e-space
Manchester Metropolitan University's Research Repository

    CPU and RAM Energy-based SLA-aware Workload Consolidation Techniques for Clouds

    Gul, Beenish, Khan, Imran A, Mustafa, Saad, Khalid, Osman, Hussain, Syed Sajid, Dancey, Darren and Nawaz, Raheel ORCID logoORCID: https://orcid.org/0000-0001-9588-0052 (2020) CPU and RAM Energy-based SLA-aware Workload Consolidation Techniques for Clouds. IEEE Access, 8. pp. 62990-63003. ISSN 2169-3536

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution.

    Download (2MB) | Preview

    Abstract

    Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as ‘‘pay as you go’’ provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server’s CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server’s capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    265Downloads
    6 month trend
    226Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record