Game theory-based virtual machine migration for energy sustainability in cloud data centers

被引:2
作者
Maldonado-Carrascosa, Francisco Javier [1 ]
Garcia-Galan, Sebastian [1 ]
Valverde-Ibanez, Manuel [2 ]
Marciniak, Tomasz [3 ]
Szczerska, Malgorzata [4 ]
Ruiz-Reyes, Nicolas [1 ]
机构
[1] Jaen Univ, Linares Higher Polytech Sch, Telecommun Engn Dept, Ave Univ S-N, Jaen 23700, Spain
[2] Jaen Univ, Linares Higher Polytech Sch, Elect Engn Dept, Ave Univ S-N, Jaen 23700, Spain
[3] Bydgoszcz Univ Sci & Technol, Inst Telecommun & Comp Sci, Prof Sylwestra Kaliskiego 7, PL-85796 Bydgoszcz, Poland
[4] Gdansk Univ Technol, Fac Elect Telecommun & Informat, Metrol & Optoelect Dept, Gabriela Narutowicza 11-12, PL-80233 Gdansk, Poland
关键词
Cloud computing; Virtual machine migration; Scheduling; Follow the renewable; Game theory;
D O I
10.1016/j.apenergy.2024.123798
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
As the demand for cloud computing services increases, optimizing resource allocation and energy consumption has become a key factor in achieving sustainability in cloud environments. This paper presents a novel approach to address these challenges through an optimized virtual machine (VM) migration strategy that employs a game-theoretic approach based on particle swarm optimization (PSO) (PSO-GTA). The proposed approach leverages the collaborative and competitive dynamics of Game Theory to minimize energy consumption while using renewable energy. In this context, the game is represented by the swarm, where each player, embodied by particles, carries both competitive and cooperative elements essential to shape the collective behavior of the swarm. PSO is integrated to refine migration decisions, improving global convergence and optimizing the allocation of VMs to hosts. Through extensive simulations and performance evaluations, the proposed approach demonstrates significant improvements in resource utilization and energy efficiency, promoting sustainability in cloud computing environments. This research contributes to the development of environmentally friendly cloud computing systems, thus ensuring the delivery of energy-efficient cloud computing. The results demonstrate that the proposed approach outperforms fuzzy and genetic methods in terms of renewable energy usage. The PSO-GTA algorithm consistently outperforms Q-Learning, Pittsburgh and KASIA across three simulation scenarios with varying cloudlet dynamics, showcasing its efficiency and adaptability, and yielding improvements ranging from 0.68% to 5.32% over baseline results in nine simulations.
引用
收藏
页数:16
相关论文
共 42 条
[1]  
Ahmad S., 2023, CONT STUDIES RISKS E, P241, DOI DOI 10.1108/978-1-80455-562-020231016
[2]   SR-PSO: server residual efficiency-aware particle swarm optimization for dynamic virtual machine scheduling [J].
Ajmera, Kashav ;
Tewari, Tribhuwan Kumar .
JOURNAL OF SUPERCOMPUTING, 2023, 79 (14) :15459-15495
[3]  
Alyas T, 2023, Comput Mater Contin, V74
[4]  
[Anonymous], 1980, Ph.D. Thesis
[5]   Multi-objective edge server placement using the whale optimization algorithm and game theory [J].
Asghari, Ali ;
Azgomi, Hossein ;
Darvishmofarahi, Zahra .
SOFT COMPUTING, 2023, 27 (21) :16143-16157
[6]   Energy-efficiency and sustainability in new generation cloud computing: A vision and directions for integrated management of data centre resources and workloads [J].
Buyya, Rajkumar ;
Ilager, Shashikant ;
Arroba, Patricia .
SOFTWARE-PRACTICE & EXPERIENCE, 2024, 54 (01) :24-38
[7]   Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market [J].
Chang, Weiguang ;
Dong, Wei ;
Yang, Qiang .
APPLIED ENERGY, 2023, 336
[8]   Virtual machine migration policy for multi-tier application in cloud computing based on Q-learning algorithm [J].
Cong Hung Tran ;
Thanh Khiet Bui ;
Tran Vu Pham .
COMPUTING, 2022, 104 (06) :1285-1306
[9]  
El Mir Iman, 2017, International Journal of Communication Networks and Information Security, V9, P345
[10]   Swarm Fuzzy Systems: Knowledge Acquisition in Fuzzy Systems and Its Applications in Grid Computing [J].
Garcia-Galan, Sebastian ;
Prado, Rocio P. ;
Munoz Exposito, Jose Enrique .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2014, 26 (07) :1791-1804