Load Balancing of Heuristic Algorithms on the basis of Performance Metrics in Cloud Computing

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-3
Year of Publication : 2019
Authors : Shah Faisal, Jiong Yu
  10.14445/22312803/IJCTT-V67I3P109

MLA

MLA Style: Shah Faisal, Jiong Yu, "Load Balancing of Heuristic Algorithms on the basis of Performance Metrics in Cloud Computing" International Journal of Engineering Trends and Technology 67.3 (2019): 33-40.

APA Style: Shah Faisal, Jiong Yu, (2019). Load Balancing of Heuristic Algorithms on the basis of Performance Metrics in Cloud Computing. International Journal of Engineering Trends and Technology, 67(3), 33-40.

Abstract
Allocation of user task or scheduling of users requests to cloud system is critical job. Overloading and under loading is a phenomenon which take birth when user requests (Task) are assigned to cloud system. System failure, increasing of execution time for task and more energy consumption occur because of overloading and under-loading of cloud infrastructure. On virtual machines these load balancing are main characteristic of task scheduling. Network load, memory load and computational load are types of load in cloud network. Detecting of overloading and under-loading over cloud system and then balancing of load over cloud system are the first priority of load balancing mechanism. “As pay-as-you need basis of client” cloud computing providers insure their clients to fulfill their requirements (request, demands of services). For minimizing the execution time of user request and minimizing of power consumption, efficient load balancing algorithms are necessary. Different types of load balancing algorithms was introduced for effective performance by researchers. In this paper we analyzed different performance parameters like Makespan and energy consumptions in multiple load balancing algorithms. For the brief results, CloudSim simulators were used to analyze heuristic-based algorithms performance.

