Improving the load balancing in Cloud computing using a rapid SFL algorithm (R-SFLA)
Subject Areas : ICTKiomars Salimi 1 , Mahdi Mollamotalebi 2 *
1 - Department of Computer, Buinzahra branch, Islamic Azad University, Buinzahra, Iran
2 - Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Keywords: Cloud computing, Load balancing, Rapid shuffled frog leaping, Resource scheduling, Response time,
Abstract :
Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of the load distribution among the processing nodes in order to have the best usage of resources while having minimum response time for the users’ requests. Several methods for load balancing in Cloud computing have been proposed in the literature. The shuffled frog leaping algorithm for load balancing is a dynamic, evolutionary, and inspired by nature. This paper proposed a modified rapid shuffled frog leaping algorithm (R-SFLA) that converge the defective evolution of frogs rapidly. In order to evaluate the performance of R-SFLA, it is compared to Shuffled Frog Leaping Algorithm (SFLA) and Augmented Shuffled Frog Leaping Algorithm (ASFLA) by the overall execution cost, Makespan, response time, and degree of imbalance. The simulation is performed in CloudSim, and the results obtained from the experiments indicated that the proposed algorithm acts more efficient compared to other methods based on the above mentioned factors.
[1] Mell. P , T. Grance.2011.The Nist Definition Of Cloud Computing. Pp.1-2-3
[2] Muzaffar Eusuff , Kevin Lansey & Fayzul Pasha (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization, Engineering Optimization, 38:2.
[3] Parmeet Kaur, S. M. (2017). Resource Provisioning and Work flow Scheduling in Clouds using Augmented Shuffled. Journal of Parallel and Distributed Computing- Elsevier.
[4] Mina Nabi, M. T. 2015. Availability In The Cloud: State Of The Art. Journal Of Network And Computer Applications.
[5] Klaithem Al Nuaimi, N. M. J. 2012. A Survey Of Load Balancing In Cloud Computing: Challenges And Algorithms. IEEE Second Symposium On Network Cloud Computing And Applications .p138
[6] N¨Ageli, T. R.-H. 2002. Heterogeneous Dynamic Load Balancing. Springer.
[7] Jaiswal, R. M. 2012. Ant Colony Optimization: A Solution of Load Balancing In Cloud. International Journal of Web & Semantic Technology.
[8] Shang-Liang Chen, Y.-Y. C.-H. 2016. Clb: A Novel Load Balancing Architecture and Algorithm for Cloud Services. Computers and Electrical Engineering.p2.
[9] Jagadev, B. S. 2018. Cloud Computing For Optimization: Foundations, Applications, and Challenges. Springer.
[10] Einollah Jafarnejad Ghomi, A. M. 2017. Load-Balancing Algorithms in Cloud Computing: A. Yjnca, 62-63.
[11] Shah, S. A. 2015. Load Balancing Algorithms In Cloud Computing: A Survey Of Modern Techniques. National Software Engineering ConferenceIEEE.p31.
[12] Minxian Xu, W. T. 2017. A Survey On Load Balancing Algorithms For Virtual Machines Placement In Cloud Computing. Wiley.p6
[13] Geethu Gopinath P P1, S. K. 2015. An In-Depth Analysis and Study of Load Balancing Techniques in the Cloud Computing Environment. Procedia Computer Science-Elsevier, 428
[14] Kumar, D. V. 2015. An Assessment On Various Load Balancing Techniques In Cloud Computing. Ijaict.
[15] Violetta N. Volkova1, L. V. 2018. Load Balancing In Cloud Computing. IEEE.p378.
[16] Nguyen Khac Chien, N. H. 2016. Load Balancing Algorithm Based On Estimating Finish Time Of Services In Cloud Computing.18Th International Conference on Advanced Communication Technology Icact. IEEE
[17] Singh, S. B. 2014. A Survey On Scheduling And Load Balancing Techniques In Cloud Computing Environment. 5Th International Conference on Computer and Communication Technology Iccct IEEE.p89.
[18] Samuel, K. R. 2016. Enhanced Bee Colony Algorithm For Efficient Load Balancing And Scheduling In Cloud. Springer International Publishing Switzerland.p67-77
[19] Alireza Sadeghi Milani, N. J. 2016. Load Balancing Mechanisms and Techniques in the Cloud Environments: Systematic Literature Review and Future Trends. Journal of Network and Computer Applications, 9.
[20] Rami N. Khushaba, A. A.-A.-J. 2008. A Combined Ant Colony and Differential Evolution Feature Selection Algorithm. Springer-Verlag Berlin Heidelbergp2-11.
[21] Hussain, F. R. 2013. Task-Based System Load Balancing In Cloudcomputing Using Particle Swarm Optimization. Springer, Non Page.
[22] Kushwah, S. S. 2016. A Genetic Based Improved Load Balanced Min-Min Task Scheduling Algorithm For Load Balancing In Cloud Computing. 8Th International Conference on Computational Intelligence and Communication Networks .p678-679.
[23] Albert Y. Zomaya, S. M.-H. 2001. Observations on Using Genetic Algorithms for Dynamic Load-Balancing. IEEE Transactions on Parallel and Distributed Systems, 900.
[24] Mohammed Abdullahi, M. A. 2014. Symbiotic Organism Search Optimization Based Task Scheduling In Cloud Computing Environment. Future Generation Computer Systems.p4-3.
[25] http://www.cs.huji.ac.il/labs/parallel/workload/l_lcg/index.html
[26] Babak Amiri & Mohammad Fathian & Ali Maroosi. (2007). Application of shuffled frog-leaping algorithm on clustering. The International Journal of Advanced Manufacturing Technology, 199-208.
[27] Emad Elbeltagiy, T. H. (2007). A modified shuffled frog-leaping optimization algorithm: applications to project management. Structure and Infrastructure Engineering, 53-60.
[28] S. Sarathambekai, K. U.-G. (2015). Shuffled Frog Leaping Algorithm in Distributed System. International Conference on Innovations in Computing Techniques (ICICT 2015). International Journal of Computer Applications.