Cache Control of Edge Computing System for Tradeoff Between Delays and Cache Storage Costs

Chen Hou, Cangqi Zhou, Qilong Huang, Chao-Bo Yan

Abstract: This paper studies the edge computing system (ECS) in which caching the frequently reusable service (FRS) at the edge server (ES) is an effective way to reduce delays. The larger cache space available in the ES (we call it ES cache space) might buffer the larger scale of FRS, and subsequently decrease the delays while bringing higher cache storage costs. Meanwhile, the distribution of FRS is not always known in advance. Therefore, how much ES cache space should be supplied to make the optimal tradeoff between the delays and cache storage costs arises as an interesting issue in practice. To address this issue, this paper first formulates the problem of determining the amount of ES cache space supply as a constrained Markov decision process (CMDP), then adopts the Zipf’s distribution to estimate the probability distribution of FRS, and finally proposes an effective cache space control algorithm (CSCA) guiding the ES to determine the amount of ES cache space supply to minimize the cache storage costs while maintaining the delays at the acceptable level. Theoretical analysis, simulations and field experiments document and illustrate its performance. Note to Practitioners—This paper addresses the interesting trade-off between the delays and cache storage costs for the edge computing system that operates with limited cache storage budgets while must satisfy the required real-time performances. It helps to improve the operation efficiency of the systems with edge computing setting in the area of Internet of Things (IoT) or Cyber-Physical Systems (CPS) that employ the edge server to cache the frequently reusable services, arriving at the minimization of the accumulative cache storage costs while maintaining the accumulative delays at the acceptable level. Theoretical analysis, simulation and field experimental investigations jointly show that the solution proposed here outperforms existing solutions.