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An Approach for Minimizing Energy Consumption in Cloud Environment


Affiliations
1 Department of Computer Science, College of Education, University of Thi Qar, Nasiriya, Iran, Islamic Republic of
 

Background/Objectives: Cloud computing could be considered of vital paradigms in IT which allows services to be delivered to the users via the internet on demand and on pay as you go basis. The growing demand on cloud computing environments increasing the number of datacenters which in turn increase the amount of power consumption in datacenters along with cooling equipment. Load balancing is considered a major challenge affecting in cloud performance Methods: An existing problem is how to allocate Virtual Machines (VMs) to Physical Machines (PMs) or hosts. This process is called VM placement. An algorithm is proposed that can reduce power consumption. Findings: The proposed algorithm assigns VMs onto PMs based on first fit decreasing algorithm and improves an existed one through reducing power consumption by turning-off some under load hosts if available and migrating their VMs to other active hosts. Application: The presented approach could decrease significantly energy consumption in comparison with the existing one through migrating VMs from underload hosts and turns them off.
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  • Dilip K. Energy efficient resource allocation for cloud computing. Rourkela: Department of Computer Science and Engineering, National Institute of Technology; 2014. p. 1–85.
  • Elhossiny I. Job scheduling based on harmonization between the requested and available processing power in the cloud computing environment. Journal of Information Technology and Software Engineering. 2015; 125(3):23–6.
  • Zaouch A. Load balancing for improved quality of service in the cloud. International Journal of Advanced Computer Science and Applications. 2015; 6(7):1–6. Crossref
  • Gohil B. A comparative analysis of Virtual Machine placement techniques in the cloud environment. International Journal of Computer Applications. 2016; 156(14):1–7. Crossref
  • Geetinder K. Improved hyper-heuristic scheduling with load-balancing and RASA for cloud computing systems. International Journal of Grid and Distributed Computing. 2016; 9(1):13–24. Crossref
  • Mevada A. Enhanced energy efficient Virtual Machine placement policy for load balancing in cloud environment. International Journal of Current Research and Review. 2017; 9(6):1–4.
  • Kumar A. Virtual Machine placement in cloud computing. Indian Journal of Science and Technology. 2016; 9(29):1–5. Crossref
  • Li K. Migration-based Virtual Machine placement in cloud systems. IEEE 2nd International Conference on Cloud Networking (Cloud Net); 2013. p. 83–90. Crossref
  • Liao JS. Energy-efficient resource provisioning with SLA consideration on cloud computing. IEEE 41st International Conference on Parallel Processing Workshops; 2012. p. 206–11. Crossref
  • Dhari A, Arif KI. An efficient load balancing scheme for cloud computing. Indian Journal of Science and Technology. 2017; 10(11):1–8. Crossref
  • Phi NX. Load balancing algorithm to improve response time on cloud computing. International Journal on Cloud Computing: Services and Architecture. 2017; 7(6):1–12.

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  • An Approach for Minimizing Energy Consumption in Cloud Environment

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Authors

Khaldun Ibraheem Arif
Department of Computer Science, College of Education, University of Thi Qar, Nasiriya, Iran, Islamic Republic of

Abstract


Background/Objectives: Cloud computing could be considered of vital paradigms in IT which allows services to be delivered to the users via the internet on demand and on pay as you go basis. The growing demand on cloud computing environments increasing the number of datacenters which in turn increase the amount of power consumption in datacenters along with cooling equipment. Load balancing is considered a major challenge affecting in cloud performance Methods: An existing problem is how to allocate Virtual Machines (VMs) to Physical Machines (PMs) or hosts. This process is called VM placement. An algorithm is proposed that can reduce power consumption. Findings: The proposed algorithm assigns VMs onto PMs based on first fit decreasing algorithm and improves an existed one through reducing power consumption by turning-off some under load hosts if available and migrating their VMs to other active hosts. Application: The presented approach could decrease significantly energy consumption in comparison with the existing one through migrating VMs from underload hosts and turns them off.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i24%2F121964