Minimizing Service Latency Through Image-Based Microservice Caching and Randomized Request Routing in Mobile Edge Computing

In the context of mobile edge computing (MEC), the traditional method of requesting microservices from a central cloud can result in increased delay for users due to the physical distance between the user and the cloud server. To address this issue, MEC advocates for placing servers closer to the users at the edge of the network. However, this approach is constrained by the storage capacity and computing resources of edge servers (ESs). Therefore, it is crucial to devise a strategy for processing user requests that minimizes the average request delay. To address this problem, This article formulates the microservice caching problem as an image-based microservice placement and task request routing problem. We model the problem as an integer linear programming problem with multicondition constraints. Considering the limited resources of ESs, we propose a microservice placement algorithm called approximate algorithm based on randomized task request routing. The proposed algorithm is designed to provide near-optimal solutions in polynomial time, leveraging Chernoff's theorem. Our approach is evaluated through comparisons with two existing algorithms: 1) the image-pull-based microservice cache request algorithm and 2) the greedy-based microservice cache and request routing algorithm. The results demonstrate that our algorithm exhibits superior performance compared to existing methods.