Hybrid Artificial Bee Colony and Tabu Search Based Power Aware Scheduling for Cloud Computing

Full Text (PDF, 827KB), PP.39-47

Views: 0 Downloads: 0


Priya sharma 1,* Kiranbir Kaur 1

1. Department of Computer Science & Engineering, Guru Nanak Dev. University, Amritsar, Punjab

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.07.04

Received: 21 Jun. 2017 / Revised: 5 Aug. 2017 / Accepted: 15 Sep. 2017 / Published: 8 Jul. 2018

Index Terms

Cloud computing, load balancing, artificial bee colony, tabu search


Load balancing is an important task on virtual machines (VMs) and also an essential aspect of task scheduling in clouds. When some Virtual machines are overloaded with tasks and other virtual machines are under loaded, the load needs to be balanced to accomplish optimum machine utilization. This paper represents an existing technique “artificial bee colony algorithm” which shows a low convergence rate to the global minimum even at high numbers of dimensions. The objective of this paper is to propose the integration of artificial bee colony with tabu search technique for cloud computing environment to enhance energy consumption rate. The main improvement is makespan 28.4 which aim to attain a well balanced load across virtual machines. The simulation result shows that the proposed algorithm is beneficial when compared with existing algorithms.

Cite This Paper

Priya sharma, Kiranbir kaur, "Hybrid Artificial Bee Colony and Tabu Search Based Power Aware Scheduling for Cloud Computing", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.7, pp.39-47, 2018. DOI:10.5815/ijisa.2018.07.04


[1]Ajit, M., and G. Vidya, “VM level load balancing in cloud environment,” Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on. IEEE, 2013.
[2]Al-maamari, Ali, and Fatma A. Omara, “Task scheduling using PSO algorithm in cloud computing environments,” International Journal of Grid and Distributed Computing, vol: 8, issue: 5, pp: 245-256, 2015.
[3]Awad, A. I., N. A. El-Hefnawy, and H. M. Abdel_kader, “Enhanced particle swarm optimization for task scheduling in cloud computing environments,” International Conference on Communication, Management and Information Technology, vol: 65, pp: 920-929,2015.
[4]Babu, K. R., P. Mathiyalagan, and S. N. Sivanandam, “Pareto based hybrid Meta heuristic ABC–ACO approach for task scheduling in computational grids,” International Journal of Hybrid Intelligent Systems, vol: 11, issue: 4, pp:241-255, 2014.
[5]Dasgupta, Kousik, et al, “A genetic algorithm based load balancing strategy for cloud computing,” International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), vol: 10, pp: 340-347, 2013.
[6]Domanal, Shridhar G., and G. Ram Mohana Reddy, “Optimal load balancing in cloud computing by efficient utilization of virtual machines,” Communication Systems and Networks (COMSNETS), Sixth International Conference on. IEEE, 201).
[7]Effatparvar, M., and M. S. Garshasbi, “A genetic algorithm for static load balancing in parallel heterogeneous systems,” International Conference on Innovation, Management and Technology Research, pp: 358-364, 2014.
[8]Ge, Fatemeh Rastkhadiv and Kamran Zamanifar, “A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing,” International Journal of Advanced Biotechnology and Research, Vol: 7, Issue: 5, pp: 1058-1069,2016.
[9]Jena, R. K, “Multi objective task scheduling in cloud environment using nested PSO framework,” Procedia Computer Science 57, pp: 1219-1227, (2015).
[10]Karaboga, Dervis, and Bahriye Akay, “A comparative study of artificial bee colony algorithm,” Applied mathematics and computation, vol: 214, issue: 1, pp: 108-132, 2009.
[11]Karaboga, Dervis, and Bahriye Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of global optimization, vol: 39, issue: 3, pp: 459-471, 2007.
[12]K.R. Remesh Babu and Philip Samue, “Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud,” Innovations in Bio-Inspired Computing and Applications. Springer International Publishing, pp.:67-78, 2016.
[13]LD, Dhinesh Babu, and P. Venkata Krishna, “Honey bee behavior inspired load balancing of tasks in cloud computing environments,” Applied Soft Computing, vol: 13, issue:5, pp: 2292-2303, 2013.
[14]Liu, Yu, et al, “DeMS: a hybrid Alla, Hicham Ben, et al, “An Efficient Dynamic Priority-Queue Algorithm Based on AHP and PSO for Task Scheduling in Cloud Computing,” Springer International Publishing AG International Conference on Hybrid Intelligent Systems Springer, Cham, pp: 134-143, 2016.
[15]Masdari, Mohammad, et al, “A Survey of PSO-Based Scheduling Algorithms in Cloud Computing,” Journal of Network and Systems Management, pp: 1-37, 2016.
[16]Masdari, Mohammad, et al, “Towards workflow scheduling in cloud computing: A comprehensive analysis,” Journal of Network and Computer Applications, vol: 66, pp: 64-82, 2016.
[17]Science, C, & Engineering, S, “Differential Evolution Based Optimal Task Scheduling in Cloud Computing,” International Journal of Advanced Research in Computer Science and Software Engineering, vol: 6, issue: 6, pp:340–347, 2016.
[18]Shaw, Subhadra Bose, and A. K. Singh, “A survey on scheduling and load balancing techniques in cloud computing environment,” Computer and Communication Technology (ICCCT), 2014 International Conference on. IEEE, pp: 87-95, 2014.
[19]Zuo, Xingquan, Guoxiang Zhang, and Wei Tan, “Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud,” IEEE Transactions on Automation Science and Engineering, vol: 11, issue: 2, pp: 564-573, 2014.