The Seminal Role of Edge-Native Applications

2019 IEEE International Conference on Edge Computing (EDGE)

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

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&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. Distributed sensing can also connected directly to Tier 1. Consequently, Tier 2 processing offer real-time sensory inputs (e.g., from video cameras) of large volumes of live data captured at Tier 3 avoids obtained from vantage points other than the first-person excessive bandwidth demand anywhere in the system. viewpoint of a human. By seamlessly integrating these Note that “proximity” here refers to network proximity resources with human perception and cognition, such an rather than physical proximity. It is crucial that RTT be low assistive application could achieve a whole that is much and end-to-end bandwidth be high. This is achievable by greater than the sum of parts. using a fiber link between a wireless access point and a The potential business value of cognitive augmentation cloudlet that is many tens or even hundreds of kilometers is huge. Consider, for example, just one small segment of away. Conversely, physical proximity does not guarantee this market: cognitive assistance for the elderly in the US. network proximity. A highly congested WiFi network may Today, over 20 million Americans are affected by some have poor RTT, even if Tier-2 is physically near Tier-3. form of cognitive decline that significantly affects their ability to function as independent members of society. This III. EDGE-NATIVE APPLICATIONS includes people with neurodegenerative conditions such as Edge computing offers at least four valuable attributes, as Alzheimer’s disease (˜4.5M) and mild cognitive impairment mentioned in Section I: (a) bandwidth scalability, (b) low- (>6M), survivors of stroke (˜2.5M) and people with trau- latency offload, (c) privacy-preserving denaturing, and (d) matic brain injury (˜5.3M). These numbers are expected WAN-failure resiliency. An edge-native application is one to grow significantly due to an aging population and an that is custom-designed to take advantage of one or more increase in long-term post-traumatic stress disorders arising of these attributes. Such an application does not function from occupational and social causes. Cognitive impairment satisfactorily without a cloudlet. It is from this class of can manifest itself in many ways, including the inability applications, uniquely enabled by the attributes above, that to recognize people, locations and objects, loss of short- the “killer apps” of edge computing are going to emerge. and long-term memory, and changes in behavior and ap- Even an imperfect initial implementation of a future pearance such as decreased attention to personal hygene. killer app can provide such high value to end users that Among the many challenges faced by older Americans, it creates new demand for edge computing. In the absence cognitive decline often has the largest negative impact on of viable competing alternatives, such an application can them and their family members. The potential cost savings coevolve with supporting infrastructure over an extended from even modest steps towards addressing this challenge period of time. For example, there is strong evidence that the are enormous. In the US alone, it is estimated that just a development of the spreadsheet circa 1982-1983 (VisiCalc, one-month delay in nursing home admissions nationwide Lotus-123 and, eventually, Microsoft Excel) was a major could save over $1 billion annually [22]! At global scale, catalyst in the adoption of personal computers (PCs) by with rapidly aging populations in the richest countries of small businesses. It was the low and stable latency of the world, the potential business value is many times larger. human interaction (relative to timesharing) that made PCs Of course, cognitive augmentation is not the only source indispensable infrastructure for spreadsheets. of edge-native applications. Real-time video analytics from a The four attributes listed above have the potential to play large array of cameras is another example, with many uses in an analogous role for edge computing. The creation of new domains such as law enforcement, surveillance, and military edge-native applications that leverage one or more of these intelligence. Combining live video analytics with real-time attributes will be the true drivers of edge computing. The denaturing for privacy is an even more demanding source of history of science and technology is full of examples of edge-native applications [10]. 360-degree video/VR distribu- rudimentary implementations of “killer apps” (e.g., auto- tion, as reported by Mangiante et al [23] is another example mobiles, aircraft, television, the microprocessor, the World of an edge-native application. Over time, we are confident Wide Web) driving the evolution of the surrounding ecosys- that the many valuable attributes of edge computing will tem. This rapidly establishes a virtuous cycle that leads to lead to many different types of edge-native applications. We continuous long-term improvements and business value in emphasize cognitive augmentation in this paper because it both the core technology and the sustaining ecosystem. has been a major source of first-hand experience for us, and Edge-native applications that augment human cogni- because we believe that such applications will ultimately tion [2], [3], [20], [21] are potential killer apps for edge have the potential for major societal benefit. computing. These applications improve some aspect of hu- Our characterization of edge-native applications until this man cognition (e.g., task performance, long-term memory, point has focused solely on their deep dependence on edge- face recognition, etc.) in real time. By leveraging edge specific attributes that cannot be offered by cloud computing.

