bargaining (as the last AP).

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 98 / 147 No Inter-Grouping Benefit Under the concurrent bargaining, grouping of APs does not affect the APs not in the group.

Impact of Grouping in Concurrent Bargaining

Intra-Grouping Beneﬁt Under the concurrent bargaining, grouping of APs always beneﬁts APs in the group.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 99 / 147 Impact of Grouping in Concurrent Bargaining

Intra-Grouping Beneﬁt Under the concurrent bargaining, grouping of APs always beneﬁts APs in the group.

No Inter-Grouping Beneﬁt Under the concurrent bargaining, grouping of APs does not aﬀect the APs not in the group.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 99 / 147 Simulations

Sequential Bargaining Concurrent Bargaining C Bargaining

AP 1 16 5 denotes the merged 16 16 AP 2 hgroup:i 5,6,7,8,9,10 . { } AP 3 14 14 14 AP 4 AP 5 12 12 12 AP 6 AP 7 10 10 10 AP 8 5 8 h i 8 8 AP 9

Payoﬀ of APO Group Payoﬀs of APOs 6 6 6 4 4 4 4 3

2 2 2 2 1 0 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 91234510678910 Grouping Structure Grouping Structure Group Structure

Left ﬁgure: Payoﬀs of APs under sequential bargaining

I Early-mover advantage I Positive intra-grouping effect, positive inter-grouping effect. Right figure: Payoffs of APs under concurrent bargaining

I Concurrently moving tragedy I Positive intra-grouping eﬀect, no inter-grouping eﬀect.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 100 / 147 Summary

Study an oﬄoading market with one MNO and multiple APs.

Propose two one-to-many bargaining protocols.

Analyze the impact of bargaining protocols and grouping structure.

Next Step: What happen if there are multiple MNOs?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 101 / 147 An Iterative Double Auction Mechanism for Mobile Data Oﬄoading

Joint work with Lin Gao (CUHK) George Iosiﬁdis& Leandros Tassiulas (University of Thessaly)

IEEE WiOpt 2013 (Best Paper Award), IEEE ToN 2015

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 102 / 147 Mobile Data Oﬄoading Market

MU13 BS2

BS1 AP1

MU11 MU21 AP2

MU24 AP4 MU14

MU32 AP3

BS3 MU33 MU31

Multiple MNOs and multiple APs Each MNO can lease multiple APs Each AP can oﬄoad for multiple MNOs

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 103 / 147 Key Problems

From the MNO’s Perspective: How much traﬃc should each MNO oﬄoad to each AP, and how much to pay?

From the AP owner’s Perspective: How much traﬃc should each AP oﬄoad for each MNO, and how much to charge?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 104 / 147 System Model

Each MNO is represented by one Base Station (BS) 1, ..., I : the set of BSs I , { } 1, ..., N : the set of APs N , { }

MU13 BS2

BS1 AP1

MU11 MU21 AP2

MU24 AP4 MU14

MU32 AP3

BS3 MU33 MU31

Example: = 1, 2, 3 and = 1, 2, 3, 4 . I { } N { }

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 105 / 147 For Each BS i ∈ I

xin: oﬄoading request to AP n

xi (xin, n ): oﬄoad request vector to all APs , ∀ ∈ N Ji (xi ): the utility (cost reduction) function of BS i

I Positive, increasing, and jointly strictly concave P I AP-specific: depending on x i , not just the total traffic n xin ∈I I Intuition: offloading cell-edge traffic will lead to more cost reduction

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 106 / 147 For Each AP n ∈ N

yin: oﬄoad admission for BS i

y (yin, i ): oﬄoad admission vector for all BSs; n, ∀ ∈ I Qn(y n): the cost function of AP n

I Positive, increasing, and jointly strictly convex.

