A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center

被引:23
作者
Liu, Fagui [1 ]
Ma, Zhenjiang [1 ]
Wang, Bin [1 ]
Lin, Weiwei [1 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
Cloud computing; ant colony system; virtual machine consolidation; energy consumption; SLA; ENERGY-EFFICIENT; DYNAMIC CONSOLIDATION; MIGRATION; MANAGEMENT; HEURISTICS;
D O I
10.1109/ACCESS.2019.2961786
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another. However, excessive virtual machine consolidation can lead to dangerous Service Level Agreement (SLA) violations. Therefore, how to balance between effective energy consumption and SLA violations avoidance effectively is a paradox to be mediated. The virtual machine consolidation problem is NP-hard. The traditional heuristic algorithm is easy to fall into the local optimal and some meta-heuristic algorithms can help to avoid it. However, the existing meta-heuristic algorithms are with high complexity. Therefore, we propose a lower complexity multi-population ant colony system algorithm with the Extreme Learning Machine (ELM) prediction (ELM005F;MPACS). The algorithm firstly predicts the host state employing ELM and then the virtual machine on the overloaded host will be migrated to the normal host, while the virtual machine on the underloaded host will be consolidated to another underloaded host with higher utilization. Multiple populations concurrently construct migration plans and local search further optimizes the results obtained by each population to reduce SLA violations. We compare ELM005F;MPACS with the benchmark, heuristic and meta-heuristic algorithms. The experimental results have shown that compared with these algorithms, our algorithm reduces energy consumption, migration times and SLA violations effectively.
引用
收藏
页码:53 / 67
页数:15
相关论文
共 47 条
  • [1] Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing
    Alresheedi, Shayem Saleh
    Lu, Songfeng
    Abd Elaziz, Mohamed
    Ewees, Ahmed A.
    [J]. HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, 2019, 9 (01)
  • [2] Virtual Machine Placement optimization supporting performance SLAs
    Anand, Ankit
    Lakshmi, J.
    Nandy, S. K.
    [J]. 2013 IEEE FIFTH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING TECHNOLOGY AND SCIENCE (CLOUDCOM), VOL 1, 2013, : 298 - 305
  • [3] Quality-of-service in cloud computing: modeling techniques and their applications
    Ardagna, Danilo
    Casale, Giuliano
    Ciavotta, Michele
    Perez, Juan F.
    Wang, Weikun
    [J]. JOURNAL OF INTERNET SERVICES AND APPLICATIONS, 2014, 5 (01)
  • [4] Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers
    Arianyan, Ehsan
    Taheri, Hassan
    Sharifian, Saeed
    [J]. COMPUTERS & ELECTRICAL ENGINEERING, 2015, 47 : 222 - 240
  • [5] A View of Cloud Computing
    Armbrust, Michael
    Fox, Armando
    Griffith, Rean
    Joseph, Anthony D.
    Katz, Randy
    Konwinski, Andy
    Lee, Gunho
    Patterson, David
    Rabkin, Ariel
    Stoica, Ion
    Zaharia, Matei
    [J]. COMMUNICATIONS OF THE ACM, 2010, 53 (04) : 50 - 58
  • [6] Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony System
    Aryania, Azra
    Aghdasi, Hadi S.
    Khanli, Leyli Mohammad
    [J]. JOURNAL OF GRID COMPUTING, 2018, 16 (03) : 477 - 491
  • [7] Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system
    Ashraf, Adnan
    Porres, Ivan
    [J]. INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS, 2018, 33 (01) : 103 - 120
  • [8] Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers
    Beloglazov, Anton
    Buyya, Rajkumar
    [J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2012, 24 (13) : 1397 - 1420
  • [9] Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing
    Beloglazov, Anton
    Abawajy, Jemal
    Buyya, Rajkumar
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2012, 28 (05): : 755 - 768
  • [10] CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
    Calheiros, Rodrigo N.
    Ranjan, Rajiv
    Beloglazov, Anton
    De Rose, Cesar A. F.
    Buyya, Rajkumar
    [J]. SOFTWARE-PRACTICE & EXPERIENCE, 2011, 41 (01) : 23 - 50