35 However, a complete definition of edge-native applications (May 2019) and the path forward is shrouded in uncertainty. would also have to recognize the fact that they need to The source of this uncertainty is a “chicken or egg” problem be written to function effectively in spite of limitations of involving customer demand for edge computing. Before the edge. In particular, scalability is an area where edge making large investments, companies expect to see clear computing suffers relative to cloud computing. As its name evidence of customer demand for edge computing. This implies, a cloudlet is designed for much smaller physical demand will only arise when there are a large number of space and electrical power than a cloud data center. Hence, edge-native applications that deliver high value to end users. the sudden arrival of an unexpected flash crowd of users Before investing resources to create such applications, their can overwhelm a cloudlet and its wireless network. There authors need confidence that the critical resource (i.e., edge are only two solutions to this problem. The first is to computing) is widely deployed. This is the deadlock we face. relocate one or more application back-ends to a less-loaded There are two paths to breaking this deadlock, and we cloudlet using a mechanism such as VM Handoff [24]. assert that both paths are important. First, the industry Unfortunately, this is likely to be suboptimal if the original segments that stand to benefit from edge computing over cloudlet was optimally chosen. The other solution is for the long term should play an active role in nurturing the applications associated with that cloudlet to lower their creation of edge-native applications. This could be through resource demands. In other words, scalability requires the support of non-commercial open source efforts, as well as average burden placed by each user on the cloudlet and the direct investments in startup companies. In making these wireless channel to fall as these resources saturate. investments, the potential beneficiaries of a vibrant edge These considerations suggest that adaptive application computing ecosystem should recognize that “return on in- behavior based on guidance received from the cloudlet, vestment (ROI)” should be interpreted more broadly than the network, or inferred by the user’s mobile device needs classic investment metrics would suggest. They should take to be an integral part of what it means to be an edge- the long view, and recognize that ROI includes their own native application. This important area of future research in long-term survival. This requires an active role in application edge computing can build upon earlier work. Specifically, development, and lies well outside the traditional comfort the approach to trading off quality of user experience or zone of many companies. Especially for telecommunications battery life for reduced resource demand that was introduced companies, edge computing represents an opportunity to by Odyssey [17], [25], and the concept of multi-fidelity create long term business value for their unique assets. applications [26], are both valuable foundations to build The second path is to recognize that edge-native applica- upon. Scalability at the edge is thus only achievable for tions are not the only class of applications to benefit from applications that have been designed with this goal in mind. edge computing. There are applications that can leverage This is another important aspect of edge-native applications. edge computing when available, but deliver acceptable user experience when run in the cloud. We refer to this class of IV. A TAXONOMY OF EDGE COMPUTING APPLICATIONS applications as edge-accelerated, cloud-native applications. There is intense industry interest in edge computing, and For brevity, we just use the qualifier “edge-accelerated” to it is believed that we are on the cusp of major industry in- refer to this class. The 20-year history of content distribu- vestments [27]. Coming at the same time as the rollout of 5G tion networks (CDNs) for web access is a good example wireless systems, the total demand for capital investments of edge acceleration. Today, venture capital is focused is substantial even for large telecommunications companies. on identifying new edge-accelerated use cases rather than A question that is frequently asked is whether 5G should edge-native use cases, because they involve less investment be a precursor to edge computing. For a number of use risk. Edge-accelerated use cases involve much less soft- cases, deploying 5G without also deploying edge computing ware development, and their markets are much larger