Cn: capacity constraint

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 107 / 147 Market Outcome

A feasible market outcome is where BSs and APs reach an agreement:

xin = yin, n , i . ∀ ∈ N ∀ ∈ I

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 108 / 147 A Benchmark Problem

Social Welfare Maximization (Eﬃciency)

X X maximize Ji (xi ) Qn(y ) ...... Social Welfare − n i n ∈IP ∈N subject to (i) i yin Cn, n , ...... Capacity constraint ∈I ≤ ∀ ∈ N (ii) xin = yin, n , i , ...... Feasibility ∀ ∈ N ∈ I variables xi , y , n, i. n ∀ ∀

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 109 / 147 Socially Optimal Solution

Socially Optimal KKT

∂Ji (x i ) ∂Qn(y in) (A1) : µin = 0, (A2) : µin + λn = 0, ∂xin − ∂yin − X (A3) : λn yin Cn = 0, (A4) : µin (yin xin) = 0, · − · − i ∈I (A5) : xin = yin.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 110 / 147 Challenge: Information Asymmetry

The utility function Ji (xi ) is the private information of BS i:

I Not known by other BSs, APs, and any market coordinator

The cost function Qn(y n) is the private information of AP n: I Not known by other APs, BSs, and any market coordinator

It is diﬃcult to achieve eﬃciency (social welfare maximization).

I Conflict of interests: BSs want to offload more traffic with less payment, while APs want to admit less traffic with more payment.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 111 / 147 A First Throught

A traditional approach: Two-sided Market Double Auction → I A market controller or broker acts as the auctioneer;

I BSs and APs act as bidders;

I The auctioneer decides the allocation and payment rules such that all bidders truthfully disclose their private information.

Direct application of double auction does not work here

I Every bidder may have inﬁnite amount of private information due to the continuity of the utility/cost function.

I According to [Myerson’1983], there does not exist a double auction that possesses all the following properties:

F Eﬃciency F Individually rationality F Incentive compatibility F Budget balanced

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 112 / 147 Our Approach: Iterative Double Auction (IDA)

Our proposed approach: Iterative Double Auction

I Conducts one double auction in each round.

Next round

Auctioneer Auctioneer

Payment rule Updating Payment rule Allocation rule Allocation rule

Bidder Bidder

Disclore all private Signaling his private information information

Fig. Double Auction vs Iterative Double Auction

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 113 / 147 Question: What are the conditions that guarantee us to achieve the social optimal allocation?

How IDA Works? Key Steps

Step 1 (Rules): The auctioneer broadcasts the payment rule hi ( ) to · every BS i and the reimbursement rule ln( ) to every AP n; ·

Step 2 (Bidding): Every BS i determines his bids pin to every AP n. Every AP n determines his bid αin to every BS i. Both aim at maximizing their respective objectives.

Step 3 (Allocation): The auctioneer determines the allocation xin and yin between every BS i and AP n, aiming at maximizing a public auxiliary objective function:

X X αin 2 W (x, y) pin log xin y . , − 2 in i n ∈I ∈N

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 114 / 147 How IDA Works? Key Steps

Step 1 (Rules): The auctioneer broadcasts the payment rule hi ( ) to · every BS i and the reimbursement rule ln( ) to every AP n; ·

Step 2 (Bidding): Every BS i determines his bids pin to every AP n. Every AP n determines his bid αin to every BS i. Both aim at maximizing their respective objectives.

Step 3 (Allocation): The auctioneer determines the allocation xin and yin between every BS i and AP n, aiming at maximizing a public auxiliary objective function:

X X αin 2 W (x, y) pin log xin y . , − 2 in i n ∈I ∈N Question: What are the conditions that guarantee us to achieve the social optimal allocation?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 114 / 147 Analysis of IDA

Step 3: Allocation

Auctioneer Optimal KKT Socially Optimal KKT ()

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 115 / 147 Analysis of IDA

Step 2: Bidding

Individual Optimal Bids Socially Optimal Bids ()

* Step 3: Allocation

Auctioneer Optimal KKT Socially Optimal KKT ()

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 115 / 147 Analysis of IDA

Step 1: Rules

Socially Optimal Rules

* Step 2: Bidding

Individual Optimal Bids Socially Optimal Bids ()

