4-K Cloud Gaming
COMMUNICATION NETWORK (ENSC 835)
On the viability of 4K-Cloud Gaming on Wi-Fi SPRING 2018 PROJECT REPORT
Ahmed, Haris | [email protected] | 301336579 Miaze, Faisal|[email protected]| 301324167 https://ensc835team2.wordpress.com/
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Contents List of Figures ...... 2 List of Abbreviations ...... 3 List of Nodes ...... 4 Acknowledgement ...... 5 Abstract ...... 6 1. Introduction ...... 6 2. Review of Literature ...... 7 3. Wireless LAN 802.11n ...... 8 4. Modeling 4k cloud-gaming ...... 8 4.1 OBTAINING VIDEO TRACES ...... 10 4.2 MODELEING SERVER-SIDE SUBNET ...... 10 4.3 MODELING CLIENT-SIDE SUBNET ...... 12 5. Scenarios...... 13 5.1 VALIDATING 4k CLOUD-GAMING ...... 13 5.2 INTRODUCING BACKGROUND LOAD ...... 15 5.3 TESTING THE LIMITS OF CLOUD-GAMING ...... 16 5.4 CLOUD-GAMING ON A SHARED WI-FI WITH BACKGROUND LOAD ...... 18 5.5 CLOUD-GAMING WITH MOBILE USERS ...... 20 5.6 FINDING THE OPTIMAL SPEED FOR 4K-GAMING ...... 22 6. Conclusion & Discussion ...... 24 Reference ...... 25 Appendix ...... 26 SIMULATION ENVIRONMENT ...... 26
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List of Figures
Figure 1: Cloud -gaming in action ...... 7 Figure 2: Network requirements by cloud-gaming service...... 8 Figure 3: Flow-chart for cloud-gaming ...... 8 Figure 4: Maximum permissible interaction delay [2] ...... 9 Figure 5: Cloud gaming topology ...... 9 Figure 6: introducing propagation delay & packet discard ratio ...... 9 Figure 7: Sample video trace [11] ...... 10 Figure 8: using the sample video trace in Riverbed Modeler ...... 10 Figure 9: Modeling server-side subnet ...... 11 Figure 10: Cloud-overhead delay ...... 11 Figure 11: Wi-Fi configuration deployed ...... 12 Figure 12: Client-side subnet ...... 12 Figure 13: Setting up Application config. node ...... 12 Figure 14: Setting up Profile config. node ...... 13 Figure 15: Comparing ETE (4k & 1080p) ...... 14 Figure 16: Comparing Jitter (4k & 1080p) ...... 14 Figure 17: Setting up start time for individual profile used ...... 14 Figure 18: Introducing background load ...... 15 Figure 19: ETE delay comparison between 4k & 1080p ...... 15 Figure 20:Jitter comparison (4k &1080p) ...... 16 Figure 21: Client-side ...... 16 Figure 22: ETE delay comparison; 4 gamers vs 1 gamer ...... 17 Figure 23: Jitter comparison; 4k-gaming, 4users vs 1 user ...... 17 Figure 24: Client-side subnet ...... 18 Figure 25: ETE delay comparison (4k & 1080p) ...... 19 Figure 26: Jitter comparison (4k & 1080p) ...... 19 Figure 27: Client-side subnet for mobile gamer ...... 20 Figure 28: Trajectory for mobile-gamer ...... 20 Figure 29: ETE delay comparison; 4k-mobile, 4k & 1080p ...... 21 Figure 30: Jitter comparison; 4k-mobile, 4k & 1080p ...... 21 Figure 31: ISP speed by Speedscore [14] ...... 22 Figure 32: ETE delay comparison at different network link speeds ...... 23 Figure 33: Jitter comparison at different network link speeds ...... 23
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List of Abbreviations
DSL Digital Subscriber Line
ETE End to End
FPS First Person Shooter
HTTP Hypertext Transfer Protocol
IEEE Institute for Electrical & Electronics Engineers
IP Internet Protocol
LTE Long Term Evolution
MIMO Multiple Input & Multiple Output
MMO Massively Multiplayer Online
NASA National Aeronautics & Space Administration
PPP DS3 Point to Point Protocol Digital Signal 3
PS Play-Station
QoE Quality of Experience
QoS Quality of Service
RPG Role-Playing Game
RTS Real Time Strategy
WiMAX Wireless Interoperability for Microwave Access
WLAN Wireless Local Area Network
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List of Nodes
wlan_wkstn ip32_cloud
iPhone PPP_DS3 link
iPad Subnet
Android Smartphone CS_7507
Profile Config. Intel_D875PBZ
Application Config. Huawei_AR1220W
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Acknowledgement We’d like to thank our instructor Prof. Ljiljana Trajkovic for her invaluable advices, guidance & support throughout this project. We’d also like to thank our Teaching Assistant, Mr. Zhida Li for his recommendations on how to best use the Academic Edition of Riverbed Modeler 17.5.
