Joint Optimization of Service Caching Task Offloading and Resource Allocation in Cloud-Edge Cooperative Network

The cloud-edge cooperative network presents both opportunities and challenges for latency-sensitive and computation-intensive tasks. Effectively harnessing the strengths of edge computing and cloud computing enables real-time task handling, thus reaching a win-win situation where not only the stated quality of service (QoS) is delivered from the angle of service providers, but also the quality of experience (QoE) is improved from the angle of service requestors. However, due to the unpredictable task generation and time-varying environments, it is challenging to achieve optimal task scheduling and effective resource management and allocation. To address this issue, we propose an innovative cloud-edge framework that incorporates task offloading, service caching, and resource allocation in this paper. In this framework, we can determine where to offload the task, e.g., locally, at the edge, or in the cloud center. In view of the importance of the superior user experience, we aim to maximize the user satisfaction regarding task offloading in this framework. The problem is actually a mixed-integer nonlinear programming (MINLP) problem that entails simultaneously addressing cache decisions, offloading decisions, and resources allocation in a dynamic cloud-edge computing system. Owing to the NP-hardness, our original problem is decomposed into two layers of alternating problems. Specifically, we adopt a genetic algorithm (GA) based approach to jointly make cache and offloading decisions, and then iteratively optimize the communication and computing resources allocation. Extensive experimentation has demonstrated the feasibility and effectiveness of the proposed approach.