QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge Computing

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.

I. Introduction

With the proliferation of smartphones, IoT devices, and other connected gadgets, the demand for real-time data and services continues to grow. Services such as video streaming, online gaming, and IoT applications require lower latency and higher bandwidth, posing significant challenges to the bandwidth of the backbone network and cloud data center. The traditional high-latency centralized cloud center model may result in longer service response times, and may not be able to meet user demands when network congestion occurs. In order to address these challenges, multi-access edge computing has emerged as an appealing solution that deploys edge servers at the network edge, bringing computation and storage closer to end-users and devices, thereby enabling low-latency and high-bandwidth service delivery. However, as the amount of services and users continues to burgeon, and users exhibit increasingly dynamic behavior, the effective management of service caching and updating on edge servers presents a formidable challenge. Traditional caching strategies often lead to a waste of resources, while inflexible updating mechanisms may fail to ensure the Quality of Service (QoS) required by users. Consequently, there arises a critical imperative to develop a QoS-aware dynamic service caching and updating approach that not only caters to users’ QoS requirements but also maintains cost efficiency. The limited storage resources of edge servers and the dynamic behavior of users pose challenges to service deployment.