A Review of Multi-Objective Optimization for Cloud Environment Storage Optimization

The advent of cloud computing offers a novel mode of managing computing resources, enabling users to flexibly utilize required computing and storage resources through the network. Simultaneously, it allows large-scale computing centers and data centers to more effectively utilize their computing resources. With the rapid development of cloud computing technology, the exponential growth of massive data storage needs from numerous users has brought challenges in storage cost, performance, reliability, and security. Conventional single-objective optimization approaches, which concentrate exclusively on a singular performance metric, are increasingly inadequate to address the intricate requirements of cloud storage systems. In contrast, multi-objective optimization methods can simultaneously optimize multiple aspects of concern to users or operators, providing more comprehensive solutions for cloud computing services. In this paper, we first introduce the basic concepts of multi-objective optimization problems. Subsequently, we present a comprehensive review of multi-objective optimization applications in cloud storage systems, including the setting of optimization objectives, algorithm selection, and comparison methods of experimental results. Finally, this paper summarizes and discusses the current implementation of multi-objective evolutionary algorithms in cloud storage optimization, and provides an outlook on future research directions.