Emerging Technologies in Computing. Second International Conference, iCETiC 2019, London, UK, August 19–20, 2019, Proceedings

Research Article

Economic Impact of Resource Optimisation in Cloud Environment Using Different Virtual Machine Allocation Policies

  • @INPROCEEDINGS{10.1007/978-3-030-23943-5_4,
        author={Bilal Ahmad and Zaib Maroof and Sally McClean and Darryl Charles and Gerard Parr},
        title={Economic Impact of Resource Optimisation in Cloud Environment Using Different Virtual Machine Allocation Policies},
        proceedings={Emerging Technologies in Computing. Second International Conference, iCETiC 2019, London, UK, August 19--20, 2019, Proceedings},
        proceedings_a={ICETIC},
        year={2019},
        month={7},
        keywords={Cloud computing Energy optimisation Resource optimisation Economic impact Service quality Green computing Virtualisation},
        doi={10.1007/978-3-030-23943-5_4}
    }
    
  • Bilal Ahmad
    Zaib Maroof
    Sally McClean
    Darryl Charles
    Gerard Parr
    Year: 2019
    Economic Impact of Resource Optimisation in Cloud Environment Using Different Virtual Machine Allocation Policies
    ICETIC
    Springer
    DOI: 10.1007/978-3-030-23943-5_4
Bilal Ahmad1,*, Zaib Maroof2, Sally McClean1, Darryl Charles1, Gerard Parr3
  • 1: Ulster University
  • 2: National Defence University
  • 3: University of East Anglia
*Contact email: ahmad-b@ulster.ac.uk

Abstract

Exceptional level of research work has been carried in the field of cloud and distributed systems for understanding their performance and reliability. Simulators are becoming popular for designing and testing different types of quality of service (QoS) matrices e.g. energy, virtualisation, and networking. A large amount of resource is wasted when servers are sitting idle which puts a negative impact on the financial aspects of companies. A popular approach used to overcome this problem is turning them ON/OFF. However, it takes time when they are turned ON affecting different matrices of QoS like energy consumption, latency, consumption and cost. In this paper, we present different energy models and their comparison with each other based on workloads for efficient server management. We introduce a different type of energy saving techniques (DVFs, IQRMC) which help toward an improvement in service. Different energy models are used with the same configuration and possible solutions are proposed for big data centres that are placed globally by large companies like Amazon, Giaki, Onlive, and Google.