Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

Authors

  • Sakshi Chhabra National Institute of Technology Kurukshetra Haryana, India https://orcid.org/0000-0002-2447-1611
  • Ashutosh Kumar Singh National Institute of Technology Kurukshetra Haryana, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2083

Keywords:

Cloud Computing, Resource configuration, Dynamic Allocation, Optimization.

Abstract

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

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Author Biographies

Sakshi Chhabra, National Institute of Technology Kurukshetra Haryana, India

Sakshi Chhabra. She received the BCA degree in Computer Applications from the Punjab University, Chandigarh in 2012, and the MCA degree in Computer Applications in 2015 (INDIA). She just completed her Ph.D degree in 2020 from National Institute of Technology, Kurukshetra in Department of Computer Applications. Currently, she is working as an Assistant Professor in the above institute. Her main research interests include Cloud Computing, Load Balancing and Information Security. She has published the research papers in SCI, Scopus journals and International Conferences.

Ashutosh Kumar Singh, National Institute of Technology Kurukshetra Haryana, India

Ashutosh Kumar Singh. He is working as a Professor in National Institute of Technology, Kurukshetra, India. He has more than 15 years research and teaching experience in various Universities of India, UK, and Malaysia. Prior to this appointment, he has worked as an Associate Professor and Head of Department Electrical and Computer Engineering in School of Engineering Curtin University Australia offshore Campus Malaysia, Sr. Lecturer and Deputy Dean (Research and Graduate Studies) in Faculty of Information Technology, University Tun Abdul Razak Kuala Lumpur Malaysia, Post Doc RA in the Department of Computer Science, University of Bristol, Faculty of Information Science and Technology, Multimedia University Malaysia and Sr. Lecturer in Electronics and Communication Department at NIST, India. His research area includes Web Technology, Big Data, Verification, Synthesis, Design and Testing of Digital Circuits. He has published more than 300 research papers now in different journals, conferences and news magazines.

References

Yu, Rong, Yan Zhang, Stein Gjessing, Wenlong Xia, and Kun Yang. ‘Toward cloud-based vehicular networks with efficient resource management.’ IEEE Network 27, no. 5, 2013: 48–55.

Chhabra Sakshi, and Ashutosh Kumar Singh. ‘A Probabilistic Model for Finding an Optimal Host Framework and Load Distribution in Cloud Environment.’ Procedia Computer Science 125 (2018): 683–690.

Dou, Wanchun, Xiaolong Xu, Xiang Liu, Laurence T. Yang, and Yiping Wen. ‘A Resource Co-Allocation method for load-balance scheduling over big data platforms.’ Future Generation Computer Systems 86 (2018): 1064–1075.

Tso, Fung Po, and Dimitrios P. Pezaros. ‘Improving data center network utilization using near-optimal traffic engineering.’ IEEE transactions on parallel and distributed systems 24, no. 6 (2013): 1139–1148.

Liang, Quan, Jing Zhang, Yong-hui Zhang, and Jiu-mei Liang. ‘The placement method of resources and applications based on request prediction in cloud data center.’ Information Sciences 279 (2014): 735–745.

Ma, Teng, Jiangxing Wu, Yuxiang Hu, and Wanwei Huang. ‘Optimal VM placement for traffic scalability using Markov chain in cloud data centre networks.’ Electronics Letters 53, no. 9 (2017): 602–604.

Zuo, Liyun, Shoubin Dong, Lei Shu, Chunsheng Zhu, and Guangjie Han. ‘A multiqueue interlacing peak scheduling method based on tasks classification in cloud computing.’ IEEE Systems Journal 12, no. 2 (2016): 1518–1530.

Peng, Jun-jie, Xiao-fei Zhi, and Xiao-lan Xie. ‘Application type based resource allocation strategy in cloud environment.’ Microprocessors and Microsystems 47 (2016): 385–391.

S. Chhabra and A.K. Singh. ‘Dynamic Hierarchical Load Balancing Model for Cloud Data Center Networks.’ IET Digital Library, Volume 55, Issue 2, 24 January 2019, pp. 94–96.

Dou, Wanchun, Xiaolong Xu, Xiang Liu, Laurence T. Yang, and Yiping Wen. ‘A Resource Co-Allocation method for load-balance scheduling over big data platforms.’ Future Generation Computer Systems 86 (2018): 1064–1075.

Alkhanak, Ehab Nabiel, and Sai Peck Lee. ‘A hyper-heuristic cost optimisation approach for Scientific Workflow Scheduling in cloud computing.’ Future Generation Computer Systems 86 (2018): 480–506.

Zhao, Yangming, Yifan Huang, Kai Chen, Minlan Yu, Sheng Wang, and DongSheng Li. ‘Joint VM placement and topology optimization for traffic scalability in dynamic datacenter networks.’ Computer Networks 80 (2015): 109–123.

Published

2021-11-19

Issue

Section

Articles