since is unlikely to be satisfactory. In such a deployment, 5G only they can function acceptably even in the absence of edge improves last-mile connectivity. The rest of the path to the computing. To the extent that they stimulate investment, distant cloud remains what it is today. Average RTTs on the these applications can offer modest help in catalyzing the order of a hundred milliseconds, with a tail of one second rollout of edge infrastructure. However, unlike edge-native or more, can be expected [28]. This is a lower bound on the applications, they will not deliver transformative value and achievable end-to-end application response time. In contrast, will therefore generate only modest premium for the edge. deploying edge computing in today’s 4G LTE environment Between edge-native and edge-accelerated applications can yield end-to-end application response times (both mean are a third class of applications that leverage edge comput- and tail) of just a few tens of milliseconds [4], [5]. Edge ing. These typically run on-device, rather than in the cloud. computing cuts RTT for both 4G LTE and 5G, and makes When edge computing is available, they are able to make innovative applications already viable on 4G networks today. optional use of it to improve functionality or performance. In spite of enormous industry interest in edge computing, For example, an application may have certain features that actual deployments of edge computing are minimal today only work when the device is associated with a cloudlet.

36 V. C ASE STUDY 7LHU 7LHU 7LHU A. Concept 'HHSHU8VHRI(GJH 'HYLFHRQO\'HYLFHRQO\ 3 To illustrate the concepts discussed in Sections III and IV, we present a real-world use case. This is in the domain (GJHDFFHOHUDWHG 2 3 of automotive safety. Specifically, it is a cognitive assistance FORXGQDWLYH application for drivers (and future autonomous vehicles) that (GJHHQKDQFHG 3 2 alerts them to a pedestrian who unexpectedly appears in front GHYLFHQDWLYH of a moving vehicle. In theory, a driver should always be on the alert for such a possibility. In practice, many factors (GJHQDWLYH 3 3 such as fatigue, poor lighting, and distracted driving (e.g., talking or texting on a cell phone) together conspire to make 3 3ULPDU\H[HFXWLRQVLWH drivers less than perfect in their ability to avoid accidents 2 2SWLRQDOQRQFULWLFDOXVH in this setting. The goal is to create a cognitive assistant This table only refers to the main code paths. Not shown are incidental that uses computer vision and edge computing to detect the uses of Tier-1 for purposes such as authentication, error reports, and pedestrian and immediately alert the driver. The input for software upgrades. sensing comes from a video camera mounted on the vehicle. Figure 3. Taxonomy of Application Types Such a system would, in effect, augment the driver’s vision system with an extra pair of eyes that never gets fatigued or Otherwise, only a subset of the full functionality may be distracted. Figure 4 illustrates this concept. available on the device. Another approach is to reduce the Our use of edge computing has at least two benefits. First, fidelity of the underlying algorithms when edge computing is it avoids the need for expensive in-vehicle compute infras- not available. This approach is consistent with the approach tructure to process frames from the video camera in real of multi-fidelity computation, introduced nearly 20 years time. For a low-end vehicle costing $20,000, even a $1,000 ago [26]. In a computer vision application, for example, computer is a significant price increase. The drawback in the algorithm used for object detection may be much more having no in-vehicle cloudlet is the significant continuous accurate on the cloudlet. The version of the application on use of wireless bandwidth to stream video to an off-vehicle the mobile device may use a simpler algorithm that has cloudlet. In recent work [29], we have shown how prelimi- smaller memory footprint and lower processing demand, but nary on-board processing of the video by a relatively small comes at the cost of lower accuracy. We refer to this class and cheap cloudlet can suppress transmission of many video of applications as edge-enhanced, device-native applications. frames without loss of accuracy in detection. Such a hybrid For brevity, we just use the qualifier “edge-enhanced” to approach may strike the right cost balance. refer to this class. Second, looking to the future, it may be possible to Finally, there are many applications that run entirely on combine video and GPS data from a vehicle with additional a Tier-3 device, without any use of Tier-2. We refer to video streams from static cameras nearby to