i-manager Publications

Survey on Cloud Computing Load Balancing

Chandu Vaidya *, Kalpana S. Bhure**

*-** Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering & Research, Nagpur, Maharashtra, India.

Periodicity:January - June'2020
DOI : https://doi.org/10.26634/jcc.7.1.17156

Abstract

A wise person is the basis of counseling just as cloud computing is a computer particle. Cloud computing is an unmistakable, limitless, unchanged, remarkable, upright, pervasive technology of the present period. This also pursues enormous capability to support all end users with fancy facilities. Cloud computing has multi-tenancy features, virtualization, load balancing technology, security, scheduling and much more. Cloud computing Virtualization (helping design of the cloud) enables users to access several virtual machines or their features, delivering pay-per-use services to those. Load Balancing is one of the big issues of cloud computing. Load balancing is the strategy used to divide the load between various computing elements that are available and to equalize load with virtual machines to achieve efficiency and improve the throughput. Cloud computing is a massive network and behaves heterogeneously. But it could be an incentive for inappropriate allocation of loads and unequal use of resources. A survey on cloud computing models, load balancing & scheduling process is carried out in this paper. By considering the different parameters of the resources, many authors have proposed the solution on load balancing problem. Here, we suggest a new load balancing method (Decider, Threshold based), taking into account various parameters such as RAM, CPU consumption, network traffic statistics. We also concentrate on scheduling at the local and global level for optimal use of computing elements and thus reduce the task's overall execution time. Based on comparative analysis we will assess the efficiency of algorithms / servers.

Keywords

Cloud Computing, Load Balancing, Scheduling Algorithms, VM, Statistics, Cloud Models, Deployments.

How to Cite this Article?

Vaidya, C., and Bhure, K. S. (2020). Survey on Cloud Computing Load Balancing. i-manager's Journal on Cloud Computing, 7(1), 32-46. https://doi.org/10.26634/jcc.7.1.17156

References

[1]. Baby, A. (2015). Improved honey bee inspired load balancing of tasks with position updation. International Journal of Research in Applied Science & Engineering Technology (IJRASET), 3(IV), 1157-1163.

[2]. Bhure, K., Titarmare, N. (2020). A study on:- cloud computing, virtual private cloud, load balancing. International Journal of Engineering and Creative Science, 3(2), 47-52.

[3]. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5^th utility. Future Generation Computer Systems, 25(6), 599-616. https://doi.org/10.1016/j.future.2008.12. 001

[4]. Chakraborty, S., & Khan, A. K. (2013). A Study of Load Distribution Algorithms In Distributed Scheduling. International Journal of Research in Engineering and Technology, 2(2), 37-40. https://doi.org/10.15623/ijret. 2013.0214007

[5]. Choudhary, A., & Rathi, R. (2015). An approach on dynamic semi-distributed load balancing algorithm for cloud computing system. International Journal of Scientific & Engineering Research, 6(6), 406-411.

[6]. Dasgupta, K., Mandal, B., Dutta, P., Mandal, J. K., & Dam, S. (2013). A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technology, 10, 340-347.

[7]. Deepa, T., & Cheelu, D. (2018). A Comparative study of static and dynamic load balancing algorithms in cloud computing. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017) (pp. 3375-3378).

[8]. Dhakal, S., Hayat, M. M., Pezoa, J. E., Yang, C., & Bader, D. A. (2007). Dynamic load balancing in distributed systems in the presence of delays: A regeneration-theory approach. IEEE transactions on parallel and distributed systems, 18(4), 485-497. https://doi.org/10.1109/TPDS. 2007.1009

[9]. Domanal, S.G., & Reddy, G.R. (2013). Load balancing in cloud computingusing modified throttled algorithm. 2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 1-5.

[10]. Fang, Y., Wang, F., & Ge, J. (2010, October). A task scheduling algorithm based on load balancing in cloud computing. In International Conference on Web Information Systems and Mining (pp. 271-277). Springer, Berlin, Heidelberg.

[11]. Feng, J., Liu, Z., Wu, C., & Ji, Y. (2018). Mobile edge computing for the Internet of vehicles: Offloading framework and job scheduling. IEEE Vehicular Technology Magazine, 14(1), 28-36.

[12]. Fiandrino, C., Allio, N., Kliazovich, D., Giaccone, P., & Bouvry, P. (2019). Profiling performance of application partitioning for wearable devices in mobile cloud and fog computing. IEEE Access, 7, 12156-12166.

[13]. Gawali, M. B., & Shinde, S. K. (2018). Task scheduling and resource allocation in cloud computing using a heuristic approach. Journal of Cloud Computing, 7(1), 1- 16.

[14]. Hawilo, H., Jammal, M., & Shami, A. (2019). Network function virtualization-aware orchestrator for service function chaining placement in the cloud. IEEE Journal on Selected Areas in Communications, 37(3), 643-655.

