Edge computing in the ePC

Hadzic, Ilija; Abe, Yoshihisa; Woithe, Hans Christian

doi:10.1145/3132211.3134449

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…Recent investigations report performance of preliminary deployment of 5G in the realworld to be sub-optimal [49,71]. Likewise, Hadzic et al [31] and Cartas et al [12] find that latency gains for accessing edge server colocated with an LTE basestation is minimal compared to accessing a datacenter located ≈ 1000 km away. While the "true" gains of 5G are yet to be seen, considering supporting strict MTP thresholds, even with edge servers located at basestations, seems uncertain.…”

Section: Discussionmentioning

confidence: 99%

“…Second, modern smartphones come equipped with considerable processing power, including specialized chipsets, enabling them to process complex tasks, such as AR. On the other hand, last-mile access being the latency bottleneck over (cellular) wireless remains true [12,31]. While new technologies, such as 5G, show promise, initial tests report their performance to be deficient in practice [49,71].…”

Section: Introductionmentioning

confidence: 99%

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Pruning Edge Research with Latency Shears

Mohan

Corneo

Zavodovski

et al. 2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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Edge computing has gained attention from both academia and industry by pursuing two significant challenges: 1) moving latency critical services closer to the users, 2) saving network bandwidth by aggregating large flows before sending them to the cloud. While the rationale appeared sound at its inception almost a decade ago, several current trends are impacting it. Clouds have spread geographically reducing end-user latency, mobile phones' computing capabilities are improving, and network bandwidth at the core keeps increasing. In this paper, we scrutinize edge computing, examining its outlook and future in the context of these trends. We perform extensive client-to-cloud measurements using RIPE Atlas, and show that latency reduction as motivation for edge is not as persuasive as once believed; for most applications the cloud is already "close enough" for majority of the world's population. This implies that edge computing may only be applicable for certain application niches, as opposed to a general-purpose solution.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pruning Edge Research with Latency Shears

Mohan

Corneo

Zavodovski

et al. 2020

Proceedings of the 19th ACM Workshop on Hot Topics in Networks

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“…Virtualized cellular networks without OTA testing have been studied widely [7], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25]. OTA testbeds without MEC have been installed on LTE [26], [27], [28], [29] and, to avoid regulated spectrum, on WiFi [30], [31]. Proprietary OTA testbeds with MEC exist [32], [33], [34], [35], but without consideration of DevOps practices.…”

Section: Related Workmentioning

confidence: 99%

Open-source RANs in Practice: an Over-The-Air Deployment for 5G MEC

Haavisto

Arif

Lovén

et al. 2019

2019 European Conference on Networks and Communications (EuCNC)

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Edge computing that leverages cloud resources to the proximity of user devices is seen as the future infrastructure for distributed applications. However, developing and deploying edge applications, that rely on cellular networks, is burdensome. Such network infrastructures are often based on proprietary components, each with unique programming abstractions and interfaces. To facilitate straightforward deployment of edge applications, we introduce open-source software (OSS) based radio access network (RAN) on over-the-air (OTA) commercial spectrum with Development Operations (DevOps) capabilities. OSS allows software modifications and integrations of the system components, e.g., Evolved Packet Core (EPC) and edge hosts running applications, required for new data pipelines and optimizations not addressed in standardization. Such an OSS infrastructure enables further research and prototyping of novel end-user applications in an environment familiar to software engineers without telecommunications background. We evaluated the presented infrastructure with end-to-end (E2E) OTA testing, resulting in 7.5MB/s throughput and latency of 21ms, which shows that the presented infrastructure provides low latency for edge applications.

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“…They classify edge networks into roughly four categories and consider the issues of computation offloading and caching in each categiry. The four categories they consider are mobile cloud computing [15], mobile edge computing [5], [8], [12], fog computing [6], [10], and cloudlet [21]).…”

Section: A Related Workmentioning

confidence: 99%

MECANO: Integrated Measurement of Compute and Network Operations

Albalawi

Westphal

Chakraborti

et al. 2019

2019 IEEE International Symposium on Measurements &Amp; Networking (M&N)

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In-network computing is a growing area of research where computations are more tightly integrated with the networking substrate and placed dynamically within the network at appropriate locations. However, this yield new issues in the domain of measurements and networking: it adds a dimension in the measurement domain, namely that of the computation for some tasks. We discuss MECANO, an architecture that integrates application measurement with network measurement to optimize the placement of workloads in the network. We have implemented an initial proof of concept of this architecture and validated its performance in our testbed. In particular, keeping track at the network layer of the location of running instances of a specific function can significantly speed up the completion time for a specific client request.

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Edge computing in the ePC

Cited by 18 publications

References 10 publications

Pruning Edge Research with Latency Shears

Pruning Edge Research with Latency Shears

Open-source RANs in Practice: an Over-The-Air Deployment for 5G MEC

MECANO: Integrated Measurement of Compute and Network Operations

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