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The Seminal Role of Edge-Native Applications 2019 IEEE International Conference on Edge Computing (EDGE) The Seminal Role of Edge-Native Applications Mahadev Satyanarayanan Guenter Klas, Marco Silva, Simone Mangiante Carnegie Mellon University Vodafone Group Pittsburgh, PA, USA Newbury, UK [email protected] {Guenter.Klas, marco.silva1, simone.mangiante}@vodafone.com Abstract—We introduce the concept of edge-native applications This paper focuses on the transformative aspects of edge that fully exploit the potential of edge computing and have a deeply computing. We begin in Section II by positioning edge symbiotic relationship with it. Such an application is custom- computing relative to other elements of today’s computing designed to take advantage of one or more of the unique attributes of edge computing such as (a) bandwidth scalability, (b) low- landscape. Then, in Section III, we identify a broad class of latency offload, (c) privacy-preserving denaturing, and (d) WAN- new applications called edge-native applications that fully failure resiliency. The application may also contribute to scalability exploit the potential of edge computing and have a deeply through adaptation to reduce offered load. We contrast edge- symbiotic relationship with it. In Section IV, we develop a native applications with shallower uses of edge computing in a taxonomy that contrasts edge-native applications with other taxonomy that spans edge-enhanced, device-native applications, edge-accelerated, cloud-native applications, and device-only appli- applications that make shallower use of edge computing. To cations. We close with a case study that illustrates these concepts illustrate the abstract concepts in our discussion, Section V in the context of cognitive assistance for automotive safety. describes a specific use case and discusses how alternative implementations map to our taxonomy. We close in Sec- I. INTRODUCTION tion VI by reiterating the central message of this paper: it The roots of Edge Computing reach back over a decade. is edge-native applications that will deliver transformative This new tier of computing has arisen from the observation value to society, and investing in them is critical to business that consolidation, which is the central premise of cloud success in edge computing. computing, has negative consequences. It tends to lengthen network round-trip times (RTT) from mobile users, and to II. A THREE-TIER MODEL OF COMPUTING increase cumulative ingress bandwidth demand from Inter- The cumulative body of evidence cited in Section I on net of Things (IoT) devices. These negative consequences the merits of edge computing has led to the three-tier model stifle the emergence of new classes of real-time, sensor-rich shown in Figure 1. Each tier represents a distinct and stable applications such as assistive augmented reality (AR) and set of design constraints that dominate attention at that streaming IoT video analytics. tier [13]. There are typically many alternative implemen- Since we first articulated this insight in 2009 [1], ex- tations of hardware and software at each tier, but all of perimental studies by us and others have validated and them are subject to the same set of design constraints. There quantified the many benefits of edge computing. First, we is no expectation of full interoperability across tiers — have shown [2], [3], [4], [5] that edge computing can help randomly choosing one component from each tier is unlikely to achieve considerable improvement in response times and to result in a functional system. Rather, there are many sets battery life by offloading computation from a mobile device of compatible choices across tiers. For example, a single to a nearby cloudlet rather than to the distant cloud. Second, company will ensure that its products at each tier work well we have demonstrated [6], [7], [8], [9], [10] that enormous with its own products in other tiers, but not necessarily with reduction in ingress bandwidth demand is achievable by products of other companies. The tiered model of Figure 1 processing high data rate sensor streams (such as video is thus quite different from the well-known “hourglass” streams) on cloudlets rather than the cloud. Third, we have model of interoperability. Rather than defining functional shown [10], [11] that cloudlets can serve as privacy firewalls boundaries or APIs, this model segments the end-to-end that enable users to dynamically and selectively control the computing path and highlights design commonalities. release of sensitive information from sensors to the cloud. Tier-1 represents “the cloud” in today’s parlance. Two Fourth, we have shown [12] that cloudlets can offer fallback dominant themes of Tier-1 are compute elasticity and storage services that mask the unavailability of cloud services due to permanence. Cloud computing has almost unlimited elastic- network or server failures, or cyber attacks. A decade after ity, as a Tier-1 data center