improve the these as device-only applications. The opportunity here is predictive ability of the system. For example, a camera to evangelize edge computing, and to help developers to pointed at the sidewalk may detect a soccer ball kicked refine and extend these applications into edge-native or edge- towards the road. AI software can reasonably infer that the enhanced, device-native applications that offer improved child who kicked the ball may run onto the street to retrieve functionality, user-experience or both. it. It can therefore proactively raise an alert before the child Figure 3 visually illustrates these four classes of appli- actually runs onto to the road. The window of advance notice cations. Edge computing is more deeply used as we go may only be a few hundred milliseconds, but that may save from top to bottom. At the very bottom (edge-native), one the child’s life. A cloudlet that is processing all these video or more attributes of edge computing is so deeply woven streams views a vehicle’s video stream as just one more input into the fabric of the application that it cannot function in its sensor fusion. It will be difficult in a purely vehicle- effectively without edge computing. As we move up through centric system to leverage these multiple sensor viewpoints the edge-enhanced and edge-accelerated levels of this table, in the surrounding infrastructure. the dependence on edge computing decreases. It is deep Others have also recognized the value of edge computing dependence on edge computing that will ultimately drive its for real-time video analytics in traffic safety. For example, widespread deployment and success. Relative to scalability, Ananthanarayanan et al [30] have described a video analytics only edge-native applications contribute to reducing offered software stack for edge computing that has been deployed in load through adaptation. They can thus be viewed as the the city of Bellevue, WA and aims to provide high accuracy true first-class citizens of the edge, both contributing to and while minimizing the cost of execution. In our taxonomy, benefiting from its unique attributes. their system would be viewed as an edge-native application.

37 Figure 4. Predictive Automotive Safety through AI at the Edge

computing”) software platform, Software Defined Network- ing Control, virtualised IPSec gateways, virtualized active measurement software and probes, and a Vodafone-specific cloudlet environment supporting cloud native development and hardware acceleration through GPUs. A series of drive tests were conducted to analyse robustness of the application in the face of varying radio conditions, radio cell traffic load and vehicle speeds. These tests confirmed the viability of this system with 4G LTE. Upgrading to 5G will boost available bandwidth per vehicle, while also reducing radio latency. C. Mapping to Taxonomy As implemented in February 2019, this system is very much dependent on the cloudlet — only a little pre- Figure 5. Aldenhoven Testing Center, Germany processing is done in the vehicle. The low end-to-end latency offered by edge computing is crucial in this application. At typical vehicular speeds, even a few hundred milliseconds can mean the difference between an accident and a near miss: for example, at 30 mph, a vehicle covers 4.4 feet in 100 milliseconds. Edge computing is also crucial for bandwidth scalability in this implementation. Hulu estimates that its video streams require 13 Mbps for 4K resolution and 6 Mbps for HD resolution using highly optimized offline encoding [31]. Live Figure 6. One of the Cloudlets Used in the Prototype System streaming is less bandwidth-efficient: 10 Mbps for HD video B. Proof of Concept Implementation at 25 FPS is reported by Wang et al [32]. Without edge computing, the total bandwidth demand to the cloud from In partnership, Vodafone (a leading telecommunications 10,000 vehicles in a city would exceed 100 Gbps. This company) and Continental (a leading technology company) would place severe stress on its metropolitan area network. have created a proof of concept implementation of this sys- In the taxonomy of Section IV, this version of the tem. The implementation is located at the outdoor automo- application qualifies as an edge-native application since it tive test track of the Aldenhoven Testing Center in Germany is critically dependent on one or more attributes of edge (Figure 5), the home of Vodafone’s 5G Mobility lab. Conti- computing. However, it lacks any support for scalability at nental developed the application software; Vodafone and its the edge. If a large number of proximate vehicles overwhelm vendors created the edge computing infrastructure (Figure 5, the processing capacity of the single cloudlet that they 6). This infrastructure features a MEC (“multi-access edge are associated with, there is currently no mechanism to

