Work place: National Institute of Technology, Srinagar, 190006, India
E-mail: ahsan@nitsri.net
Website:
Research Interests: Computer Science & Information Technology, Applied computer science, Computational Science and Engineering, Computer systems and computational processes, Theoretical Computer Science
Biography
Mohammad Ahsan Chishti, has done his Bachelor of Engineering and M.S. in Computer and Information Engineering from International Islamic University Malaysia with specialization in Computer Networking. He has received his Ph.D. from National Institute of Technology Srinagar, India. Presently he is working as Assistant Professor and Head of the Department of Computer Science & Engineering, National Institute of Technology Srinagar, India. He has more than 60 research publications to his credit and 10 patents with two granted International Patents. He is a Young Scientist awardee from Department of Science & Technology, Government of Jammu and Kashmir for the year 2009-2010 andIEI young engineer awardee 2015. He is Senior Member-Institute of IEEE, Member IEI, Life Member CSI, and Member IETE. He is a certified White belt in Six Sigma by Six Sigma Advantage Inc. of USA (SSAI).
By Mohammad Irfan Bala Mohammad Ahsan Chishti
DOI: https://doi.org/10.5815/ijwmt.2019.06.01, Pub. Date: 8 Nov. 2019
Cloud computing is a highly popular computing paradigm providing on-demand resources with high reliability and availability. The user requests are fulfilled by providing a virtual machine with the requested configuration. However, with the ever-increasing load on the cloud resources, the need for optimal resource utilization of the cloud resources has become the need of the hour. Load balancing has been identified as one of the possible ways to improve resource utilization in the cloud and the current state-of-the-art algorithms indicate the numerous attempts made to find the approximate solution for this NP-hard problem. In this work, we have focused on evaluating the efficiency of the Hungarian algorithm for load distribution in the cloud and compared its performance with First-come-first-serve (FCFS). The simulations were carried out in CloudSim and show remarkable improvement in various performance parameters. Finish time of a given task schedule was reduced by 41% and average execution time was reduced by 13% in the Hungarian algorithm when compared with FCFS. The simulations were carried out under different workload conditions to validate our results.
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