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.