A Review on Optimization Approaches in Cloud Computing Service

  • Authors

    • V. E. Jayanthi
    • R. Divya
    • M. Jagannath
    https://doi.org/10.14419/ijet.v7i3.24.22804
  • Cloud service provider, Deadlock, Dynamic resource provisioning, Virtual machine migration, Starvation.

  • Cloud has become the best revenue generator tool in the business world. The Cloud Service Provider (CSP) gives importance to the big data arriving in the cloud. Many methodologies are currently available for cloud storage, retrieval and processing. But a discrete solution for all the problems is not possible due to the volume, velocity and variety of data arriving in cloud. Certain best and proved algorithms can be used based on resource utilization, cost pricing, load balancing for the effective utilizations of cloud. The profit based optimization becomes the goal of the CSP. This draws the attention in finding the major factors that influence cloud computing services. In this paper, a detailed survey of optimization techniques for various key factors of cloud are analyzed and the result obtained in each technique is consolidated, tabulated and compared.

     

     

  • References

    1. [1] G. Portaluri, S. Giordano, D. Kliazovich, B. Dorronsoro, A power efficient genetic algorithm for resource allocation in cloud computing data centers, In Proceedings of the IEEE 3rd International Conference on Cloud Networking (CloudNet), 2014, pp. 58-63.

      [2] H. Goudarzi, M. Pedram, Maximizing profit in cloud computing system via resource allocation, In Proceedings of the International Conference on Distributed Computing Systems Workshops, 2011, pp. 1-6.

      [3] M. Dabbagh, B. Hamdaoui, M. Guizani, A. Rayes, Exploiting task elasticity and price heterogeneity for maximizing cloud computing profits, IEEE Transactions on Emerging Topics in Computing, Vol. 6, No. 1, 2018, 85-96.

      [4] R. Yu, J. Ding, S. Maharjan, S. Gjessing, Y. Zhang, D. Tsang, Decentralized and optimal resource cooperation in geo-distributed mobile cloud computing, IEEE Transactions on Emerging Topics in Computing, Vol. 6, No. 1, 2015, pp. 72-84.

      [5] H. Chen, F. Wang, N. Helian, A cost-efficient and reliable resource allocation model based on cellular automaton entropy for cloud project scheduling, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 4, 2013, pp. 7-14.

      [6] L. Zuo, L. Shu, S. Dong, C. Zhu, T. Hara, A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing, IEEE Access, Vol. 3, 2015. pp. 2687-2699.

      [7] G. Zhang, J. Lu, Y. Gao, Multi-level Decision Making: Models, Methods and Applications, Springer Publishing Company, 2015, ISBN 978-3-662-46058-0.

      [8] M.A. Vouk, Cloud computing: Issues, research and implementations, In Proceedings of the 30th International Conference on Information Technology Interfaces, 2008, pp. 31-40.

      [9] H.H.C. Nguyen, H.V. Dang, N.M.N. Pham, V.S. Le, T.T. Nguyen, Deadlock detection for resource allocation in heterogeneous distributed platforms. In: Unger H., Meesad P., Boonkrong S. (Eds.) Recent Advances in Information and Communication Technology, Advances in Intelligent Systems and Computing, Vol. 361. Springer, Cham, 2015.

      [10] L. Wu, S.K. Garg, R. Buyya, SLA-based resource allocation for a software as a service provider in cloud computing environments, In Proceedings of the 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Los Angeles, USA, 2011, pp. 195-204.

      [11] R.N. Calheiros, R. Ranjan, A. Beloglazov, C.A.F.D. Rose, and R. Buyya, CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software – Practice and Experience, Vol. 41, 2011, pp. 23-50.

      [12] W. Shi, L. Zhang, C. Wu, Z. Li, F.C.M. Lau, An online auction framework for dynamic resource provisioning in cloud computing, IEEE/ACM Transactions on Networking, Vol. 24, No. 4, 2016, pp. 2060-2073.

      [13] Z. Cao, E.W. Zegura, Utility max-min: an application-oriented bandwidth allocation scheme, In Proceeding of the IEEE INFOCOM International Conference on Computer Communications, Vol. 2, 1999, pp. 793-801.

