Issue |
MATEC Web Conf.
Volume 335, 2021
14th EURECA 2020 – International Engineering and Computing Research Conference “Shaping the Future through Multidisciplinary Research”
|
|
---|---|---|
Article Number | 04007 | |
Number of page(s) | 9 | |
Section | Computer Engineering | |
DOI | https://doi.org/10.1051/matecconf/202133504007 | |
Published online | 25 January 2021 |
A Novel Local Search-Based Approximation Algorithm to Optimize Virtual Machine Placement With Resource Constraints
1 Reva University, Bengaluru, Karnataka - 560064, India
2 Reva University, Bengaluru, Karnataka - 560064, India
3 M.S. Engineering College, Bengaluru, Karnataka - 560064, India
* Corresponding author: darshan.ms.shah@gmail.com
Many problems in cloud computing are not solvable in polynomial time and only option left is to choose approximate solution instead of optimum. Virtual Machine placement is one of such problem with resource constraints in which overall objective is to optimize multiple resources of hosts during placement process. In this paper we have addressed this problem with large size NP-Hard instances and proposed novel local search-based approximation algorithm. This problem is not yet studied in the research community with NP hard instances. A new proposed algorithm is empirically evaluated with state-of-the-art techniques. and our algorithm has improved placement result by 18% in CPU utilization, 21% in resource contention and 26% in overall resource utilization for benchmark instances collected from azure private cloud data center.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.