* Step 3: Allocation

Auctioneer Optimal KKT Socially Optimal KKT ()

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 115 / 147 300 taking bidders. There are very interesting directions for future 250 work. First, one can study what is the impact of strategic, price-anticipating behavior in the market outcome. Similarly,

SW 200

− it is challenging to study how colluding behaviors will affect the algorithm. Also, it is important to consider practical 150 implementation issues such as how to hardwire this algorithm

100 to APs and BSs so as to communicate with the broker and IDA - ConvergenceSocial Welfare execute IDA algorithm in a real-time fashion. 50 REFERENCES 0 [1] Cisco, “Cisco Visual Networking Index: Global Mobile Data Traffic Convergence of IDA 0 20 40 60 80 100 120 140 Step − t Forecast Update”, Feb. 2012. The IDA algorithm converges to the socially optimal solution. [2] Bloomberg: “AT&T Pays 2 Billions to Qualcom for Spectrum”, 2009. [3] FemtoForum, “Femtocell Business Case Whitepaper”, 2009. Fig. 3. Evolution of social welfare produced by the IDA. [4] AT&T Press Release, “AT&T Expands Wi-Fi Hotzone Pilot Project to 0.4 Additional Cities”, July, 2010. [5] BT Wifi Press Release, “O2 Brings 3,000 New Wi-Fi Hotspots to iPhone 0.2 Customers”, July, 2008. [6] Cisco, “Making Wi-Fi as Secure and Easy to Use as Cellular”, 2012. 0 [7] Republic Wireless, http://www.republicwireless.com/ Nov. 2012. [8] Spectrum bridge, http://www.spectrumbridge.com/. −0.2 [9] R. B. Myerson, and M. A. Satterthwaite, “Efficient Mechanisms for x BS 1, AP 1: y −x − 11 11 Bilateral Trading”, Journal of Econ. Theory, vol. 29, no. 2, 1983. −0.4 BS 1, AP 2: y −x 21 12 [10] R. P. McAfee, “A Dominant Strategy Double Auction”, Journal of BS 2, AP 1: y −x

