Abstract
Nowadays, cloud computing as a developing Internet accommodation concept has been propagating to provide different Internet resources to users. Cloud computing occupies a variety of computing Internet applications for facilitating the execution of sizable voluminous-scale tasks. Cloud computing is a web predicated distributed computing. There is more than a million number of servers connected to the Internet to provide several types of accommodations to provide cloud users. Constrained numbers of servers execute fewer numbers tasks at a time. So it is not too easy to execute all functions at a time. Some systems run all functions, so there are needed to balance all loads. Load balance reduces the completion time as well as performs all tasks in a particular way. There are not possible to remain an equal number of servers to execute equal tasks. Tasks to be executed in cloud computing would be less than the connected servers sometime. Excess servers have to execute a fewer number of tasks. Here, we are going to present an algorithm for load balancing and performance with minimization completion time and throughput. We apply here a very famous Hungarian method to balance all loads in distributing computing. Hungarian Technique helps us to minimize the cost matrix problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
B. Hayes, Cloud computing. Commun. ACM 51(7), 9–11 (2008)
S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, A. Ghalsasi, Cloud computing—the business perspective. Decis. Support Syst. 51(1), 176–189 (2011)
B.P. Rimal, A. Jukan, D. Katsaros, Y. Goeleven, Architectural requirements for cloud computing systems: an enterprise cloud approach. J. Grid Comput. 9(1), 3–26 (2011)
Y. Fang, F. Wang, J. Ge, A task scheduling algorithm based on load balancing in cloud computing, in International Conference on Web Information Systems and Mining (Springer, Berlin, Heidelberg, 2010), pp. 271–277
T. Wang, Z. Liu, Y. Chen, Y. Xu, X. Dai, Load balancing task scheduling based on genetic algorithm in cloud computing, in 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing (DASC) (IEEE, 2014), pp. 146–152
A.Y. Zomaya, T. Yee-Hwei, Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans. Parallel Distrib. Syst. 12(9), 899–911 (2001)
C. Zhao, S. Zhang, Q. Liu, J. Xie, J. Hu, Independent tasks scheduling based on genetic algorithm in cloud computing, in 5th International Conference on Wireless Communications, Networking, and Mobile Computing (2009), pp. 1–4
E. Juhnke, T. Dörnemann, D. Böck, B. Freisleben, Multi-objective scheduling of BPEL workflows in geographically distributed clouds, in 4th IEEE International Conference on Cloud Computing (2011), pp. 412–419
F. Ramezani, J. Lu, F.K. Hussain, Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Program. 42(5), 739–754 (2014)
Y. Wang, J. Li, H.H. Wang, Cluster and cloud computing framework for scientific metrology in flow control. Cluster Comput. 1–10 (2017)
A. Shawish, M. Salama, Cloud computing: paradigms and technologies,in Inter-cooperative Collective Intelligence: Techniques and Applications (Springer, Berlin, Heidelberg, 2014), pp. 39–67
A. Shawish, M. Salama, Cloud computing: paradigms and technologies, in Inter-cooperative Collective Intelligence: Techniques and Applications (Springer, Berlin, Heidelberg, 2014), pp. 39–67
Y. Wang, J. Li, H.H. Wang, Cluster and cloud computing framework for scientific metrology in flow control. Cluster Comput. 1–10 (2017)
M. Armbrust, A. Fox, R. Griffith, A.D. Joseph, R.H. Katz, A. Konwinski, G. Lee, D.A. Patterson, A. Rabkin, I. Stoica, et al., Above the clouds: a Berkeley view of cloud computing (2009)
L. Zhao, S. Sakr, A. Liu, A. Bouguettaya, Cloud Data Management (Springer, 2014)
J. Hu, J. Gu, G. Sun, T. Zhao, A scheduling strategy on load balancing of virtual machine resources in cloud computing environment, in 2010 3rd International Symposium on Parallel Architectures, Algorithms, and Programming (IEEE, 2010), pp. 89–96
