Service Caching and Computation Resource Allocation for Large-Scale Edge Computing-Enabled Networks

In this paper, we consider a large-scale edge computing (EC)-enabled network. We consider multiple latency-sensitive services. We adopt a random service caching scheme and a computation resource allocation scheme at base stations (BSs). We first derive the successful service probability (SSP). Using tools from stochastic geometry and queuing theory, we formulate the SSP maximization problem with respect to (w.r.t.) the service caching distribution and computation resource allocation, which is a challenging non-convex problem due to the complicated form of the SSP. Using parallel successive convex approximation (SCA), we develop an efficient iterative algorithm to obtain a stationary point of the non-convex problem. Finally, by numerical simulations, we show that the proposed solution achieves a higher SSP than the baseline schemes. We also show the impacts of the cache size and service rate of EC servers.