JCSP: Joint Caching and Service Placement for Edge Computing Systems

TL;DR

This paper introduces JCSP, a joint stochastic modeling approach that optimizes content caching and service placement in edge computing, significantly improving response time and reducing memory usage.

Contribution

It develops a novel joint modeling framework combining queueing networks and caching components for edge computing resource optimization.

Findings

Up to 35% response time improvement

500MB memory reduction compared to baselines

Effective resource allocation in real-world traces

Abstract

With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency interplay of caching and queueing. Therefore, in this paper, we propose a novel class of stochastic models that enable the optimization of content caching and service placement decisions jointly. We first explain how to apply layered queueing networks (LQNs) models for edge service placement and show that combining this with genetic algorithms provides higher accuracy in resource allocation than an established baseline. Next, we extend LQNs with caching components to establish a joint modeling method for content caching and service placement (JCSP) and present analytical methods to analyze the resulting model. Finally, we simulate real-world Azure traces to evaluate the JCSP method and find that JCSP achieves up to 35% improvement in response time and 500MB reduction in memory usage than baseline heuristics for edge caching resource allocation.