Workflow Scheduling in Cloud Using Nature Inspired Optimization Algorithms

Amit Chhabra


Cloud computing is one of the fastest growing technologies in the world. In the cloud computing scenario, selecting and assigning resource from a large pool of resources to workflow tasks is difficult and known NP-hard problem. Metaheuristics are used to provide near optimal solution to resource assignment problem in cloud and distributed computing systems. In this paper we have presented a survey of variety of meta-heuristic approaches such as genetic algorithm, particle swarm optimization, ant colony optimization, Cat Swarm, Bat, firefly and Grey Wolf Optimizer algorithms which are used for workflow scheduling in cloud environment.


Meta-heuristics, Cat Swarm, Firefly Algorithm, Bat Algorithm and Grey Wolf algorithm

Full Text:



“Taxamony on workflow management systems for grid computing” by R. Buya and J.Yu.

“Effective and fast task scheduling in heterogeneous system” by A. Gemund, A. Redulescu in HCS 2000.

“Critical-Path dynamic scheduling: An effective way to allocate task graphs to multiprocessor” by I. Ahmad, Y. Kwok in IEEE may 1996.

“NIST definition of cloud computing(draft)” by Grance, peter and mell in special NIST publication 2011.

“Workflow scheduling technique to consider task processing rate in spot-instance based cloud” by H.Yu, j.lim in Frontier and innovation in computing and communication.

“Metaheuristics: From design to implementation” by El Ghazalitalbi, 2009.

“Computational intelligence based on the behavior of cats” by Pei-Wei, Chu. International journal of innovative computing, information and control, February 2007.

“Cat swarm optimization” by S.C.Chu, P.W.Tsai and J.S.Pan in Springer 2006.

“Workflow scheduling in cloud computing using cat swarm optimization” by Bilgyan, Santwana in IEEE 2014.

“Survey and future trend of study on multi-objective scheduling” by Wang, Gao and Zangin 4th IEEE conference 2008.

“Solving multi-objective problems using cat swarm optimization” by P. M. Panda, Pardhan in expert system application 2011.

“Multi-objective CSO algorithm for workflow scheduling in cloud enviroment” by Bilgyan, Santwana, das in 2015 springer.

“New metaheuristic bat-inspired algorithm” by X.S Yang in NICSO springer 2010.

“BBA- binary bat algorithm for feature selection” by Nakamura, Costa, Pereira in 25th conference SIBGRAPI 2012.

“Bat algorithm for scheduling workflow applications in cloud” by S Raghavan, Marimuthu C, Sarwesh P in EDCAV 2015.

“Budget-Constrained Time and Reliability Optimization BAT Algorithm for Scheduling Workflow Applications in Clouds” by Navneet Kaur, Sarbjeet Singh in EUSPN 2016.

“Scheduling in Cloud Computing Environment using Firefly Algorithm” by SundarRajan, V. Vasudevan, S. Mithyain ICEEOT, 2016.

“Firefly algorithm for noisy non-linear optimization problem” by P.Aungkulanon, N.Chai.ead in national inter-conference of engineers and computer scientists, 2011.

“Evolutionary multi-objective optimization tutorial” by Bleuler, Zitler in 2004 springer.

“Optimal scheduling workflows in cloud computing environment using pareto-based grey wolf optimizer ” by Azadekhalili, Seyedbabamir in 2016 wiley.

“A survey on deadline constrained workflow scheduling algorithms in cloud environment” by Nallakumar R. arXìv preprint arXìv:1409, 7916; 2014.

“Adaptive workflow scheduling for dynamic grid and cloud computing environment” by Rahman M, et al. Concurr Comput: PractExp 2013;25(13):1816-42.

“Cost minimized PSO based workflow scheduling plan for cloud computing” by Verma A, Kaushal S; 2015.

“Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud” by Verma A, Kaushal S. In: Proceedings of the 2014 Recent Advances in Engineering and Computational Sciences (RAECS); 2014. IEEE.

“A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments” by Pandey S, et al. In: Proceedings of the 2010 24th IEEE International Conference on advanced information networking and applications (AINA) 2010. IEEE.

“A tunable workflow scheduling algorithm based on particle swarm optimization for cloud computing” by Huang J, et al. Criterion 2013;12:14.

“A set-based dìscrete PSO for cloud workflow scheduling with user-defined QoS constraints” by Chen W-N, Zhang J. In: Proceedings of the 2012 IEEE international conference on systems, man, and cybernetics (SMC).

“An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm” by Jianfang C, Junjie C, Qingshan Z. Cybern Inform Technol 2014;14 (1 ): 25—39.

“Local minima jump PSO for workflow scheduling in cloud computing environments” by Chitra S, et al. In: Advances in computer science and its applications; 2014, Springer. p. 1225—1234.

“Comparison of genetic algorithm and simulated annealing technique for optimal path selection in network routing” by Nair T, Sooda K. arXiv preprint arXiv:1001, 3920; 2010.

“Comparison of metaheuristic algorithms for solving machining optimization problems” by Madic M, Markovic D, Radovanovic M. FactaUniv Series: MechEng 2013;11(1):29–44.

“A bi-objective workflow application scheduling in cloud computing systems” by Aryan Y, Delavar AG.

“A learning architecture for scheduling workflow applications in the cloud” by Barrett E, Howley E, Duggan J. In: Proceedings of the 2011 ninth IEEE European conference on web services (ECOWS); 2011. IEEE.

“Budget constrained, priority based genetic algorithm for workflow scheduling in cloud” by Verma A, Kaushal S. In Proceedings of the Fifth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2013); 2013.

“Ant colony optimization” by Yaseen SG, Al-Slamy NM. IJCSNS 2008;8(6):351.

“Deadline and cost based ant colony optimization algorithm for scheduling workflow applications in hybrid cloud” by Singh L, Singh S.

“Score based deadline constrained workflow scheduling algorithm for Cloud systems” by Singh R, Singh S. Int J Cloud Comput: Sen’ Archit 2013;3(6):31—41.

“Scheduling workflow in cloud computing based on ant colony optimization algorithm” by Zhou Y, Huang X. In: 2013 sixth international conference on proceedings of the business intelligence and financial engineering (BIFE); 2013. IEEE.

“A multiple pheromone algorithm for cloud scheduling with various QQS requirements” by Gogulan R, Kavitha A, Karthick Kumar U. IntJ ComputSci 2012;9:3.



  • There are currently no refbacks.

Copyright (c) 2017 International Journal of Advanced Research in Computer Science