Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks
Jie Xu, Lixing Chen, Pan Zhou

TL;DR
This paper presents OREO, an online algorithm that jointly optimizes service caching and task offloading in dense MEC networks, significantly reducing latency and energy use without needing future system information.
Contribution
It introduces a novel online algorithm based on Lyapunov optimization and Gibbs sampling for dynamic service caching and task offloading in MEC, addressing key challenges in dense networks.
Findings
Reduces computation latency for users
Maintains low energy consumption
Operates effectively without future system info
Abstract
Mobile Edge Computing (MEC) pushes computing functionalities away from the centralized cloud to the network edge, thereby meeting the latency requirements of many emerging mobile applications and saving backhaul network bandwidth. Although many existing works have studied computation offloading policies, service caching is an equally, if not more important, design topic of MEC, yet receives much less attention. Service caching refers to caching application services and their related databases/libraries in the edge server (e.g. MEC-enabled BS), thereby enabling corresponding computation tasks to be executed. Because only a small number of application services can be cached in resource-limited edge server at the same time, which services to cache has to be judiciously decided to maximize the edge computing performance. In this paper, we investigate the extremely compelling but much less…
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Taxonomy
TopicsCaching and Content Delivery · IoT and Edge/Fog Computing · Opportunistic and Delay-Tolerant Networks
