Weathering the Reallocation Storm: Large-Scale Analysis of Edge Server Workload

Abstract

Efficient service placement and workload allocation methods are necessary enablers for the actively studied topic of edge computing. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks -a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections in 2013-2014, with more than 47M connections over ca. 800 access points. We identify the conditions for avoiding the reallocation storm for three common edge-based reallocation strategies, and study the latency-workload trade-off related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of top ES workload. Further, while a reallocation strategy aiming to minimize reallocation distance consistently resulted in the worst reallocation storms, the two other strategies, namely, a random reallocation strategy, and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments.

A dark and stormy night: Reallocation storms in edge computing

“…Further, we estimate spatial and spatio-temporal dependencies behind the reallocation storms and identify patterns leading to reallocation storms. This paper extends our previous work [10] by expanding overall on the study as well as including new sections on spatio-temporal analysis and further discussion on the results.…”

“…To further study edge environments and to characterize the spatio-temporal phenomena they introduce, we have explored both horizontal and vertical workloads with real-life data transmission. This paper focuses on the phenomena we call reallocation storms, expanding our earlier work [10]. We highlight how these horizontal and vertical workloads become bottlenecks of edge computing services and how reallocation strategies designed for clouds fail in the edge computing environment.…”

Network Privacy and Security Issues in 5G and 6G

“…This restricts aggressors from playing out a factual examination of the information got and surmising individual data from the information of clients 117 . United Learning (FL) thoughts are additionally hot issues in the examination local area with regards to ensuring security insurance 118,43 . FL is an adaptable AI approach that empowers for model preparing for immense amounts of information to be done locally on the delivered source, with the required demonstrating being done at every individual student in the league 119 .…”

The Many Faces of Edge Intelligence

“…Key factors for orchestration of the service deployment and access include user location, computational and communication resources, and application data. In essence, edge resources must be placed [58]- [60] and their resources allocated [61] in a way that considers such factors and their trade-offs.…”