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Abstract
Over the past decade, we’ve seen cloud gaming turn increasingly ubiquitous thanks to the proliferation of high-speed networks & cloud computing. Cloud now allows games to be played on multiple devices. It has brought a paradigm shift in the way games are distributed as digital copies. We can now rent games-on-demand without worrying about the memory usage in our systems. Sony’s PS Now & Nvidia’s GeForce Now already offer their subscribers a limited library of popular games at 1080p 60fps. Using Riverbed Modeler tools, we design a network configuration to study various scenarios to identify the feasibility of game streaming in 4K over Wi-Fi. Keywords: 4K, Cloud gaming, on-demand.
1. Introduction One of the biggest issues that have hamstrung the gaming industry is the necessity of regular hardware upgrade on the part of the end-user to get the best gaming experience. Each year, new iterations of popular titles turn more graphic intensive whereas a regular end-user rarely upgrades his hardware every year.
Cloud gaming opens a whole new world of possibilities wherein hardware upgrades are now obsolete as the game is rendered over a data-center & then, delivered to the end-user thus, requiring no computational processing on the part of the end-user.
The very seeds of cloud gaming were sown when Steve Colley in 1973 came up with the idea of Maze- War probably the world’s first Massively Multiplayer Online (MMO) game at NASA’s Ames Research Center. Over the years, incremental progress was made as online gaming began taking shape, CompuServe launched “Island of Kesmai” the first commercially available multiplayer online role- playing game.
In 2004, G-cluster deployed cloud gaming in Japan. Later, around 2010, OnLive & Gaikai launch fully functional online game-streaming service promising to potentially change the face of the gaming industry. Gaming giants like Sony & Nvidia with PS Now & GeForce respectively are already offering seamless cloud gaming at 1080p. [4]
Recently, Blade, a French start-up launched Shadow a virtual “PC streaming” service claiming to allow end-users to experience games at a whooping 4K resolution. [5]
Within this project, we plan to study the viability of Shadow’s 4K cloud-gaming in the light of acceptable latency without ruining end-user gaming experience.
This project is organized as follows. Section 2 gives a brief overview of the related literature. Section 3 gives a brief overview of related standards. In section 4 we elaborate how we model the 4k cloud gaming service. Section 5 deals with the scenarios that were used to study the cloud gaming service. Finally, we conclude in Section 6.
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2. Review of Literature All platforms including video streaming, gaming & file sharing have benefitted from utilizing cloud computation.
The premise of cloud gaming is straight forward instead of the traditional way where the system of the end-user does all the computation we have strategically placed data-centers that do the computation. The video (game) is simply streamed back to the player in a seamless fashion.
Figure 1: Cloud -gaming in action
For the gaming experience to be smooth & appear as if it were rendered locally, latency shouldn’t exceed beyond 500ms for third-person-perspective games to less than 100ms for time sensitive first-person-perspective games. [1]
Shea et al. [2] measured the performance of OnLive, a popular Cloud- gaming platform that was later acquired by Sony. They conducted an extensive study on latency & streaming quality under different game conditions to realize that decreasing the bandwidth worsens image quality. Further, OnLive could consistently show an interaction delay well below an acceptable 200ms under various test conditions.
