Real-time power optimization for application server clusters based on Mixed-Integer Programming

作     者：Xiong, Zhi Zhao, Min Tan, Linhui Cai, Lingru 

作者机构：Shantou Univ Dept Comp Sci & Technol Shantou 515063 Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Shantou Univ Minist Educ Shantou 515063 Peoples R China 

出 版 物：《FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE》 (下代计算机系统)

年 卷 期：2022年第137卷

页      面：260-273页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Guangdong Province, China [2018A030313438] Guangdong Basic and Applied Basic Research Foundation, China [2021A1515012527] Scientific Research Project of Colleges and Universities in Guangdong Province, China [2020ZDZX3073] Guangdong Science and Technology Innovation Strategy Special Fund, China [pdjh2022b0188] 

主　　题：Application server cluster Power optimization Dynamic voltage/frequency scaling Mixed-Integer Programming Primary-secondary optimization Differential evolution 

摘      要：In the environment of peer competition and energy conservation, optimizing the deployment of application server clusters in real time according to actual workload conditions to reduce operating costs and energy consumption is an important issue that must be urgently addressed. In this paper, we propose a real-time power optimization strategy for application server clusters, and the optimization measures include CPU dynamic voltage/frequency scaling and server dynamic switching. First, the feasibility of defining variables for server types is proved, and appropriate variables are defined to describe the cluster power optimization as a mixed-integer programming (MIP) problem. Then, two solution methods are proposed: the exact method based on the Gurobi optimizer and the approximate method based on primary-secondary optimization and differential evolution with two mutations (PSODE). The former turns the MIP problem into a standard mixed-integer quadratic programming form by introducing intermediate variables and solves it using the Gurobi optimizer. The latter rewrites the MIP problem as a primary-secondary optimization problem and proposes a differential evolutionary-based solution algorithm for the primary optimization problem. The evolutionary process consists of two mutation operations, inter- and intraindividual mutations, which both use a heuristic policy to accelerate the evolution convergence. The test results reveal that the Gurobi-based method can quickly determine the global optimal deployment when the cluster size is small. The PSODE-based method can quickly determine the global optimal deployment or high-quality suboptimal deployment when applied to large-scale clusters. (C) 2022 Elsevier B.V. All rights reserved.