Confidence interval-based overload avoidance algorithm for virtual machine placement

被引:3
|
作者
Ahmadi, Javad [1 ]
Haghighat, Abolfazl Toroghi [2 ]
Rahmani, Amir Masoud [3 ]
Ravanmehr, Reza [1 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Cent Tehran Branch, Tehran, Iran
[2] Islamic Azad Univ, Fac Comp & Informat Technol Engn, Qazvin Branch, Qazvin, Iran
[3] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu, Taiwan
来源
SOFTWARE-PRACTICE & EXPERIENCE | 2022年 / 52卷 / 10期
关键词
Green cloud; cloud computing; virtualization; dynamic consolidation; virtual machine placement; Overload avoidance; ENERGY-EFFICIENT; DYNAMIC CONSOLIDATION; VM CONSOLIDATION; DATA CENTERS; CLOUD; QUALITY; ENVIRONMENTS; CONSUMPTION; PREDICTION; SERVICE;
D O I
10.1002/spe.3127
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS-aware algorithm for overload avoidance and a power-aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.
引用
收藏
页码:2288 / 2311
页数:24
相关论文
共 50 条
  • [21] A Biogeography-based Optimization Algorithm for Energy Efficient Virtual Machine Placement
    Ali, H. M.
    Lee, Daniel C.
    2014 IEEE SYMPOSIUM ON SWARM INTELLIGENCE (SIS), 2014, : 231 - 236
  • [22] Energy-efficient virtual machine placement algorithm based on power usage
    Sunil, Shilpa
    Patel, Sanjeev
    COMPUTING, 2023, 105 (07) : 1597 - 1621
  • [23] VIRTUAL MACHINE PLACEMENT STRATEGY BASED ON DISCRETE FIREFLY ALGORITHM IN CLOUD ENVIRONMENTS
    Li, Xiao-Ke
    Gu, Chun-Hua
    Yang, Ze-Ping
    Chang, Yao-Hui
    2015 12TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICCWAMTIP), 2015, : 61 - 66
  • [24] Energy-efficient virtual machine placement algorithm based on power usage
    Shilpa Sunil
    Sanjeev Patel
    Computing, 2023, 105 : 1597 - 1621
  • [25] A Classification-Based Virtual Machine Placement Algorithm in Mobile Cloud Computing
    Tang, Yuli
    Hu, Yao
    Zhang, Lianming
    KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2016, 10 (05): : 1998 - 2014
  • [26] A Weighted PageRank-Based Algorithm for Virtual Machine Placement in Cloud Computing
    Yao, Wenbin
    Shen, Yue
    Wang, Dongbin
    IEEE ACCESS, 2019, 7 : 176369 - 176381
  • [27] An Efficient Request-Based Virtual Machine Placement Algorithm for Cloud Computing
    Panda, Sanjaya K.
    Jana, Prasanta K.
    DISTRIBUTED COMPUTING AND INTERNET TECHNOLOGY, (ICDCIT 2017), 2017, 10109 : 129 - 143
  • [28] A Matrix Transformation Algorithm for Virtual Machine Placement in Cloud
    Sun, Meng
    Gu, Weidong
    Zhang, Xinchang
    Shi, Huiling
    Zhang, Wei
    2013 12TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (TRUSTCOM 2013), 2013, : 1778 - 1783
  • [29] MTAD: A Multitarget Heuristic Algorithm for Virtual Machine Placement
    Chen, Lei
    Zhang, Jing
    Cai, Lijun
    Li, Rui
    He, Tingqin
    Meng, Tao
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
  • [30] Which is the best algorithm for virtual machine placement optimization?
    Mann, Zoltan Adam
    Szabo, Mate
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2017, 29 (10):