Cloud computing refers to many different types of services and applications being delivered over the internet cloud. Cloud load balancing is the process of distributing workloads across multiple computing resources. Load balancing is an optimization problem and goal of any optimization is to either minimize effort or to maximize benefit. The effort or the benefit can be usually expressed as a function of certain design variables. Hence, optimization is the process of finding the conditions that give the maximum or the minimum value of a function. Load balancing is a problem where you try to minimize value of parameters like Makespan time, Response Time, etc. and increase the utilization of cloud resources. Metaheuristic algorithms are a natural solution to the problem of load balancing in cloud. But these algorithms as such do not provide a complete solution. This paper proposes a hybrid of Particle Swarm optimization and Ant Colony optimization for load balancing of tasks on cloud resources.

ACO, PSO, VM, SJF, IAAS, PAAS, SAAS, Data Centre, Cloud Computing, DI

[1] Karger D, Stein C, Wein J. Scheduling Algorithms. Algorithms and Theory of Computation Handbook: special topics and techniques. Chapman & Hall/CRC; 2010.
[2] Medhat Tawfeek, Ashraf El-Sisi, Arabi Keshk , Fawzy Torkey, “Cloud Task Scheduling Based on Ant Colony Optimization” , The International Arab Journal of Information Technology, Vol. 12, No. 2, March 2015.
[3] Jyoti Thaman, Manpreet Singh, “Current Perspective in Task Scheduling Techniques in cloud Computing: A Review”, International Journal in Foundations of Computer Science & Technology (IJFCST) Vol.6, No.1, January 2016.
[4] Mala Kalra, Sarbjeet singh, “a review of metaheuristic scheduling techniques in cloud computing”, available online 18 august 2015.
[5] Gurtej Singh, Amritpal kaur, “Bio Inspired Algorithms: An Efficient Approach for Resource Scheduling in Cloud Computing”, International Journal of Computer Applications (0975 – 8887) Volume 116 – No. 10, April 2015.
[6] Nazmul Siddique, Hojjat Adeli, “Nature Inspired Computing: An Overview and Some Future Directions”, Published online: 30 November 2015.
[7] Pratima Dhuldhule, J. Lakshmi, S. K. Nandy, “High Performance Computing Cloud - a Platform-as-a-Service Perspective” , 2015 International Conference on Cloud Computing and Big Data.
[8] Elaheh Hallaj, Elaheh Hallaj, “Study and Analysis of Task Scheduling Algorithms in Clouds Based on Artificial Bee Colony”, Second International Congress on Technology, Communication and Knowledge (ICTCK 2015) November, 11-12, 2015 - Mashhad Branch, Islamic Azad University, Mashhad, Iran.
[9] Aarti Singh, Dimple Juneja, Manisha Malhotra, “Autonomous Agent Based Load Balancing Algorithm in Cloud Computing”, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), pp. 832-841.
[10] F. Rothlauf, “Design of Modern Heuristics Principles and Application”, Springer. Verlag Berlin Heidelberg, pp. 07-36, 2011. ISBN 978-3-540-72961.
[11] Dorigo M, Stu¨ tzle T. Ant colony optimization. MIT Press; 2004.
[12] J. Kennedy and R. Eberhart. Particle swarm optimization In IEEE International Conference on Neural Networks, volume 4, pages 1942–1948, 1995.
[13] V. Kale, “Big Data Computing: A Guide for Business and Technology”, CRC Press, US, pp. 177-203, ISBN 9781498715331.