38 reduce offered load. This would require one of three possible network failures. The main weakness of edge computing solutions to be added. First, some vehicles could be denied is the limited elasticity of even large cloudlets relative cognitive assistance (load shedding). Second, the prediction to hyperscale cloud data centers. A secondary weakness algorithm used in the cloudlet could be temporarily switched is the increased cost of managing dispersed rather than to a less accurate (and presumably less compute intensive) centralized infrastructure. This secondary weakness is not algorithm. Third, an in-vehicle cloudlet could process many directly visible to applications, but manifests itself in the of the frames with some loss of accuracy and only transmit high marginal cost of edge computing relative to cloud a much lower frame rate to the cloudlet. The first and third computing. This leads to the need for an “edge premium.” approaches could also be used to reduce wireless bandwidth The central message of this paper is that edge-native demand, if that proves to be the bottleneck. applications and edge computing need each other. Deploying How would alternative implementations of this application edge computing without a substantial body of edge-native map to the taxonomy of Figure 3? Based on the latency and applications is unlikely to result in a sustainable business. bandwidth scalability reasoning above, it is clear that this The capital cost of deploying edge infrastructure, and the application simply cannot be run in the cloud. There is there- higher marginal operating cost of that infrastructure rela- fore no flavor of implementation that could be considered tive to a cloud data center, can only be recouped with a as an edge-accelerated, cloud-native implementation. substantial premium for edge computing. Such a premium A device-only implementation is conceivable. A large in- is unlikely to be sustainable unless end-users receive new vehicle cloudlet with a high-end GPU could provide the value that delights them. Only edge-native applications can computing cycles necessary to perform video processing at provide that value. The taxonomy that we have introduced full frame rate. This would offer the lowest possible latency, includes other types of applications that can benefit from the since no wireless communication to the cloudlet would be edge, without being deeply dependent upon it. While those required. Such a design would also be highly bandwidth may be useful fellow travellers in the journey to deployment scalable. The biggest drawback of a device-only implemen- of edge computing everywhere, we posit that only edge- tation is the significant cost of an in-vehicle cloudlet that native applications can help us complete that journey. is powerful enough for continuous video analytics at high frame rate. A secondary drawback is the inability to leverage ACKNOWLEDGMENTS video streams from static cameras in the surroundings, and Satyanarayanan was supported by the Defense Advanced Research Projects to thus benefit from sensor fusion. Agency (DARPA) under Contract No. HR001117C0051 and by the National A slight modification of the device-only implementation, Science Foundation (NSF) under grant number CNS-1518865. He received to allow use of a cloudlet when available, could eliminate additional support from Intel, Vodafone, Deutsche Telekom, Verizon, Crown Castle, NTT, Seagate, Siemens, and the Conklin Kistler family fund. this drawback. Such an implementation would rely solely Any opinions, findings, conclusions or recommendations expressed in this on the in-vehicle camera and cloudlet in the worst case, but material are those of the authors and do not necessarily reflect the view(s) leverage cloudlet-based sensor fusion when possible. This of their employers or the above-mentioned funding sources. would be viewed as an edge-enhanced implementation in our taxonomy. However, if this implementation were to also REFERENCES be adaptive and to reduce offered load when the wireless network is congested or the cloudlet is heavily loaded, it [1] M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, “The would be more appropriate to view it as a full-fledged edge- Case for VM-Based Cloudlets in Mobile Computing,” IEEE Pervasive Computing, vol. 8, no. 4, 2009. native application. [2] Z. Chen, W. Hu, J. Wang, S. Zhao, B. Amos, G. Wu, VI. CONCLUSION K. Ha, K. Elgazzar, P. Pillai, R. Klatzky, D. 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