Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile-Edge Computing

作者：Jiechen Chen, Hong Xing, Xiaohui Lin, Arumugam Nallanathan, Suzhi Bi · 发表于：IEEE Internet of Things Journal · 年份：2022 · DOI：10.1109/jiot.2022.3196908 · 被引用次数：37 · 研究领域：IoT and Edge/Fog Computing、Caching and Content Delivery、Context-Aware Activity Recognition Systems

With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile-edge computing (MEC), has emerged as a promising technique to reinforce the computation capability of the resource-constrained IoT devices. To exploit the cloud-like functions at the network edge, service caching has been implemented to reuse the computation task input/output data, thus effectively reducing the delay incurred by data retransmissions and repeated execution of the same task. In a multiuser cache-assisted MEC system, users’ preferences for different types of services, possibly dependent on their locations, play an important role in the joint design of communication, computation, and service caching. In this article, we consider multiple representative locations, where users at the same location share the same preference profile for a given set of services. Specifically, by exploiting the location-aware users’ preference profiles, we propose joint optimization of the binary cache placement, the edge computation resource, and the bandwidth (BW) allocation to minimize the expected sum-energy consumption, subject to the BW and the computation limitations as well as the service latency constraints. To effectively solve the mixed-integer nonconvex problem, we propose a deep learning (DL)-based offline cache placement scheme using a novel stochastic quantization-based discrete-action generation method. The proposed hybr...