can easily spin up servers to its original conception, the importance of edge computing is rapidly meet peak demand. Relative to Tier-1, all other tiers no longer in doubt. The focus of effort is now on accelerating have very limited elasticity. In terms of archival preservation, the adoption of edge computing. the cloud is the safest place to store data with confidence 978-1-7281-2708-8/19/$31.00 ©2019 IEEE 33 DOI 10.1109/EDGE.2019.00022 0RELOH &ORXGOHWV &ORXG ,R7'HYLFHV ORZ ODWHQF\ 'URQHV /XJJDEOH KLJK EDQGZLGWK 6WDWLF 9HKLFXODU 6HQVRU$UUD\V Wide-Area Network 9HKLFXODU 0LFURVRIW +ROROHQV 0DJLF/HDS ZLUHOHVV ,QWHO QHWZRUN 9DXQW &RIIHH6KRS 2FXOXV*R 2'* 0LQLGDWDFHQWHU $5958VHUV 7LHU 7LHU 7LHU Figure 1. Three-tier Model of Computing that it can be retrieved far into the future. A combination of storage redundancy (e.g., RAID), infrastructure stability (i.e., Typical Tier-1 Server Typical Tier-3 Device data center engineering), and management practices (e.g., Year Processor Speed Device Speed data backup and disaster recovery) together ensure the long- 1997 Pentium II 266 MHz Palm Pilot 16 MHz term integrity and accessibility of data entrusted to the cloud. 2002 Itanium 1 GHz Blackberry 5810 133 MHz Relative to the data permanence of Tier-1, all other tiers 2007 Intel Core 2 9.6 GHz Apple iPhone 412 MHz (4 cores) offer more tenuous safety. Getting important data captured 2011 Intel Xeon 32 GHz Samsung Galaxy S2 2.4 GHz at those tiers to the cloud is often an imperative. Tier-1 X5 (2x6 cores) (2 cores) exploits economies of scale to offer very low total costs of 2013 Intel Xeon 64 GHz Samsung Galaxy S4 6.4 GHz E5-2697v2 (2x12 cores) (4 cores) computing. As hardware costs shrink relative to personnel Google Glass 2.4 GHz costs, it becomes valuable to amortize IT personnel costs (2 cores) over many machines in a large data center. Consolidation is 2016 Intel Xeon 88.0 GHz Samsung Galaxy S7 7.5 GHz E5-2698v4 (2x20 cores) (4 cores) thus a third dominant theme of Tier-1. For many large tasks, HoloLens 4.16 GHz Tier-1 is typically the optimal place to perform them. This (4 cores) remains true even after the emergence of edge computing. 2017 Intel Xeon 96.0 GHz Pixel 2 9.4 GHz Gold 6148 (2x20 cores) (4 cores) Mobility and sensing are the defining attributes of Tier- 3. Mobility places stringent constraints on weight, size, and Source: Adapted from Chen [15] and Flinn [16] “Speed” metric = number of cores times per-core clock speed. heat dissipation of devices that a user carries or wears [14]. Such a device cannot be too large, too heavy or run Figure 2. The Mobility Penalty: Impact of Tier-3 Constraints too hot. Battery life is another crucial design constraint. Together, these constraints severely limit designs. Techno- computation over a wireless network to Tier-1. This was first logical breakthroughs (e.g., a new battery technology) may described by Noble et al [17] in 1997, and has since been expand the envelope of feasible designs, but the underlying extensively explored by many others [16], [18]. For example, constraints always remain. speech recognition and natural language processing in iOS Today’s mobile devices are rich in sensors such as GPS, and Android nowadays work by offloading their compute- microphones, accelerometers, gyroscopes, and video cam- intensive aspects to the cloud. eras. Unfortunately, a mobile device may not be powerful IoT devices can be viewed as Tier-3 devices. Although enough to perform real-time analysis of data captured by they may not be mobile, there is a strong incentive for its on-board sensors (e.g., video analytics). While mobile them to be inexpensive. Since this typically implies meager hardware continues to improve, there is always a large gap processing capability, offloading computation to Tier-1 is between what is feasible on a mobile device and what is again attractive. feasible on a server of the same technological era. Figure 2 shows this large performance gap persisting over a 20-year The introduction of Tier-2 is the essence of edge comput- period from 1997 to 2017. One can view this stubborn ing [19], and creates the illusion of bringing Tier 1 “closer.” gap as a “mobility penalty” — i.e., the price one pays in This achieves two things. First, it enables Tier 3 devices performance foregone due to mobility constraints. to offload compute-intensive operations at very low latency. To overcome this penalty, a mobile device can offload This helps to overcome stringent Tier 3 design constraints (e.g., weight, size, battery life, heat dissipation) without 34 compromising the tight response time bounds needed for computing, the computing resources that can be brought to immersive user experience and by cyber-physical systems. bear in this task can be far larger, heavier, more energy- Proximity also results in a much smaller fan-in between hungry and more heat-dissipative than could ever be carried Tiers 3 and 2 than was the case when Tier 3 devices or worn by a human user.
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