Online Fresh Service Caching, Task Offloading, and Resource Allocation in Mobile Edge Computing

摘要

Service caching facilitates service provisioning in mobile edge computing (MEC) systems, which support latency-sensitive and computationally-intensive applications (services) such as intelligent traffic systems and online video games. In such systems, users are served by service data cached locally at an edge server (ES). The ES may use its cached service data to serve requests from its users and allocate its computation resources to the users, or offload the requests to the cloud center (CC). The ES should also download refreshed versions of the service data from the CC to maintain the freshness of its cached service data. In this paper, we investigate the joint online design of fresh service caching, task offloading, and computation resource allocation of the ES. The formulated problem involves spatially and temporally coupled variables, which makes the problem challenging to handle. To solve the formulated problem, we propose a two-layer online algorithm termed Knapsack Problem-based Double Proximal policy optimization (KPDP) algorithm, in which the first layer addresses service caching by using proximal policy optimization (PPO) and knapsack problem formulation, while the second layer addresses task offloading and computation resource allocation by using another PPO. We also perform a competitive ratio analysis for our proposed algorithm. Through extensive simulation experiments, it shows that KPDP algorithm outperforms other benchmark algorithms.