A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers

被引:43
|
作者
Gholipour, Niloofar [1 ]
Arianyan, Ehsan [2 ]
Buyya, Rajkumar [3 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Sci & Res Branch, Tehran, Iran
[2] ICT Res Inst, Tehran, Iran
[3] Univ Melbourne, Sch Comp & Informat Syst, Cloud Comp & Distributed Syst CLOUDS Lab, Melbourne, Vic, Australia
关键词
Cloud computing; Consolidation; Containerization; Datacenter; Energy consumption; Resource management; VIRTUAL MACHINES; HEURISTICS; SIMULATION; PERFORMANCE; ALGORITHMS;
D O I
10.1016/j.simpat.2020.102127
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Cloud computing is being rapidly adopted for managing IT services as a notable solution due to diverse beneficiaries such as automatically optimized resource management as well as modern service delivery models. The container as a service has been recently introduced by cloud providers as a new service apart from traditional cloud services. Containers enable applications to run and deploy on isolated virtual space, and the operating system kernel is shared among them. Also, containerization has some attributes such as scalability, highly portable properties, and lightweight, for those reasons, it is applied for running isolated applications. Reducing energy consumption, as well as their CO2 emissions, are great deals for cloud providers. In this direction, consolidation is recommended as a vital energy-aware approach in cloud data centers. Previously, independent virtual machine migration or container migration was proposed in the literature for green computing in cloud data centers. However, this paper proposes a new cloud resource management procedure based on a multi-criteria decision-making method that takes advantage of a joint virtual machine and container migration approach concurrently. The results of simulations using ContainerCloudsim simulator validates the applicability of the proposed approach which shows notable reductions in energy consumption, SLA violation, and number of migrations in comparison with the state-of-the-art algorithms.
引用
收藏
页数:15
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