Hierarchy based least square approximation and interpolation method for resource allocation in cloud environment
Keywords:
Cloud computing, resource allocation, least square approximation, processing elements, optimal solutionAbstract
Cloud resource allocation is a complex process because the Service Level Agreement(SLA) parameters and Quality of Service (QoS) parameter should be satisfied before allocating resources to the user. In this paper, resources are allocated to the user based on user requested memory and bandwidth. Weighted Least Square Approximation method is used to match the user required memory and bandwidth with the available resource memory and bandwidth. Weighted Least Square Approximation method produces a set of line equations that match the user request with the resource. For each parameter set of the line equation will be produced. Iterative Interpolation Technique is used to predict suitable resources by using the set of line equations. Data are tested in CloudSim, the proposed method provides an optimal solution in all test cases.
References
BaominXu ,Chunyan Zhao, Enzhao Hu and Bin Hu, “Job scheduling algorithm based on Berger model in cloud environment”, Advances
in Engineering Software, Elsevier, Vol. 42 (2011) , pp. 419–425.
LiyunZuo, Shoubin Dong, Lei Shu, Chunsheng Zhu and Guangjie Han, “A Multi-queue Interlacing Peak Scheduling Method Based on
Tasks”, Classification in Cloud Computing‟, IEEE Systems Journal, 2016.
Narander Kumar and Swati Saxena, “A Preference-based Resource Allocation In Cloud Computing Systems”, 3rd International Conference on Recent Trends in Computing, Procedia Computer Science, Elsevier, Vol.57 ( 2015 ), pp. 104 – 111.
MehiarDabbagh, Bechir Hamdaoui, Mohsen Guizani and AmmarRayes, “Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers”, IEEE Transactions on Network and Service Management, Vol. 12 (2015), No. 3, pp. 377-391.
JiLia, LonghuaFenga and Shenglong Fang, “An Greedy-Based Job Scheduling Algorithm in Cloud Computing”, Journal of Software,
Academy Publisher, Vol. 9, No. 4, April 2014, pp. 921-925.
Hang Liu, Shiwen Liu and KanZheng, „A Reinforcement LearningBased Resource Allocation Scheme for Cloud Robotics‟, IEEE Access, Vol.6, 2018.
AnushaBamini Antony Muthu1 and Sharmini Enoch, “Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms
in Cloud Environment”, International Journal of Intelligent Engineering and Systems, Vol.10, No.5, 2017.
Zhang Q, Cherkasova L, Smirni E (2007) A regression-based analyticmodel for dynamic resource provisioning of multi-tier applications.In: Proc. of the 4th ICAC Conference, Jacksonville, Florida, USA,pp. 27–27.
Kalbasi A, Krishnamurthy D, Rolia J, Richter M (2011) “MODE: Mix drivenon-line resource demand estimation”. in Proceedings of the 7th International Conference on Network and Services Management. International Federation for Information Processing, pp 1–9.
Liu Y, Gorton I, Fekete A (2005) “Design-level performance prediction of component-based applications”. IEEE Trans SoftwEng 31(11):928–941.
S.S. Sastry, “Introductory Methods of Numerical Analysis”, 5th Edition, PHI Learning Private Limited, Delhi, India, 2012, pp. 101-125.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.