Gap y 12 21 Economic Theory, vol. 56, no. 2, pp. 434-450, 1992. −0.6 BS 2, AP 2: y −x [11] P. Maille, B. Tuffin, “Why VCG Auctions can Hardly be Applied to the 22 22 −0.8 Pricing of Interdomain and Ad hoc Networks”, in Proc. of NGI, 2007. [12] D. P. Palomar, M. Chiang, “A Tutorial on Decomposition Methods for −1 Network Utility Maximization ”, IEEE JSAC, vol. 24, no. 8, pp. 1439- 1451, 2006. −1.2 [13] F. P. Kelly, A. Maulloo, and D. Tan, “Rate Control for Communication 0 20 40 60 80 100 120 Step − t Networks: Shadow Prices, Proportional Fairness and Stability”, Journal of Oper. Res. Society, vol. 29, no. 3, pp. 237-252, 1998. [14] L. Johansen, “Price-Taking Behavior”, Econometrica, vol. 4, no. 7, 1977. Evolution of the gap between xin and yin, i.e., yin xin. Fig. 4. Evolution of the gap between requested (x) and admitted− data (y). [15] K. Lee, I. Rhee, J. lee, S. Chong, and Y. Yi, “Mobile Data Offloading: How Much Can WiFi Deliver?”, In Proc. of ACM CoNEXT, 2010. ρ11 =0.74. Finally, the payments of the BSs 1, 2 and 5 are [16] N. Ristanovic, J. Y. Le Boudec, A. Chaintreau, and V. Erramilli, “Energy Efficient Offloading of 3G Networks”, In Proc. of IEEE MASS, 2011. Jianwei Huangp (CUHK)11 =7.3, p21 =6Mobile.29, Data and Offloadingp51 =6 (Tutorial).63, respectively. NoticeJune 2015 116 / 147 [17] Informa Telecoms & Media, “Femtocell Market Status”, Feb. 2011. that BS 5 pays less than BS 1 although it offloads more data [18] M. H. Cheung, and J. Huang, “Optimal Delayed Wi-Fi Offloading”, in than the latter. Proc. of WiOpt, 2013. In Fig. 3, we plot the social welfare achieved by the [19] J. G. Andrews, et al., “Femtocells: Past, Present, and Future”, IEEE JSAC, vol. 30, no. 3, pp. 497-508, 2012. algorithm in each iteration and we observe that it gradually [20] H. S. Jo, P. Xia, and J. G. Andrews, “Downlink Femtocell Networks: converges to the optimal one, i.e. to the solution of SWM Open or Closed?”, In Proc. of IEEE ICC, 2011. (dotted line). In Fig. 4, we present the convergence of x and [21] S. Y. Yun, Y. Yi, D. H. Cho, and J. Mo , “The Economic Effects of Sharing Femtocells”, IEEE JSAC, vol. 30, no. 3, 2012. y. Specifically, we plot the gaps between x and y for 4 BS-AP [22] Y. Chen, J. Zhang, Q. Zhang, and J. Jia, “A Reverse Auction Frame- pairs, and observe that the gaps gradually converge to zero. work for Access Permission Transaction to Promote Hybrid Access in These two figures imply that our IDA algorithm elicits the Femtocell Network”, in Proc. of IEEE Infocom, 2012. [23] F. Pantisano, M. Bennis, W. Saad, and M. Debbah, “Spectrum Leasing true hidden information (i.e., the utility and cost functions), as an Incentive towards Uplink Macrocell and Femtocell Cooperation”, and converges to the socially optimal solution. IEEE JSAC, vol. 30, no.3, pp. 617-630, 2012. [24] L. Gao, G. Iosifidis, J. Huang, and L. Tassiulas, “Economics of Mobile VI. DISCUSSION AND CONCLUSIONS Data Offloading”, in Proc. of IEEE SDP Workshop, 2013. [25] S. Hua, X. Zhuo, and S. Panwar, “A Truthful Auction Based Incentive In this paper, we considered a market where MNOs lease Framework for Femtocell Access”, Arxiv, http://arxiv.org/abs/1210.5012. third-party owned WiFi or femtocell APs to offload their [26] H. Xu, J. Jin, and B. Li, “A Secondary Market for Spectrum”, in Proc. of IEEE Infocom, 2010. mobile data traffic. This is a promising solution for increasing [27] D. Yang, X. Fang, and G. Xue, “Truthful Auction for Cooperative the user perceived network capacity in a dynamic and scal- Communications”, in Proc. of ACM Mobihoc, 2011. able fashion, with low CAPEX and OPEX costs. Today, the [28] G. Iosifidis, and I. Koutsopoulos, “Double Auction Mechanisms for Resource Allocation in Autonomous Networks”, IEEE JSAC, vol.28, no.1, technologies to implement such solutions are already in place pp.95-102, Jan. 2010. (e.g., secure offloading methods). Data offloading can alleviate [29] C. Courcoubetis, and R. Weber, “Pricing Communication Networks: congestion of 2G/3G cellular networks, and also serve as a Economics, Technology and Modelling”, Willey Press, 2003. [30] L. Duan, J. Huang, and B. Shou, “Economics of Femtocell Service low-cost auxiliary technology for the emerging 4G networks. Provision”, IEEE TMC, forthcoming, 2013. We proposed an iterative double auction mechanism, which [31] D. Bertsekas, “Nonlinear Programming”, Athena Scientific, 1999. satisfies the desirable economic properties, and maximizes [32] N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, “Algorithmic the welfare of the market, under the assumption of price- Game Theory”, Cambridge University Press, 2007. Properties of IDA

Properties of IDA Eﬃcient

I The IDA mechanism achieves the social welfare maximization; Weakly Budget Balanced

I The auctioneer does not lose money by organizing an IDA; I If there is no capacity constraint, the auctioneer neither lose money nor gain money by organizing an IDA (strongly budget balanced); Incentive Compatible

I All bidders (price-taking) act in a truthful manner; Individually Rational

I All bidders achieve non-negative utilities.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 117 / 147 Summary

Consider multiple MNOs oﬄoading to multiple APs.