Chan [3] tested the feasibility of Cloud-gaming over WLAN/ WIMAX underlining how these protocols impact the latency & scalability of a cloud-based gaming service. He notes that the gaming experience deteriorates with every additional user, background load, gamers joining the game at different times & moving users.
Cai et al. [6] present an ambitious cognitive Cloud-gaming service wherein the system learns about the users’ status & optimizes cloud, network & terminal resources to provide the best possible QoE.
Laxmi [8] modeled a Cloud-Gaming service like Gamefly using Riverbed Modeler 18.0.
Karl et al. [9] provide a vision on exploiting the technology of Cloud Gaming for improving the accessibility of Virtual Reality gaming.
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3. Wireless LAN 802.11n
IEEE 802.11n [7] often referred to as MIMO, “multiple input & multiple output” is a popular wireless networking standard. Since, it employs multiple antennas, therefore, can increase the data rate. It significantly increases network throughput when compared to its predecessors. It has an indoor range of 175 feet+ & a maximum net data rate between 54Mbps to 600Mbps. It can be used in both; 2.4GHz & 5 GHz frequency band.
Given its higher throughput & lesser congestion compared to 2.4GHz Wi-Fi; 5GHz Wi-Fi is often recommended by most of the cloud-gaming service providers.
Figure 2: Network requirements by cloud-gaming service
4. Modeling 4k cloud-gaming The following figure describes how cloud-gaming works. The user’s commands are sent to a dedicated data-center (server-side) wherein, they’re converted to in-game actions. The result is encoded & streamed back to the client side which is finally decoded & displayed on a TV/ workstation.
Figure 3: Flow-chart for cloud-gaming
All through the process, overall latency should be within the maximum prescribed range such as to ensure a seamless gaming experience on the part of the end-user.
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Figure 4: Maximum permissible interaction delay [2]
Our server- side subnet is in L.A. & the client-side subnet is situated in Vancouver; simulating Shadow’s 4k cloud-gaming service. They’re connected to the internet via a PPP_DS3 link with 45Mbps data rate.
Figure 5: Cloud gaming topology
Since the distance between the two subnets is 2,057.6 Kms; this introduces a propagation delay of 20.5ms (one way). Thus, to account for propagation delay we introduce a packet latency 0.041s in the IP cloud node. Further, to simulate real-life conditions we set the packet discard ratio to be 2.5% [10].
Figure 6: introducing propagation delay & packet discard ratio
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4.1 OBTAINING VIDEO TRACES
Arizona State University’s video trace library [11] gives us access to a plethora of relevant video traces. We chose one 4k (4096*1744) & one 1080p (1929*1080) trace for this project. The traces are of length 60mins each & a quantization of 35 (4k) & 40 (1080p) at 24fps & 30fps respectively [11].
Traces are imported to Riverbed Modeler using “video trace import” method as given by SFU’s Network System lab [12].
Figure 7: Sample video trace [11]
Figure 8: using the sample video trace in Riverbed Modeler
In the following section, we show how we modeled our base-scenario to validate 4k cloud- gaming.
4.2 MODELEING SERVER-SIDE SUBNET The very premise of cloud-gaming is to provide a seamless gaming experience to end-user without deploying his hardware for processing & rendering games. This requires highly sophisticated assembly of processors & GPUs which can translate user-commands to in-game actions & send them back to the end-user within the stipulated time such as to ensure a user- experience that closely mimics game being rendered locally.
To demonstrate the viability of 4k cloud gaming, we model the server side as follows:
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• A datacenter is used that processes & renders the game on part of the end-user. • A firewall is used to shield the datacenter from malicious attacks. • A router then connects the datacenter to the client side via the PPP_DS3 link. • Datacenter, firewall & the router are connected by a PPP_DS3 link with a data rate of 45Mbps.