Reference
[1] Wang, R., and Rao, B. (2015) ’Research on the Cloud Computing Load Balance Degree of Priority Scheduling Algorithm based on Convex Optimization Theory’, pp. 156–160.
[2] Chaisiri, S., Lee, B. S., and Niyato, D. (2012) ’Optimization of resource provisioning cost in cloud computing’, IEEE Transactions on Services Computing, 5(2), pp. 164–177.
[3] Dam, S., Mandal, G., Dasgupta, K., and Dutta, P. (2015, February) ’Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing’, Third IEEE International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–7.
[4] Wang, R., and Rao, B. (2015) ’Research on the Cloud Computing Load Balance Degree of Priority Scheduling Algorithm based on Convex Optimization Theory’, pp. 156–160.
[5] Jebalia, M., Ben Letafa, A., Hamdi, M., and Tabbane, S. (2015) ’An overview on coalitional game-theoretic approaches for resource allocation in cloud computing architectures’, International Journal of Cloud Computing, 22, 4(1), pp. 63-77.
[6] Singh, A., Juneja, D., and Malhotra, M. (2015) ’Autonomous agent based load balancing algorithm in cloud computing’, Procedia Computer Science, 45, pp. 832–841.
[7] Patni, J. C., Aswal, M. S., Agarwal, A., and Rastogi, P. (2015) ’A Dynamic and Optimal Approach of Load Balancing in Heterogeneous Grid Computing Environment’, In Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India CSI, Springer, Vol. 2, pp. 447–455.
[8] http://blog.nexright.com/?cat=6―Cloud Computing « Nexright Blog‖.
[9] A.Sidhu, S. Kinger, ―Analysis of load balancing techniques in cloud computing‖, INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4 (2) (2013) pages737–741.
[10] Chaudhari, Anand and Kapadia, Anushka, ― Load Balancing Algorithm for Azure Virtualization with Specialized VM‖, 2013,algorithms,vol 1,pages 2, Chaudhari.
[11] http://letslearncloud.wordpress.com/―Cloud Computing | Learn Cloud and its tips."
[12] Sahu, Yatendra and Pateriya, RK, ―Cloud Computing Overview with Load Balancing Techniques‖, International Journal of Computer Applications, 2013,vol. 65, Sahu2013.
[13] Liang, B., Yanli, H., Songyang, L., Weiming, Z., ―Task Scheduling with Load Balancing using Multiple Ant Colonies Optimization in Grid Computing‖ in 2010 Sixth International Conference on Natural Computation (ICNC 2010), DIO: 10.1109/ICNC.2010.5582599, pp.2715-2719, 2010.
[14] U.Bhoi, P. N. Ramanuj, ― Enhanced max-min task scheduling algorithm in cloud computing‖, International Journal of Application or Innovation in Engineering and Management (IJAIEM), ISSN,2013,pages 2319—4847.
[15] S.Kabiraj, V. Topka, and R. C. Walke, ―Going Green: A Holistic Approach to Transform Business‖, International Journal of Managing Information Technology (IJMIT), Vol. 2, No. 3, August 2010, pages 22-31.
[16] S.Ray and A.D. Sarkar (2012, October). Execution Analysis of Load Balancing Algorithms in Cloud Computing Environment. International Journal on Cloud Computing: Services and Architecture (IJCCSA). 2(5), pp. 1-13
[17] Chaudhari, Anand and Kapadia, Anushka, ― Load Balancing Algorithm for Azure Virtualization with Specialized VM‖, 2013,algorithms,vol 1,pages 2, Chaudhari
[18] Rajwinder Kaur1 and Pawan Luthra2 ―Load Balancing in Cloud Computing‖ , 2014, vol 5, pages 8.
[19] A.M.Nakai, E. Madeira, L. E. Buzato,―Load Balancing for Internet Cloud Services Using Limited Redirection Rates‖, 5th IEEE Latin-American Symposium on Dependable Computing (LADC), pp. 156-165, 2011.
[20] Y.Lua, Q. Xiea, G. Kliotb, A. Gellerb, J. R. Larusb, A. Greenber,―Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services‖, An international Journal on Performance evaluation, In Press, Accepted Manuscript, Available online 3 August 2011.
[21] J.Hu, J. Gu, G. Sun, T. Zhao,―A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment‖, Third International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), pp. 89-96, 2010.
[22] Manasa, H.B. & Basu, A., 2013. Energy Aware Resource Allocation in Cloud Data center. , International Journal of Engineering and Advanced Technology (IJEAT), 2(5), pp.277–281.
[23] Calheiros, R.N., Ranjan, R., De Rose, C.A.F., Buyya, R., ― CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services‖ in Technical Report, GRIDS-TR-2009-1, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, 2009.
[24] SaeedParsa and Reza Entezari-Maleki , "RASA: A New Grid Task Scheduling Algorithm", International Journal of Digital Content Technology and its Applications,Vol. 3, pp. 91-99, 2009
[25] Aissi H, Bazgan C, Vanderpooten D. Complexity of the min–max and min–max regret assignment problems. Operations research letters. 2005; 33(6):634–40.
[26] Braun TD, Siegel HJ, Beck N, Bo¨lo¨ni LL, Maheswaran M, Reuther AI, et al. A comparison of eleven static heuristics formapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed computing. 2001; 61(6):810–37
[27] Ibarra OH, Kim CE. Heuristic algorithms for scheduling independent tasks on nonidentical processors. Journal of the ACM (JACM). 1977; 24(2):280–9.
[28] Wu M-Y, Shu W, Zhang H. Segmented min-min: A static mapping algorithm for meta-tasks on heterogeneous computing systems. hcw; 2000: IEEE
[29] He X, Sun X, Von Laszewski G. QoS guided min-min heuristic for grid task scheduling. Journal of Computer Science and Technology. 2003; 18(4):442–51.
[30] Maheswaran M, Ali S, Siegel HJ, Hensgen D, Freund RF. Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. Journal of parallel and distributed computing. 1999; 59 (2):107–31 [31] L.K.Wiseling lazarus, Dr.P.Venkadesh, J.P.Jayan "Joint Power Allocation and Beam Forming for Energy-Efficient Multi-Way Multi-Antenna Relay Networks". International Journal of Computer Trends and Technology (IJCTT) V60(2):111-114 June 2018.

Keywords
cloud computing, load balancing, Makespan, Virtual machine, Energy consumption.