Iterative double auction mechanism that satisﬁes all desirable properties.

Next Step: Do we always oﬄoad traﬃc from cellular to Wi-Fi?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 118 / 147 Enabling Crowd-Sourced Mobile Internet Access

Joint work with Lin Gao (CUHK) George Iosiﬁdis& Leandros Tassiulas (Yale University)

IEEE INFOCOM 2014

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 119 / 147 Imbalance of Mobile Internet Access

Diﬀerent users have diﬀerent access technologies and access speeds: 3G/4G, femtocell, Wi-Fi.

Diﬀerent networks have diﬀerent congestion levels even at the same time and location.

How to eﬀectively take advantage of and integrate heterogeneous network access capabilities?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 120 / 147 Crowd-Sourced Mobile Internet Access

3G/4G

Wi-Fi

Femtocell

Share the best mobile internet connection(s) among users.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 121 / 147 Commercial Cases

Open Garden (http://opengarden.com)

M-87 (http://www.m-87.com/)

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 122 / 147 Key Problems

How to achieve an eﬃcient and fair network resource allocation?

I Who will download data for whom, and how much? I Who will route data from each host to each client, and how much?

How to encourage the user participation and cooperation?

I how to compensate the hosts and the relays for their eﬀorts?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 123 / 147 Crowd-Sourced Mobile Internet Access

Internet

WiFi Router 3G Base Station 4G Base Station

3G 4G

WiFi

WiFi Bluetooth

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 124 / 147 Crowd-Sourced Mobile Internet Access

Internet

WiFi Router 3G Base Station

4G Base Station

Data Data 3G 4G

WiFi

Relay WiFi Bluetooth Gateway Data Gateway (Host) Data (Host) Data Client Client

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 125 / 147 User-provided networking

I Mobile users can access internet through the hosting of other users.

Multi-hop accessing

I Mobile users can access internet through the relay of multiple devices.

Access bonding

I Mobile users can access internet through multiple access links.

Key Features

Three types of roles

I Host (Gateway): Downloading data from Internet

I Relay: Forwarding data for others

I Client: Consuming data

I A mobile user may have multiple roles

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 126 / 147 Key Features

Three types of roles

I Host (Gateway): Downloading data from Internet

I Relay: Forwarding data for others

I Client: Consuming data

I A mobile user may have multiple roles

User-provided networking

I Mobile users can access internet through the hosting of other users.

Multi-hop accessing

I Mobile users can access internet through the relay of multiple devices.

Access bonding

I Mobile users can access internet through multiple access links.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 126 / 147 System Model

A set of mobile users: = 1, 2, ..., I I { } For each user i : ∈ I

Internet

Ci ei pi C s ij, eij WiFi Bluetooth User i C r User j ji, eij

I ci , cij , cji , j : link capacity; ∈ I s r I ei , e , e , j : unit energy consumption; ij ij ∈ I I pi : usage-based pricing for accessing Internet.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 127 / 147 Client Model

When user i is a client. ∈ I

Internet

(i) (i) (i) (i) y yj y1 y2 i

User 2 User i User j User 1 (client) (i) (i) (i) y1 y2 ... yI

(i) I yj : the data downloaded via host j for client i; (i) P (i) I y = j yj : the total data consumed by client i; ∈I (i) I Ui y : the utility function of client i.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 128 / 147 I Downloading capacity constraint: yi ci . ≤

Host Model

When user i is a host (gateway). ∈ I

Internet

(1) yi (2) yi ... y (I) y (2) i (1) i y (j) yi i User 2 User i User j User 1 (host) y (3) y (4) i i User 4 User 3

(j) I yi : the data downloaded via host i for a client j; P (j) I yi = j yi : the total data downloaded via host i; ∈I I ei yi : the total energy consumption for downloading data; · I pi yi : the total payment for downloading data; ·

