Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

Rajeshwari B S; M. Dakshayini; H.S. Guruprasad

doi:10.5614/itbj.ict.res.appl.2021.15.3.2

Authors

Rajeshwari B S Department of CSE, B.M.S College of Engineering, Bangalore 560019, India
M. Dakshayini Department of ISE, B.M.S College of Engineering, Bangalore 560019, India
H.S. Guruprasad Department of ISE, B.M.S College of Engineering, Bangalore 560019, India

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2021.15.3.2

Keywords:

cloud computing, fair load distribution, federated cloud, service level agreement, task scheduling

Abstract

The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.

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References

Assis, M.R.M, Bittencourt, L.F. & Tolosana-Calasanz, R., Cloud Federation: Characterization and Conceptual Model, IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 585-590, Dec. 2014. DOI: 10.1109/UCC.2014.90.

Lokesh, V., Jayaraman, S., N, A., Soni, A., & Guruprasad, H.S., A Survey on the Capabilities of Cloud Simulators, International Journal of Engineering Research and General Science, 3(4), pp. 958-969, Aug. 2015.

Rajeshwari, B.S. & Dakshayini, M., Optimized Bit Matrix-based Power Aware Load Distribution Policy among Federated Cloud, Elsevier Procedia Computer Science, 167, pp. 1771-1790, April 2020. DOI: 10.1016/j.procs.2020.03.387.

Culhane, W., Eugster, P., Jayalath, C., Kogan, K. & Stephen, J., Cloud Federation and Geo-Distribution, Encyclopedia of Cloud Computing, John Wiley and Sons Ltd., pp. 178-190, May 2016.

Sudhakara, S., Nithya, N.S. & Radhakrishnana, B.L., Fair Service Matching Agent for Federated Cloud, Computers and Electrical Engineering, Elsevier, 76, pp. 13-23, June 2019. DOI: 10.1016/ j.compeleceng.2019.03.002.

Sindhu, K. & Guruprasad, H.S., A Performance Analysis on Cloud Based Mobile Augmentation in Mobile Cloud Computing, McGraw-Hill International Conference on Signal, Image Processing Communication and Automation, JSSATE, Bangalore, pp. 398-403, 6th and 7th June 2017.

Bhavani B.H. & Guruprasad, H.S., A Comparative Study on Resource Allocation Policies in Cloud Computing Environment, Compusoft, An International Journal of Advanced Computer Technology, 3(6), pp. 893-899, June 2014.

Rajeshwari B.S., Dakshayini, M. & Guruprasad, H.S., Service Level Agreement-based Scheduling Techniques in Cloud: A Survey, International Journal of Computer Applications, 132(5), pp. 20-26, Dec. 2015. DOI: 10.5120/ijca 2015907358.

Ghobaei-Arani, M. & Shahidinejad, A., An Efficient Resource Provisioning Approach for Analyzing Cloud Workloads: A Metaheuristic based Clustering Approach, The Journal of Supercomputing, 77(1), pp. 711-750, Jan. 2021. DOI: 10.1007/s11227-020-03296-w.

Wei, J., Zhou, A., Yuan, J. & Yang, F., AIMING: Resource Allocation with Latency Awareness for Federated-Cloud Applications, Hindawi: Wireless Communications and Mobile Computing, pp.1-11, April 2018. DOI: 10.1155/2018/4593208.

Xu, J., & Palanisamy, B., Cost-Aware Resource Management for Federated Clouds using Resource Sharing Contracts, IEEE 10th International Conference on Cloud Computing, pp. 238-245, June 2017. DOI: 10.1109/CLOUD.2017.38.

Zhao, L., Du, M. & Chen, L., A New Multi-Resource Allocation Mechanism: A Tradeoff between Fairness and Efficiency in Cloud Computing, China Communications, IEEE, 15(3), pp. 57-77, April 2018. DOI: 10.1109 /CC.2018.8331991.

Habibi, M., Fazli, M. A. & Movaghar, A., Efficient Distribution of Requests in Federated Cloud Computing Environments Utilizing Statistical Multiplexing, Future Generation Computer Systems, Elsevier, 90, pp. 451-460, Jan. 2019. DOI: 10.1016/j.future.2018.08.032.

Taha, A., Manzoor, S. & Suri, N., SLA-Based Service Selection for Multi-Cloud Environments, IEEE International Conference on Edge Computing, pp. 65-72, June 2017. DOI: 10.1109/ IEEE.EDGE.2017.17.

Levin, A., Lorenz, D., Merlino, G., Panarello, A., Puliafito, A. & Tricomi, G., Hierarchical Load Balancing as a Service for Federated Cloud Networks, Computer Communication, Elsevier, 129, pp. 125-137, Sept. 2018. DOI: 10.1016/j.comcom.2018.07.031.

Motwani, A., Chaturvedi, R. & Shrivastava, A., Profit Based Data Center Service Broker Policy for Cloud Resource Provisioning, International Journal of Electrical, Electronics and Computer Engineering, 5(1), pp. 54-60, 2016.

Kumar, M. & Sharma, S.C., Deadline Constrained Based Dynamic Load Balancing Agorithm with Elasticity in Cloud Environment, Computers and Electrical Engineering, Elsevier, 69, pp. 395-411, July 2018. DOI: 10.1016/j.compeleceng.2017.11.018.

Sharma, M. & Sharma, P., Performance Evaluation of Adaptive Virtual Machine Load Balancing Algorithm, International Journal of Advanced Computer Science and Applications, 3(2), pp. 86-88, 2012. DOI: 10.14569/ IJACSA.2012.030215

Shahidinejad, A., Ghobaei-Arani, M., & Masdari, M., Resource Provisioning using Workload Clustering in Cloud Computing Environment: A Hybrid Approach, Cluster Computing, 24(1), pp. 319-342, March 2021. DOI: 10.1007/s10586-020-03107-0.

Aslanpour, M.S., Dashti, S.E., Ghobaei-Arani, M. & Rahmanian, A.A., Resource Provisioning for Cloud Applications: A 3-D Provident and Flexible Approach, The Journal of Supercomputing, 74(12), pp. 6470-6501, Dec. 2018. DOI 10.1007/s11227-017-2156-x.

Shahidinejad, A., Ghobaei-Arani, M. & Esmaeili, L., An Elastic Controller using Colored Petri Nets in Cloud Computing Environment, Cluster Computing, 23(2), pp. 1045-1071, June 2020. DOI: 10.1007/s105 86-019-02972-8.

Ghobaei-Arani, M., Souri, A., Baker, T. & Hussien, A., ControCity: An Autonomous Approach for Controlling Elasticity using Buffer Management in Cloud Computing Environment, IEEE Access 7, Special Section on Mobile Edge Computing and Mobile Cloud Computing: Addressing Heterogeneity and Energy Issues of Compute and Network Resources, pp. 106912-106924, Aug. 2019, DOI: 10.1109/ACCESS.2019.2932462.

Horowitz, E., Sahni, S. & Rajasekhara, Fundamentals of Computer Algorithms, 2nd Edition, University Press Pvt. Ltd, 2009.

Singh, D.A.A.G., Priyadharshini, R. & Leavline, E.J., Analysis of Cloud Environment using CloudSim, Artificial Intelligence and Evolutionary Computations in Engineering Systems, Springer, pp. 325-333, March 2018.

Goyal, T., Singh, A. & Agrawal, A., CloudSim: Simulator for Cloud Computing Infrastructure and Modeling, Procedia Engineering, Elsevier, 38, pp. 3566-3572, Sep. 2012. DOI: 10.1016/j.proeng.2012.06.412.