Intelligent multi-agent reinforcement learning model for resources allocation in cloud computing

被引:17
|
作者
Belgacem, Ali [1 ]
Mahmoudi, Said [2 ]
Kihl, Maria [3 ]
机构
[1] Mhamed Bougara Univ, Boumerdes, Algeria
[2] Mons Univ, Mons, Belgium
[3] Lund Univ, Lund, Sweden
关键词
Cloud computing; Resource allocation; Multi-agent system; Q-learning; Energy consumption; Fault tolerance; Load balancing; LOAD-BALANCING ALGORITHM; FAULT-TOLERANCE; OPTIMIZATION ALGORITHM;
D O I
10.1016/j.jksuci.2022.03.016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Now more than ever, optimizing resource allocation in cloud computing is becoming more critical due to the growth of cloud computing consumers and meeting the computing demands of modern technology. Cloud infrastructures typically consist of heterogeneous servers, hosting multiple virtual machines with potentially different specifications, and volatile resource usage. This makes the resource allocation face many issues such as energy conservation, fault tolerance, workload balancing, etc. Finding a comprehensive solution that considers all these issues is one of the essential concerns of cloud service providers. This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent characteristics and the Q-learning process to improve the performance of cloud resource allocation. IMARM uses the properties of multi-agent systems to dynamically allocate and release resources, thus responding well to changing consumer demands. Meanwhile, the reinforcement learning policy makes virtual machines move to the best state according to the current state environment. Also, we study the impact of IMARM on execution time. The experimental results showed that our proposed solution performs better than other comparable algorithms regarding energy consumption and fault tolerance, with reasonable load balancing and respectful execution time. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.
引用
收藏
页码:2391 / 2404
页数:14
相关论文
共 50 条
  • [1] Multi-agent Reinforcement Learning for Task Allocation in Cooperative Edge Cloud Computing
    Ding, Shiyao
    SERVICE-ORIENTED COMPUTING, ICSOC 2021 WORKSHOPS, 2022, 13236 : 283 - 297
  • [2] Multi-agent reinforcement learning for intelligent resource allocation in IIoT networks
    Rosenberger, Julia
    Urlaub, Michael
    Schramm, Dieter
    2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT), 2021, : 118 - 119
  • [3] Cloud game computing offload based on Multi-Agent Reinforcement Learning
    Tian, Kaicong
    Yang, Hongwen
    Liu, Yitong
    Zheng, Qingbi
    2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL), 2022,
  • [4] Multi-Agent Reinforcement Learning Aided Resources Allocation Method in Vehicular Networks
    Ji, Yuxin
    Zhang, Xixi
    Wang, Yu
    Gacanin, Haris
    Sari, Hikmet
    Adachi, Fumiyuki
    Gui, Guan
    2022 IEEE 96TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-FALL), 2022,
  • [5] Multi-Agent Reinforcement Learning for Resource Allocation in IoT Networks with Edge Computing
    Liu, Xiaolan
    Yu, Jiadong
    Feng, Zhiyong
    Gao, Yue
    CHINA COMMUNICATIONS, 2020, 17 (09) : 220 - 236
  • [6] Container Allocation in Cloud Environment Using Multi-Agent Deep Reinforcement Learning
    Danino, Tom
    Ben-Shimol, Yehuda
    Greenberg, Shlomo
    ELECTRONICS, 2023, 12 (12)
  • [7] IMAV: An Intelligent Multi-Agent Model Based on Cloud Computing for Resource Virtualization
    Kim, Myougnjin
    Lee, Hanku
    Yoon, Hyogun
    Kim, Jee-In
    Kim, HyungSeok
    INFORMATION AND ELECTRONICS ENGINEERING, 2011, 6 : 199 - 203
  • [8] Multi-Agent Reinforcement Learning for Resource Allocation in Io T Networks with Edge Computing
    Xiaolan Liu
    Jiadong Yu
    Zhiyong Feng
    Yue Gao
    中国通信, 2020, 17 (09) : 220 - 236
  • [9] Distributed Multi-Cloud Multi-Access Edge Computing by Multi-Agent Reinforcement Learning
    Zhang, Yutong
    Di, Boya
    Zheng, Zijie
    Lin, Jinlong
    Song, Lingyang
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2021, 20 (04) : 2565 - 2578
  • [10] Multi-Agent Deep Reinforcement Learning for Cooperative Offloading in Cloud-Edge Computing
    Suzuki, Akito
    Kobayashi, Masahiro
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 3660 - 3666