      [14] Y.O. Yazir, C. Matthews, R. Farahbod, S. Neville, A. Guitouni, S. Ganti, Y. Coady, Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis, In Proceedings of the IEEE 3rd International Conference on Cloud Computing, 2010, pp. 91-98.

      [15] W. Voorsluys, J. Broberg, S. Venugopal, R. Buyya, Cost of virtual machine live migration in clouds: A performance evaluation, In: Jaatun M., Zhao G., and C. Rong C. (Eds.), Cloud Computing, Lecture Notes in Computer Science, Springer Berlin Heidelberg, Vol. 5931, 2009, pp. 254-265.

      [16] Greenberg, J. Hamilton, D. Maltz, and P. Patel, The cost of a cloud: research problems in data center networks, ACM SIGCOMM Computer Communication Review, Vol. 39, No. 1, 2009, pp. 68-73.

      [17] A. Beloglazov, R. Buyya, Energy efficient resource management in virtualized cloud data centers, In Proceedings of the IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010, pp.826-831.

      [18] D. Kliazovich, P. Bouvry, S. Khan, Dens: Data center energy-efficient network-aware scheduling, In Proceedings of the Green Computing and Communications (GreenCom), IEEE/ACM International Conference on Cyber, Physical and Social Computing (CPSCom), 2010, pp. 69-75.

      [19] K.H. Kim, A. Beloglazov, R. Buyya, Power-aware provisioning of cloud resources for real-time services, In Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science, Urbana Champaign, Illinois, 2009, pp. 1-6.

      [20] S. Chen, S. Irving, L. Peng, Operational cost optimization for cloud computing data centers using renewable energy, IEEE Systems Journal, Vol. 10, No. 4, 2016, pp. 1447-1458.

      [21] A. Nodari, Cost Optimization in Cloud Computing, Master’s Thesis, Aalto University, 2015.

      [22] Amazon EC2, http://aws.amazon.com/ec2/ [Accessed July 09, 2018].

      [23] M. Andreolini, S. Casolari, M. Colajanni, and M. Messori, Dynamic load management of virtual machines in cloud architectures, In: Avresky D.R., Diaz M., Bode A., Ciciani B., Dekel E. (Eds.) Cloud Computing, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Springer, Berlin, Heidelberg, Vol. 34, 2009.

      [24] D. Bruneo, S. Distefeno, Quantitative Assessment on Distributed Systems: Methods and Techniques, Wiley Scrivener Publishing, 2015, ISBN: 978-1-118-59521-3.

      [25] S. Kaur, V. Pandey, A survey of virtual machine migration techniques in cloud computing, Computer Engineering and Intelligent Systems, Vol. 6, No. 7, 2015, pp. 28-34.

      [26] P. Kaur, A. Rani, Virtual machine migration in cloud computing, International Journal of Grid Distribution Computing, Vol. 8, No. 5, 2015, pp. 337-342.

      [27] K. Sato, H. Sato, S. Matsuoka, A model-based algorithm for optimizing I/O intensive applications in clouds using VM-based migration, In Proceedings of the 9th IEEE/ACM Conference on Cluster Computing and the Grid, 2014, pp. 466-471.

      [28] L. Yu, L. Chen, Z. Cai, H. Shen, Y. Liang, Y. Pan, Stochastic load balancing for virtual resource management in datacenters, IEEE Transactions on Cloud Computing, Vol. PP, No. 99, 2016, pp. 1-1.

      [29] M. Jaiganesh, A. Vincent Antony Kumar, B3: Fuzzy-based data center load optimization in cloud computing, Mathematical Problems in Engineering, Vol. 2013, Article ID 612182, pp. 1-11.

  • Downloads

  • How to Cite

    E. Jayanthi, V., Divya, R., & Jagannath, M. (2018). A Review on Optimization Approaches in Cloud Computing Service. International Journal of Engineering & Technology, 7(3.24), 517-521. https://doi.org/10.14419/ijet.v7i3.24.22804