Figure 9: Modeling server-side subnet
The datacenter is dedicated to supporting two services:
• It handles incoming user commands; simulated by light HTTP. • It sends back video frames to the client-side; simulated through a video- conferencing feature of the node Application Config.
To simulate real-life cloud-gaming service we incorporate a cloud-overhead delay [2] of 50ms to account for converting user-commands to in-game actions, rendering & encoding video frames.
In the following sections, different scenarios will have the same server-side as shown above however, the client-side may vary.
Figure 10: Cloud-overhead delay
As said earlier, we’re using wireless LAN 802.11n (5GHz). The WLAN parameters are configured as follows (meeting the requirements for cloud-gaming):
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Figure 11: Wi-Fi configuration deployed
4.3 MODELING CLIENT-SIDE SUBNET For our base scenario, we consider a single user who accesses the cloud-gaming service through a workstation; node Smart_TV. The user accesses the internet through the Wi-Fi router as shown.
Figure 12: Client-side subnet
The node Application Config. supports two applications:
• 4K_TRACE: Simulates 60mins long 4k traces. • GAME_CONTROLLER: Simulates user-commands through light HTTP.
Figure 13: Setting up Application config. node
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The node Profile config. supports two profiles:
• 4k: Containing the trace 4K_TRACE. • CONTROL: Supporting the application; GAME_CONTROLLER.
Figure 14: Setting up Profile config. node
The router is connected the server-side subnet through PPP_DS3 link via the IP cloud.
5. Scenarios
5.1 VALIDATING 4K CLOUD-GAMING In this scenario, we use the subnets as shown in 4.2 & 4.3. We’ve a single user who accesses the cloud-gaming service through his workstation. This scenario would act as base for comparison with others. All the parameters including WLAN, latency, packet-discard ratio & cloud overhead delay is set as discussed previously.
We run a 60mins game-session; same as the length of the traces used. As seen in fig. 15, the ETE delay for 4k-gaming is around, 80ms whereas for 1080p it’s around 70ms. As expected, jitter or packet delay variation is more in 4k than in 1080p. However, for both the cases, jitter is within the acceptable range for a seamless QoE. Referring to fig.4, all genres of games are supported by our base scenario.
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Figure 15: Comparing ETE (4k & 1080p)
Figure 16: Comparing Jitter (4k & 1080p)
It’s worth noticing that the simulation results always start around 40s that’s because the start-time [13]; the time at which the application generates traffic is set to be anything between 40-45s. Further, a start-time delay is always needed because the dynamic routing protocols(s) need some time to build the routing table.
Figure 17: Setting up start time for individual profile used
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5.2 INTRODUCING BACKGROUND LOAD It’s highly unlikely that a user will have a dedicated link running between the data-center & his home. Therefore, to simulate a more realistic case we incorporate a background load on the PPP_DS3 link used to connect the client-side subnet to the IP cloud & the server-side subnet. The background load is set-up as shown in figure 18. The background load steadily increases, from 6.67% to 86.67% of the PPP_DS3 link capacity.
Our aim is to see how QoS parameters are affected with increasing background load.
Figure 18: Introducing background load
As seen in fig. 19, ETE delay significantly increases for both 4k & 1080p gaming upon introduction of background load. We observe that ETE delay is around 180ms for 4k whereas it’s between 120ms to 155ms for a background load of 45.5% & 100% (unrealistic load) respectively. Therefore, we infer that FPS games are unplayable on 4k -streaming however, all the genres are still playable albeit with some deterioration in QoE for FPS games on 1080p- streaming.
Figure 19: ETE delay comparison between 4k & 1080p
It’s known that we need low packet delay variation or jitter as cloud-gaming is a time- sensitive application. As expected, graphic-intensive 4k traces show significant variation in jitter even without any background load. Upon introducing the background load, jitter
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increases manifolds, for instance at 30mins with a background load of 46% we find that jitter is twice more than in 1080p-streaming. Therefore, QoE suffers with 4k-streaming.