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 129 / 147 Host Model

When user i is a host (gateway). ∈ I

Internet

(1) yi (2) yi ... y (I) y (2) i (1) i y (j) yi i User 2 User i User j User 1 (host) y (3) y (4) i i User 4 User 3

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 129 / 147 P (n) P (n) I Relay capacity constraints: x cij , x cji n ij ≤ n ji ≤ P (n) (n) P (n) I Flow balance constraint: x + y = x , n j ji i j ij ∈ I

Relay Model When user i is a relay. ∈ I

Internet

x (n) ij , n=1,...,I User i x (n) User j (relay) ji , n=1,...,I

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 130 / 147 Relay Model When user i is a relay. ∈ I

Internet

x (n) ij , n=1,...,I User i x (n) User j (relay) ji , n=1,...,I

(n) I xij, n : the data relayed from user i to user j, for client n; ∈I r P (n) I e x : total energy consumption for receiving data from user j; ji · n ji s P (n) I e x : total energy consumption for sending data to user j. ij · n ij P (n) P (n) I Relay capacity constraints: x cij , x cji n ij ≤ n ji ≤ P (n) (n) P (n) I Flow balance constraint: x + y = x , n j ji i j ij ∈ I Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 130 / 147 User Payoﬀ

Payoﬀ of each user i : ∈ I

Ji (xi , y ) = Ui Pi Ei i − − (n) I y i = yi n : Downloading matrix; { } ∈I (n) I x i = xij j,n : Relaying matrix; { } ∈I I Ui : Utility of user i (as a client);

I Pi : Total payment of user i (as a host for internet access);

I Ei : Total energy consumption of user i (as a host and/or relay);

To maximize the payoﬀ, each user only wants to be a client, but not as a host or relay.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 131 / 147 Our Goal

Mechanism design to address incentive, eﬃciency, and fairness issues

I Encouraging the user participation and cooperation;

I Achieving an eﬃcient and fair network resource allocation.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 132 / 147 Incentive Design Challenges

Users may not want to participate in the crowd-sourced system

I For example, those without a demand for Internet access; Users may not want to download or relay data for others

I For example, user i may not want to download data for user 4.

Internet

(1) yi (2) yi ... y (I) y (2) i (1) i y (j) yi i User 2 User i User j User 1 (host) X y (3) i User 4 User 3

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 133 / 147 Solution: Virtual Currency

Key idea: User pays certain virtual currency to those who send data to him (I give you money, you give me data).

(1) (n) (I) xji ... xji ... xji

z (1) ... z (n) ... z (I) User j ji ji ji User i

(n) zji : the virtual price that user i pays j for receiving data (of client n);

P z(n) x(n): the total virtual money that user i pays j n ji · ji

Jianwei Huang (CUHK) Mobile Data Offloading (Tutorial) June 2015 134 / 147 Modified Payoff with Virtual Currency

Modiﬁed payoﬀ of each user i : ∈ I

Ji (xi , y , zi ) = Ui Pi Ei + Vi i − − (n) I z i = zij j,n : Virtual payment matrix; { } ∈I I Vi : Total virtual currency evaluation of user i;

Modiﬁed payoﬀ maximization takes care of incentive issues.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 135 / 147 Eﬃciency and Fairness Issues

How to achieve an eﬃcient and fair network resource allocation?

I Eﬃciency: The aggregate payoﬀ of all users is maximised.

I Fairness: Every user achieves a satisfactory payoﬀ;

Our Solution: Nash Bargaining Solution

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 136 / 147 Nash Bargaining Solution

Nash Bargaining Problem (NBP) 0 max Πi (Ji (xi , y i , zi ) Ji ) xi ,y ,zi , i ∈I − i ∀ 0 0 s.t., (a) Ji J (J : disagreement point) ≥ i i (b) Capacity constraints; (c) Flow balance constraint; (d) Virtual current budget constraint.

