Quantitative analysis of hypervisor efficiency and energy consumption in heterogeneous multi-VM environments with varied server workloads

IF 3.5 2区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Simulation Modelling Practice and Theory Pub Date : 2025-03-08 DOI:10.1016/j.simpat.2025.103102

Mohammad A. Altahat, Khaldoon Mhaidat, Osama Al-Khaleel

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引用次数: 0

Abstract

Maximizing resource utilization and throughput is critical in modern data centers. Virtualization enables multiple virtual machines (VMs) to run on a single host via a hypervisor, but its impact on performance and energy efficiency remains a key consideration. This study evaluates the power consumption and throughput of various hypervisors under different workloads to assist system administrators in optimizing configurations. Experimental results show that Xen has the highest power consumption (86.1 Wh, Varmail), while KVM is the most efficient (60.5 Wh). Xen achieves the highest throughput (8579 ops/s), significantly outperforming KVM (17

\times

) and vSphere (5

\times

). Additionally, CentOS VMs exhibit higher throughput on Xen, while Ubuntu VMs perform better on vSphere ESXi. This analysis provides valuable insights for selecting the best operating system, hypervisor, and workload setup, helping to strike a balance between performance and energy efficiency.

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来源期刊

Simulation Modelling Practice and Theory 工程技术-计算机：跨学科应用

CiteScore

9.80

自引率

4.80%

发文量

142

审稿时长

21 days

期刊介绍： The journal Simulation Modelling Practice and Theory provides a forum for original, high-quality papers dealing with any aspect of systems simulation and modelling. The journal aims at being a reference and a powerful tool to all those professionally active and/or interested in the methods and applications of simulation. Submitted papers will be peer reviewed and must significantly contribute to modelling and simulation in general or use modelling and simulation in application areas. Paper submission is solicited on: • theoretical aspects of modelling and simulation including formal modelling, model-checking, random number generators, sensitivity analysis, variance reduction techniques, experimental design, meta-modelling, methods and algorithms for validation and verification, selection and comparison procedures etc.; • methodology and application of modelling and simulation in any area, including computer systems, networks, real-time and embedded systems, mobile and intelligent agents, manufacturing and transportation systems, management, engineering, biomedical engineering, economics, ecology and environment, education, transaction handling, etc.; • simulation languages and environments including those, specific to distributed computing, grid computing, high performance computers or computer networks, etc.; • distributed and real-time simulation, simulation interoperability; • tools for high performance computing simulation, including dedicated architectures and parallel computing.