Figure 20:Jitter comparison (4k &1080p)
In the following section, we check the scalability of our 4k-cloud-gaming service.
5.3 TESTING THE LIMITS OF CLOUD-GAMING Within this scenario our aim is to check the scalability of a 4k cloud gaming service over a Wi-Fi network. We intend to see how Q0S parameters vary as the number of users would increase accessing a 4k-cloud gaming service over the same Wi-Fi network.
For this, scenario we employ the following client-side subnet:
Figure 21: Client-side
With reference to the following figure which shows ETE delay comparison, we observe that when the number of users grows to 4, 4k-cloud gaming shows significant delay of around 450ms. In comparison, with only single gamer, ETE delay of around 150ms was observed.
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Thus, we conclude, with 4-gamers FPS-games are rendered unplayable. Further, at this point even RPG-games would begin to incur lag.
Figure 22: ETE delay comparison; 4 gamers vs 1 gamer
Similarly, we observe a significant increase in Jitter when 4-gamers stream 4k-gaming content; there’s a 13x increase in Jitter compared to a single user streaming 4k-game. Therefore, we conclude that with 4 simultaneous users QoE for 4k-game streaming takes a significant hit.
Figure 23: Jitter comparison; 4k-gaming, 4users vs 1 user
In the following section we test, how 4k-game streaming is affected when we simulate a real- life home-based Wi-Fi network.
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5.4 CLOUD-GAMING ON A SHARED WI-FI WITH BACKGROUND LOAD In this scenario, our aim is to mimic a household Wi-Fi network with a single router. We intend to see how QoS parameters are affected when a gamer accesses 4k-cloud gaming service over a shared Wi-Fi.
For this we, use the following client-side subnet:
• iPhone: using the E-mail application. • iPad: involved in heavy internet browsing; simulated by heavy HTTP application. • Misc_User: Android smartphone user accessing medium load FTP application. • Smart_TV:4K-gamer; simulated by a scripted trace in video-conferencing application & light HTTP application to simulate gamer-commands.
It’s worth mentioning that we’re using a background load as shown in figure 18.
Figure 24: Client-side subnet
Further, there’re two PPP_DS3 links, one connecting the client-side subnet to a dedicated 4k-gaming data-center & the other connecting it to a “general server” that caters to nodes other than Smart_TV within the client-side subnet.
Looking at the ETE delay comparison in fig. 25 we observe that given the FPS games threshold latency (100ms), FPS games like the Call of Duty series, DOOM & BIOSHOCK are nearly unplayable over 4k as ETE delay averages around 200ms throughout the gaming session. However, 1080p game-streaming can consistently support all the genres of popular gaming, even with a background load as high as 46% of the link speed (at 30mins).
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Figure 25: ETE delay comparison (4k & 1080p)
Jitter comparison between the two cloud-gaming services (4k & 1080p) for the above stated scenario continues to follow previous trends. 4k-game streaming shows 266.67% more packet-delay variation than 1080p game-streaming leading to significant deterioration in gaming experience for the user. However, this packet-delay-variation is nearly 84% lower than the previous scenario where we intended to check the scalability of the cloud-gaming service.
Figure 26: Jitter comparison (4k & 1080p)
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5.5 CLOUD-GAMING WITH MOBILE USERS In this scenario we intended to test how mobility of the gamer impacts QoS parameters of our 4k-cloud gaming service. Most of the cloud-gaming services including Shadow advertise seamless 4k- gaming experience over smartphones & tablets. To test this claim we borrowed the client-side subnet from our previous scenario, 5.4 with a slight change. The static Smart_TV node was replaced by a new node; a workstation with a defined trajectory.
The in-scenario gamer moves across the range of a home-based Wi-Fi setup with a speed of 5km/h. Further, other nodes including iPhone, Heavy_User & Misc_User employ the same applications & profiles as explained in the previous scenario,5.4.