The NBP problem has a unique optimal solution.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 137 / 147 Nash Bargaining Implementation

Centralized Implementation

I A central control node collects all the required network information, and computes the Nash bargaining solution.

Decentralized Implementation

I Iterative updating: Users update their individual decisions sequentially and repeatedly, and signals to neighbors until convergence.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 138 / 147 Simulation

An example with 6 nodes

I Blue Bar: Downloading/relaying data; I Red Bar: Consuming data;

1.242 2 1.242 2 1.240 2 1.240 2 0.0 0.0 2.295 2.295 0.337 0.239 0.3372.247 0.239 2.247 1 1 5 51 0.097 1 0.0975 5 1.704 1.704 1.242 1.242 2.295 2.2951.143 1.143 0.304 0.304 2.295 2.295 0.059 0.0591.880 1.880 0.350 0.350 2.295 6 2.295 6 4 2.245 46 0.3662.245 6 0.3664 4 0.059 0.059 0.059 0.059 0.046 0.046 3 3 3 0.410 3 0.410 0.059 0.059 0.044 0.044

Standalone (Independent)Standalone (Independent) UPN Bargained UPN Bargained Operation Operation Operation Operation

Left: Independent Operation. Right: Crowd-sourced Operation.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 139 / 147 Summary

We study the crowd-sourced mobile internet access system, in particular, we answer

I How to achieve an eﬃcient and fair network resource allocation? I How to encourage the user participation and cooperation?

We propose a Nash bargaining solution with virtual currency, which addresses the incentive, eﬃciency, and fairness issues.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 140 / 147 Going Beyond Oﬄoading

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 141 / 147 Blurring the Cellular/Wi-Fi Boundary

Mi-Fi turn cellular signal into Wi-Fi signals

Social bandwidth trading: Karma

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 142 / 147 Why Wi-Fi Complements Cellular?

Wi-Fi can be the primary access technology with cellular as a coverage supplement

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 143 / 147 It’s Up to You and Me.

Where Will Wi-Fi Go in The Future?

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 144 / 147 Where Will Wi-Fi Go in The Future?

It’s Up to You and Me.

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 144 / 147 Related Publications

G. Iosiﬁdis, L. Gao, J. Huang, and L. Tassiulas, “An Double Auction Mechanism for Mobile Data Oﬄoading Markets,” IEEE/ACM Transaction on Networking, 2015 (conference version received Best Paper Award in IEEE WiOPT 2013)

M. Cheung and J. Huang, “DAWN: Delay-Aware Wi-Fi Oﬄoading and Network Selection,” IEEE Journal on Selected Areas in Communications, June 2015

L. Gao, G. Iosiﬁdis, J. Huang, L. Tassiulas, and D. Li, “Bargaining-based Mobile Data Oﬄoading,” IEEE Journal on Selected Areas in Communications, June 2014

G. Iosiﬁdis, L. Gao, J. Huang, and L. Tassiulas, “Enabling Crowd-Sourced Mobile Internet Access,” IEEE INFOCOM, May 2014

L. Gao, G. Iosiﬁdis, J. Huang, and L. Tassiulas, “Hybrid Data Pricing for Network-Assisted User-Provided Connectivity,” IEEE INFOCOM, May 2014

H. Yu, M. Cheung, L. Huang, and J. Huang, “Predictive Delay-Aware Network Selection in Data Oﬄoading,” IEEE GLOBECOM, December, 2014

M. Cheung, R. Southwell, and J. Huang, “Congestion-Aware Network Selection and Data Oﬄoading” (invited), CISS, March 2014

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 145 / 147 More Information

http://ncel.ie.cuhk.edu.hk/content/wiﬁ-data-oﬄoading

http://ncel.ie.cuhk.edu.hk/content/user-provided-networks

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 146 / 147 Contact

Google “Jianwei Huang”

http://jianwei.ie.cuhk.edu.hk/

Jianwei Huang (CUHK) Mobile Data Oﬄoading (Tutorial) June 2015 147 / 147