EEVMC: An Energy Efficient Virtual Machine Consolidation Approach for Cloud Data Centers

被引:0
|
作者
Rehman, Attique Ur [1 ]
Lu, Songfeng [1 ,2 ]
Ali, Mubashir [3 ]
Smarandache, Florentin [4 ]
Alshamrani, Sultan S. [5 ]
Alshehri, Abdullah [6 ]
Arslan, Farrukh [7 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Cyber Sci & Engn, Wuhan 430074, Peoples R China
[2] Shenzhen Huazhong Univ Sci & Technol, Res Inst, Shenzhen 518057, Peoples R China
[3] Lahore Garrison Univ, Dept Software Engn, Lahore 54810, Pakistan
[4] Univ New Mexico, Math Phys & Nat Sci Div, Gallup, NM 87301 USA
[5] Taif Univ, Coll Comp & Informat Technol, Dept Informat Technol, Taif 21944, Saudi Arabia
[6] Al Baha Univ, Fac Comp & Informat Technol, Informat Technol Dept, Al Baha 65799, Saudi Arabia
[7] Univ Engn & Technol, Dept Elect Engn, Lahore 54500, Pakistan
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Cloud computing; Data centers; Energy efficiency; Quality of service; Energy consumption; Virtual machines; Power demand; Virtual machine consolidation; quality of service; energy efficient; VM migration; placement algorithm; OpenStack cloud; WORKLOAD CONSOLIDATION; AWARE; POLICY;
D O I
10.1109/ACCESS.2024.3429424
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 dynamic landscape of cloud computing design presents significant challenges regarding power consumption and quality of service (QoS). Virtual machine (VM) consolidation is essential for reducing power usage and enhancing QoS by relocating VMs between hosts. OpenStack Neat, a leading framework for VM consolidation, employs the Modified Best-Fit Decreasing (MBFD) VM placement technique, which faces issues related to energy consumption and QoS. To address these issues, we propose an Energy Efficient VM Consolidation (EEVMC) approach. Our method introduces a novel host selection criterion based on the incurred loss during VM placement to identify the most efficient host. For validation, we conducted simulations using real-time workload traces from Planet-Lab and Materna over ten days, leveraging the latest CloudSim toolkit to compare our approach with state-of-the-art techniques. For Planet-Lab's workload, our EEVMC approach shows a reduction in energy consumption by 80.35%, 59.76%, 21.59%, and 7.40%, and fewer system-level agreement (SLA) violations by 94.51%, 94.85%, 47.17%, and 17.78% when compared to Modified Best-Fit Decreasing (MBFD), Power-Aware Best Fit Decreasing (PABFD), Medium Fit Power Efficient Decreasing (MFPED), and Power-Efficient Best-Fit Decreasing (PEBFD), respectively. Similarly, for Materna, EEVMC achieves a reduction in energy consumption by 16.10%, 61.0%, 4.94%, and 4.82%, and fewer SLA violations by 76.99%, 88.88%, 12.50%, and 48.65% against the same benchmarks. Additionally, Loss-Aware Performance Efficient Decreasing (LAPED) significantly reduces the total number of VM migrations and SLA time per active host, indicating a substantial improvement in cloud computing efficiency.
引用
收藏
页码:105234 / 105245
页数:12
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