W.T. Wen, C.D. Wang, D.S. Wu, Y.Y. Xie, An aco-based scheduling strategy on load balancing in cloud computing environment, in 2015 Ninth International Conference on Frontier of Computer Science and Technology (IEEE, 2015), pp. 364–369
G. Roos, Enterprise prefer private cloud: Survey 2013, http://www.eweek.com/cloud/enterprises-prefer-private-clouds-survey/
A. Li, X. Yang, S. Kandula, M. Zhang, Cloudcmp: comparing public cloud providers, in Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement (ACM, 2010), pp. 1–14
Q. Zhang, L. Cheng, R. Boutaba, Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)
D. Petcu, Multi-cloud: expectations and current approaches, in Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds (ACM, 2013), pp. 1–6
M. Xu, W. Tian, R. Buyya, A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr. Comput. Pract. Exp. 29(12) (2017)
S. Song, T. Lv, X. Chen, Load balancing for future internet: an approach based on game theory. J. Appl. Math. (2014)
A.A. Neghabi, N.J. Navimipour, M. Hosseinzadeh, A. Rezaee, Load balancing mechanisms in the software defined networks: a systematic and comprehensive review of the literature. IEEE Access 6, 14159–14178 (2018)
H. Mehta, P. Kanungo, M. Chandwani, Decentralized content aware load balancing algorithm for distributed computing environments, in Proceedings of the International Conference & Workshop on Emerging Trends in Technology (ACM, 2011), pp. 370–375
A. B. Singh, S. Bhat, R. Raju, R. D’Souza, Survey on various load balancing techniques in cloud computing. Adv. Comput. 7(2), 28–34 (2017)
K. Al Nuaimi, N. Mohamed, M. Al Nuaimi, J. Al-Jaroodi, A survey of load balancing in cloud computing: Challenges and algorithms, in 2012 Second Symposium on Network Cloud Computing and Applications (NCCA) (IEEE, 2012), pp. 137–142
Y.-T. Wang, Load sharing in distributed systems. IEEE Trans. Comput. 100(3), 204–217 (1985)
M. Antoine, L. Pellegrino, F. Huet, F. Baude, A generic API for load balancing in distributed systems for big data management. Concurr. Comput. Pract. Exp. 28(8), 2440–2456 (2016)
D. Grosu, A.T. Chronopoulos, M.-Y. Leung, Load balancing in distributed systems: an approach using cooperative games, in Parallel and Distributed Processing Symposium., Proceedings International, IPDPS 2002, Abstracts and CD-ROM (IEEE, 2001), pp. 10
Robert Fox, Library in the clouds. OCLC Syst. Serv. Int. Digital Library Persp. 25(3), 156–161 (2009)
S.-C. Wang, K.-Q. Yan, W.-P. Liao, S.-S. Wang, Towards a load balancing in a three-level cloud computing network, in 2010 3rd IEEE International Conference on Computer Science and information technology (ICCSIT), vol. 1 (IEEE, 2010), pp. 108–113
G. Ritchie, J. Levine, A fast, effective local search for scheduling independent jobs in heterogeneous computing environments. J. Comput. Appl. 25, 1190–1192 (2005)
T.D. Braun, H.J. Siegel, N. Beck, L.L. Bölöni, M. Maheswaran, A.I. Reuther, J.P. Robertson, M.D. Theys, B. Yao, D. Hensgen, R.F. Freund, A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61, 810–837 (2001)
S.C. Wang, K.Q. Yan, W.P. Liao, S.S. Wang, Towards a load balancing in a three-level cloud computing network, in CSIT (2010), pp. 108–113
C.-L. Hung, H.-H. Wang, Y.-C. Hu, Efficient load balancing algorithm for cloud computing network, in International Conference on Information Science and Technology (IST 2012), April 2012, pp. 28–30
H.W. Kuhn, The Hungarian method for the assignment problem. Naval Res. Logist. (NRL) 2(1–2), 83–97 (1955)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Mondal, R.K., Ray, P., Nandi, E., Biswas, B., Sanyal, M.K., Sarddar, D. (2018). Load Balancing of Unbalanced Matrix Problem of the Sufficient Machines with Min-Min Algorithm. In: Mandal, J., Mukhopadhyay, S., Dutta, P., Dasgupta, K. (eds) Methodologies and Application Issues of Contemporary Computing Framework. Springer, Singapore. https://doi.org/10.1007/978-981-13-2345-4_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-2345-4_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2344-7
Online ISBN: 978-981-13-2345-4
eBook Packages: Computer ScienceComputer Science (R0)