International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 10, No. 12, 8 Dec. 2018
Cover page and Table of Contents: PDF (size: 707KB)
Author(s)
Index Terms
Cloud, virtual machine, RAM, CPU, energy, response time
Abstract
The tremendous gain owing to the ubiquitous acceptance of the cloud services across the globe results in more complexity for the cloud providers by way of resource maintenance. This has a direct effect on the cost economy for them if the resources are not efficiently utilized. Most of the allocation strategies follow mechanisms involving direct allotment of VMs onto the servers based on their capabilities. This paper presents a VM allocation strategy that looks at VM placement by allowing server capacity to be partitioned into different classes. The classes are mainly based on the RAM and processing abilities which would be matched with VMs need. When the match is found the servers from this category are provisioned for the task executions. Based on the experimentation for various datacenter scenarios, it has been found that the proposed mechanism results in significant energy savings with reduced response time compared to the traditional VM allocation policies.
Cite This Paper
Shreenath Acharya, Demian Antony D’Mello, "Energy Saving VM Placement in Cloud", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.12, pp. 28-35, 2018. DOI:10.5815/ijmecs.2018.12.04
Reference
[1]Ankita Chaudary, Shilpa Ranab and K.J. Matahai, "A Critical Analysis of Energy Efficient Virtual Machine Placement Techniques and its Optimization in a Cloud Computing Environment", Procedia Computer Science, Volume 78, pp.132-138, 2015.
[2]Jiang Tao Zhang, Hejiao Huang and Xuan Wang, " Resource provision algorithms in cloud computing: A survey", Journal of network and computer applications, Volume 64, pp.23-42, 2016.
[3]Anton Beloglazov, Jemal Abawajy and Rajkumar Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing”, Future Generation Computer Systems, 2011.
[4]Jasnil Bodele, Anil Sarje, “Dynamic Load Balancing With Cost & Energy Optimization in Cloud Computing”, IJERT Vol. 2 Issue 4, ISSN: 2278-0181, 2013.
[5]Chao-Tung Yang, Hsiang-Yao Cheng, and Kuan-Lung Huang, “A dynamic resource allocation model for virtual machine management on cloud”, Springer-Verlag Berlin Heidelberg, CCIS 261, pp. 581–590, 2011
[6]Nguyen Quang-Hung and Nam Thoai, “Minimizing Total Busy Time with Application to Energy-efficient Scheduling of Virtual Machines,“ International Conference on Advanced Computing and Applications pp. 141-148, 2016
[7]Weiwei Lina, James Z. Wangb, Chen Liangc, Deyu Qi, “A threshold-based dynamic resource allocation scheme for cloud computing”, Elsevier Procedia Engineering, 23, pp. 695 – 703, 2011
[8]SivadonChaisiri, Bu-Sung Lee and DusitNiyato, “Optimization of resource provisioning cost in cloud computing”, IEEE transactions on services computing, Vol. 5, No. 2, 2012
[9]Sharrukh Zaman and Daniel Grosu, “A combinatorial auction based mechanism for dynamic VM provisioning and allocation in clouds”, IEEE Transactions on Cloud Computing, Vol. 1, No. 2, 2013
[10]Jyotiska Nath Khasnabish, Mohammad Firoj Mithaniand, Shrisha Rao, “Tier-Centric resource allocation in multi-tier cloud systems”, IEEE Transactions on Cloud Computing, in press, DOI: 10.1109/TCC.2015.2424888, 2015
[11]T.P. Shabeera, S.D. Madhu Kumar, Sameera M. Salam and K. Murali Krishnan, “Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO meta –heuristic algorithm,” Engineering Science and Technology, an International Journal Elsevier, Vol. 20, pp.616–628, 2017.
[12]Pooja S Kshirasagar and Anita M Pujar, “Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing”, International Journal of Modern Education and Computer Science, MECS Publishers, 2, pp. 27-33, 2017. DOI: 10.5815/ijmecs.2017.02.03
[13]Madhukar Shelar, Shirish Sane, Vilas Kharat and Rushikesh Jadhav, “Efficient Virtual machine Placement with Energy Savings in Cloud datacenter”, International Journal of Cloud-Computing and Super-Computing Vol.1, No.1, pp.15-26, 2014 http://dx.doi.org/10.14257/ijcs.2014.1.1.02
[14]Shreenath Acharya and Demian Antony D’Mello, “Energy and Cost Efficient Dynamic Load Balancing Algorithm for Resource Provisioning in Cloud”, International Journal of Applied Engineering Research (IJAER), Vol. 12, No. 24, 2017.