TIME WINDOW BASED AUTO-REGRESSIVE HYBRID PSO FOR OPTIMAL CLOUD PACKAGE SELECTION
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Abstract
Rapid expansion of cloud technologies were mainly due to the increased requirements of cloud users. However, increased requests also laden with increased resource requirements especially due to the elastic nature of the cloud. This mandates the need for effective resource provisioning model. This paper presents a Time Window based Auto-Regressive Hybrid PSO (TWARP) model that provides faster and more appropriate resource allocations. The TWARP model is composed of a temporal data grouping model to create training data, an auto-regression model to predict future requirements, a PSO-SA based optimal package selection mechanism and a final request handling mechanism that allocates the actual resource to a user. Experiments indicate low time requirements and effective allocation levels. Comparison with recent literature works also indicates highly effective performances of the proposed model.
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References
A.B. Grant and O.T. Eluwole, “Cloud resource management virtual machines competing for limited resources,†In: 2013 55th international symposium ELMAR. IEEE; 2013, pp. 269–274.
A. Gulati, G. Shanmuganathan, A. Holler and I. Ahmad, “Cloud-scale resource management:challenges and techniques,†In: Proceedings of the 3rd USENIX conference on Hot topics in cloud computing. USENIX Association; 2011, pp. 3-3.
PT. Endo, A.V. de Almeida Palhares, N.N. Pereira, G.E. Goncalves, D. Sadok and J. Kelner, “et al.Resource allocation for distributed cloud: concepts and research challenges,†IEEE Netw vol.25(4), 2011, pp.42–46.
A. Tchernykh, U. Schwiegelsohn, V. Alexandrov and E.G. Talbi, “Towards understanding uncertainty in cloud omputing resource provisioning,†Procedia Computer Science, 51, 2015, pp.1772-1781.
M. Amiri and L. Mohammad-Khanli, “Survey on prediction models of applications for resources provisioning in cloud,†J. Netw. Comput. Appl. 82. 2017, pp. 93–113. http://dx.doi.org/10.1016/j.jnca.2017.01.016.
S. Singh, I. Chana and R. Buyya, “STAR: SLA-aware autonomic management of cloud resources,†IEEE Trans. Cloud Comput. http://dx.doi.org/10.1109/TCC. 2017.2648788. 2017.
A.N. Toosi, R.O. Sinnott, and R. Buyya, “Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka,†Future Generation Computer Systems, 79, 2018, pp.765-775.
M. Ghobaei-Arani, S. Jabbehdari and M.A. Pourmina, “An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach,†Future Gener. Comput. Syst. http://dx.doi.org/10.1016/j.future.2017. 02.022. 2017.
G. Mateescu, W. Gentzsch and C.J. Ribbens, “Hybrid computing-where HPC meets grid and cloud computing,†Future Gener, Comput. Syst. 27 (5). 2011, pp.440–453. http://dx.doi.org/10.1016/j.future.2010.11.003.
B. Javadi, J. Abawajy and R. Buyya, “Failure-aware resource provisioning for hybrid cloud infrastructure,†J. Parallel Distrib, Comput. vol. 72 (10). 2012, pp. 1318–1331. http://dx.doi.org/10.1016/j.jpdc.2012.06.012
X. Xu and X. Zhao, “A framework for privacy-aware computing on hybrid clouds with mixed-sensitivity data in: Proceedings of the IEEE International Symposium on Big Data Security on Cloud,†2015, pp. 1344–1349 http://doi.org/10 1109/HPCC-CSS-ICESS.2015.110.
X. Xu, M. Tang, and Y.C. Tian, “QoS-guaranteed resource provisioning for cloud-based MapReduce in dynamical environments,†Future Generation Computer Systems, 78, 2018, pp.18-30.
S. Ibrahim, H. Jin, L. Lu, S. Wu, B. He and L. Qi, “LEEN: Locality/fairness-aware key partitioning for MapReduce in the Cloud, in: Proceedings of IEEE 2nd International Conference on Cloud Computing Technology and Science,†CloudCom, IEEE, New York, Indianapolis, IN, 2010, pp. 17–24.
H. Lin, X. Ma, J. Archuleta, W.-c. Feng, M. Gardner and Z. Zhang, “Moon: MapReduce on opportunistic environments,†in: Proceedings of ACM 19th International Symposium on High Performance Distributed Computing, ACM, New York, Chicago, IL, 2010, pp. 95–106.
B. Tang, M. Moca, S. Chevalier, H. He and G. Fedak, “Towards MapReduce for Desktop Grid Computing,†in: Proceedings of 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC, IEEE, New York, Fukuoka, Japan, 2010, pp. 193–200.
L. Wang, J. Tao, R. Ranjan, H. Marten, A. Streit, J. Chen and D. Chen, “G-Hadoop: MapReduce across distributed data centers for data-intensive computing,†Future Gener. Comput. Syst. Vol. 29 (3). 2013, pp.739–750.
K. Kc and K. Anyanwu, “Scheduling hadoop jobs to meet deadlines, in: Proceedings of IEEE 2nd International Conference on Cloud Computing Technology and Science,†CloudCom, IEEE, New York, Indianapolis, IN, 2010, pp. 388–392.
J. Polo, Y. Becerra, D. Carrera, M. Steinder, I. Whalley, J. Torres and E. Ayguade, “Deadline-Based MapReduce Workload Management,†IEEE Trans. Netw. Serv. Manag, vol. 10 (2). 2013, pp.231–244.
W. Zhang, S. Rajasekaran, T. Wood and M. Zhu, “Mimp: Deadline and interference aware scheduling of hadoop virtual machines,†in: Proceedings of IEEE/ACM 14th International Symposium on Cluster, Cloud and Grid Computing, CCGrid, IEEE/ACM, New York, Chicago, IL, 2014, pp. 394–403.
P.C. Phillips, Towards a unified asymptotic theory for autoregression. Biometrika, vol.74(3), 1987, pp.535-547.
C. Han, P.C. Phillips, and D. Sul, “Lag length selection in panel autoregression,†Econometric Reviews, vol. 36(1-3), 2017, pp.225-240.
J.Kennedy, and R. Eberhart, “PSO optimization. In Proc,†IEEE Int. Conf. Neural Networks Vol. 4, 1995, pp. 1941-1948. IEEE Service Center, Piscataway, NJ.
Y. Shi, and R. Eberhart, “A modified particle swarm optimizer,†In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference on May, 1998, pp. 69-73. IEEE.
P. J. Van Laarhoven, and E. H. Aarts, “Simulated annealing,†In Simulated annealing: Theory and applications 1987, pp. 7-15. Springer, Dordrecht.
C. Madhumathi, and G. Ganapathy, “Cloud Package Selection for Academic Requirements using Multi Criteria Decision Making based Modified Ant Colony Optimization Technique,†International Journal of Engineering and Technology (IJET), 8, 2016, pp. 1205-1211
S. Dooms, T. De Pessemier and L. Martens, “Offline optimization for user-specific hybrid recommender systems,†Multimedia Tools and Applications, vol. 74(9), 2015, pp.3053-3076.
X. Ge, J. Liu, Q. Qi, and Z. Chen, “A new prediction approach based on linear regression for collaborative filtering,†IEEE Eighth International Conference In Fuzzy Systems and Knowledge Discovery (FSKD), Vol. 4, 2011, pp. 2586-2590