Figure 27: Client-side subnet for mobile gamer
Figure 28: Trajectory for mobile-gamer
Fig 29, gives us a comparison of ETE delay, with a moving user, delay increases slightly. However, as the gaming-session proceeds this slight difference vanishes & ETE delay with a moving-user turns out to be exactly same as seen in the previous scenario 5.4. Therefore, we infer, cloud-gaming experience on smartphones & tablets isn’t any different than when we have a static-gamer (using PC/ workstation).
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Figure 29: ETE delay comparison; 4k-mobile, 4k & 1080p
As the gamer moves, Jitter shows slight variation compared to the previous scenario, 5.4. Overtime, Jitter turns out to be identical to the previous scenario. Therefore, there’s little to none variation in QoE when a gamer accesses cloud-gaming service as he moves. This establishes the claim of popular cloud-gaming services that smartphones & tablets are viable alternative to PC(s)/ workstations for streaming games.
Figure 30: Jitter comparison; 4k-mobile, 4k & 1080p
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5.6 FINDING THE OPTIMAL SPEED FOR 4K-GAMING In this scenario we intend to find the optimal network link speed that would support all the three broad genres of popular-gaming; FPS, RPG & RTS. As seen in fig 31, Vancouver has an average download speed of around 65Mbps. Theoretically speaking, 4k-game streaming should be consistent & QoE seamless.
Borrowing the client subnet as seen in section 4.3 & as earlier mentioned, keeping the server- side subnet unchanged we test our 4k-cloud gaming service with a single user to find the ideal network link speed.
Figure 31: ISP speed by Speedscore [14]
We chose three different network-link speeds; 19.5Mbps, 39Mbps & 65Mbps each for the following reasons:
• 19.5Mbps: 50% higher network-link speed than the one we’ve used in all the previous scenarios. • 39Mbps: 200% increase in network-link speed & half-way from the peak as shown in fig.31. • 65Mbps: 400% increase in network-link speed to simulate the average download speed in Vancouver.
As seen in fig.32, ETE delay is around 52ms at 33% background-load & beyond when we deploy a link-speed of 19.5Mbps. The ETE delay further falls to well under 50ms as we go on to increase the link-speed, first to 39Mbps & then to its peak, 65Mbps.
With reference to fig.4, we infer that for the aforementioned link-speeds can support all of the genres of popular-gaming.
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Figure 32: ETE delay comparison at different network link speeds
Jitter or packet delay variation is shown in fig.33. Here, Jitter reduces by 100% when network-link speed is increased by 50% to 19.5Mbps. When we further increase the link- speed to a maximum of 65Mbps, Jitter reduces even more.
Figure 33: Jitter comparison at different network link speeds
Therefore, we infer that a link speed of 39Mbps can readily stream even the most graphic- intensive 4k-games provided that only a single user accesses 4k-cloud gaming service on a Wi-Fi setup.
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6. Conclusion & Discussion Within this project we simulated a 4k-cloud gaming service. Our aim was to ascertain the viability of 4k cloud-gaming service when compared to the widely available 1080p-cloud gaming services in the market.
Having simulated various scenarios we conclude that 4k-cloud gaming is indeed a viable alternative. However, given the fact that 4k-game streaming is graphic-intensive hence, a network speed of at least 19.5 Mbps is recommended (on 5GHz Wi-Fi). Further, to minimize Jitter & enhance QoE, we advise deploying a network-link speed of at least 39Mbps which runs contrary to what popular cloud-gaming service providers like Sony & Nvidia advertise.
We also found that under usual conditions, FPS games turn out to be unplayable when streamed in 4k. For the best experience, it’s recommended that fewer than 4 gamers be accessing 4k-cloud gaming service over a single Wi-Fi setup at the same time.
Future works may include, a comprehensive study on network latency to better understand the intricacies of cloud-gaming. Some cloud-gaming service providers particularly Shadow advertise seamless gaming experience on LTE meriting investigation. Since, cloud-gaming opens the frontier for cloud-computing solutions already available in its nascent stage forms a research area that needs even more attention. Also, game-encoding & rendering technologies are generally classified limiting our ability to accurately estimate critical parameters like cloud-overhead delay. We recommend further analysis of game-encoding technologies so that simulations may better reflect real-life conditions.