[15]. Kapoor, S., & Dabas, C. (2015, August). Cluster based load balancing in cloud computing. In 2015, Eighth International Conference on Contemporary Computing (IC3) (pp. 76-81). IEEE.

[16]. Kashyap, D., & Viradiya, J. (2014). A survey of various load balancing algorithms in cloud computing. International Journal of Scientific & Technology Research, 3(11), 115-119.

[17]. Khandve, T., Talekar, M., & Dhiwar, S. (2015). Security and load balancing in cloud computing. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(10), 3871-3874.

[18]. Lakshmanan, G. T., Rabinovich, Y. G., & Schloss, R. J. (2013). U.S. Patent No. 8,479,216. Washington, DC: U.S Patent and Trademark Office.

[19]. Li, C., Zhou, X., Sun, M., Lu, K., Zhou, J., Zhuang, H., & Dai, D. (2014, December). DLBS: Decentralized load balancing scheme for event-driven cloud frameworks. In th 2014, 20 IEEE International Conference on Parallel and Distributed Systems (ICPADS) (pp. 853-858). IEEE.

[20]. Liu, H., Liu, S., Meng, X., Yang, C., & Zhang, Y. (2010, May). LBVS: A load balancing strategy for virtual storage. In 2010, International Conference on Service Sciences (pp. 257-262). IEEE.

[21]. Lu, Y., Xie, Q., Kliot, G., Geller, A., Larus, J. R., & Greenberg, A. (2011).Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services. Performance Evaluation, 68(11), 1056-1071. https://doi.org/10.1016/j.peva.2011.07.015

[22]. Meng, S., Wang, Y., Jiao, L., Miao, Z., & Sun, K. (2018). Hierarchical evolutionary game based dynamic cloudlet selection and bandwidth allocation for mobile cloud computing environment. IET Communications, 13(1), 16-25.

[23]. Mishra, R., & Jaiswal, A. (2012). Ant colony optimization: A solution of load balancing in cloud. International Journal of Web & Semantic Technology, 3(2), 33-50.

[24]. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.

[25]. Mohanty, R., Behera, H. S., Patwari, K., Dash, M., & Prasanna, M. L. (2011). Priority Based Dynamic Round Robin (PBDRR) algorithm with intelligent time slice for soft real time systems. International Journal of Advanced Computer Science and Applications (IJACSA), 2(2), 46-50.

[26]. Mondal, B., Dasgupta, K., & Dutta, P. (2012). Load balancing in cloud computing using stochastic hill climbing - a soft computing approach. Procedia Technology, 4, 783-789.

[27]. Ning, Z., Kong, X., Xia, F., Hou, W., & Wang, X. (2018). Green and sustainable cloud of things: Enabling collaborative edge computing. IEEE Communications Magazine, 57(1), 72-78.

[28]. Pasha, N., Agarwal, A., & Rastogi, R. (2014). Round robin approach for VM load balancing algorithm in cloud computing environment. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 34-39.

[29]. Paya, A., & Marinescu, D. C. (2015). Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Transactions on Cloud Computing, 5(1), 15-27.

[30]. Sharma, H., & Sekhon, G. S. (2017). Load balancing in cloud using enhanced genetic algorithm. International Journal of Innovations & Advancement in Computer Science IJIACS, 6(1), 13-19.

[31]. Vaidhya, C. (2016). An approach for processor utilization in master slave environment. In Proceedings of International Conference on Advanced Material Technologies (ICAMT).

[32]. Vaidya, C. D., & Chandak, M. B. (2012, November). Efficient parallel process migration algorithm using statistical approach. In 2012 Fourth International Conference on Computational Intelligence and Communication Networks (pp. 525-529). IEEE.

[33]. Vaidya, C., Khobragade, P., & Golghate, A. (2016). Data Leakage Detection and Security in Cloud Computing. GRD Journals-Global Research Development Journal for Engineering, 1(12), 137-140.

[34]. Vaidya, C., Nampalliwar, A., Nampalliwar, K., Thakkar, R., & Bhagat, S. (2018). Statistical approach for load distribution in decentralized cloud computing, Helix, 8(5), 3884-3887. https://doi.org/10.29042/2018-3884- 3887

[35]. Vaidya, C., Saide, S., & Chadawar, S. (2016). Data leakage detection and dependable storage service. IJSTE International Journal of Science Technology & Engineering, 2 (10), 694-701.

[36]. Wang, C., Feng, C., & Cheng, J. (2017, January). Randomized load balancing with a helper. In 2017 International Conference on Computing, Networking and Communications (ICNC) (pp. 518-524). IEEE.

[37]. Xiao, Z., Song, W., & Chen, Q. (2012). Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1107-1117.

[38]. Zhang, Y., Chang, R., & Townend, P. (2019). Guest editor's introduction: special section on virtualization and services for cloud-based application systems. IEEE Transactions on Services Computing, 12(1), 88-90.

Survey on Cloud Computing Load Balancing

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25