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Reference
• [1] CLAYPOOL, M. AND FINKEL, D. The Effects of Latency on Player Performance in Cloud-based Games In-text: [1] M. Claypool and D. Finkel, "The Effects of Latency on Player Performance in Cloud-based Games", IEEE, 2014.
• [2] SHEA, R., LIU, J., NGAI, E. C. AND CUI, Y. Cloud Gaming: Architecture & Performance In-text: [2] R. Shea, J. Liu, E. Ngai, and Y. Cui, "Cloud Gaming: Architecture & Performance", IEEE Network, pp. 16-21, 2013.
• [3] CHAN, D. On the feasibility of video gaming on demand in wireless LAN/WiMAX In-text: [3] D. Chan, "On the feasibility of video gaming on demand in wireless LAN/WiMAX".[Online].Available:http://www2.ensc.sfu.ca/~ljilja/ENSC895/Projects/chan/vgod_report.pdf. [Accessed: 10- Feb- 2018].
• [4] BigFishGames.[Online].Available:https://www.bigfishgames.com/daily/cloud-gaming/. [Accessed: 11- Mar- 2018].
• [5] [Online]. Available: https://shadow.tech/int/about-us. [Accessed: 11- Mar- 2018].
• [6] CAI, W., CHI, Y., ZHOU, C., ZHU, C. AND C.M. LEUNG, V. UBC Gaming: Ubiquitous Cloud Gaming System In-text: [6] W. Cai, Y. Chi, C. Zhou, C. Zhu and V. C.M. Leung, "UBC Gaming: Ubiquitous Cloud Gaming System", IEEE Systems Journal, 2018.
• [7] ANSI/IEEE 802.11N-2009 “ANSI/IEEE 802.11n-2009", IEEE Standards Association. [Online]. Available: http://ps://standards.ieee.org/findstds/standard/802.11n-2009.html. [Accessed: 16- Mar- 2018].
• [8] L. Royyala, "Cloud Gaming Simulation". [Online]. Available: https://sites.google.com/view/cloudgamingproject. [Accessed: 10- Feb- 2018].
• [9] CHAN, K. L., ICHIKAWA, K., WATASHIBA, Y. AND IIDA, H. Cloud-Based VR Gaming: Our Vision on Improving the Accessibility of VR Gaming. K. Chan, K. Ichikawa, Y. Watashiba and H. Iida, "Cloud-Based VR Gaming: Our Vision on Improving the Accessibility of VR Gaming", 2017 International Symposium on Ubiquitous Virtual Reality, 2017.
• [10] [Online]. Available: http://www.slac.stanford.edu/comp/net/wan-mon/tutorial.html#loss. [Accessed: 15-Feb- 2018]
• [11] [Online]. Available: http://trace.eas.asu.edu/. [Accessed: 15- Feb- 2018].
• [12] [Online]. SFU Network Systems Lab. Available: https://nsl.cs.sfu.ca/wiki/Importing_video_traffic_into_OPNET. [Accessed: 13- Mar- 2018].
• [13] [Online]. Available: https://sandilands.info/sgordon/teaching/resources/itguru.html. [Accessed: 04-Mar-2018]
• [14] [Online]. Available: http://www.speedtest.net/reports/canada/#fixed. [Accessed: 12-Feb-2018]
• [15]. S. Abdulazeez, A. Rhalibi and D. Jumeily, "Simulation of Massively Online Games Communication Using OPNET Custom Application", ISCC, 2016
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Appendix
SIMULATION ENVIRONMENT The following environment was used during the development of this project:
HP Spectre x360
• Intel ® Core ™ i7-8550U CPU @1.80GHz, 1.99GHz • 8 GB LPDDR3 SDRAM • 256 GB PCIe® NVMe™ M.2 Solid State Drive(15) • Microsoft Windows 10 Home • Riverbed Modeler Academic Edition 17.5.A PL7 (32-bit)
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