Abstract
The Cloud computing is an emerging distributed systems which follows a “pay-as-you-use” model. It is a new type of shared infrastructure able to offer several resources through the Internet. There is large number of users using the services over the cloud, which generating large volume of data. The scheduling of dependent tasks is a NP-complete problem and has become as one of the most challenging problems in cloud environment. There is a need of specifying a sequence of execution of these tasks to satisfy the user requirements in terms of QoS parameters such as cost, execution time, etc. The workflow scheduling is considered to be difficult, when it becomes a multi-objective optimization problem. In this paper, we presented a comprehensive description of the existing approaches based on meta-heuristics for workflow scheduling. On the basis of the related works, we found the Genetic algorithm as the best method for scheduling. A GA searches the problem space globally and therefore, scholars have investigated combining GAs with other meta-heuristic methods to resolve the local search problem. We feel that there is a scope of using hybrid meta-heuristics approach that combines Artificial Bee Colony algorithm and Genetic Algorithm (ABC-GA) for scheduling workflows in Cloud computing. Cross-over and mutation operators of GA can be embedded into ABC to improve scheduling strategy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Grance T, Mell P. The NIST definition of cloud computing—recommendations of the National Institute of Standards and Technology. Special Publication 800-145, NIST, Gaithersburg; 2011.
Buyya RK, Kotagiri R, Yu J. Workflow scheduling algorithm for grid computing. In: Meta-heuristics for scheduling in distributed computing environment, vol. 146. Berlin Heidelberg: Springer; 2008. p. 173–214.
Barrionuevo JJD, Fard HM, Prodan R, Fahringer T. A multi-objective approach for workflow scheduling in heterogeneous environment: cluster, cloud and grid computing. In: 12th IEEE International conference; 2012. p. 300–309.
Hoheisel A, Prodan R, Wieczorek M. Taxonomies of the multi-criteria grid workflow scheduling problem. In: Grid middleware and service. Springer; 2008. p. 237–64.
Achalakul T, Udomkasemsub O, Li XO. A multiple-objective workflow scheduling framework for cloud data analytics. In: International Joint Conference; 2012. p. 391–398.
Bitam S. Bees life algorithm for job scheduling in cloud computing. In: Second international conference on communications and information technology; 2012.
Dhinesh Babu LD, Venkata KP. Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput. 2013; 2292–2303.
Sivanandam SN, Visalakshi P. Dynamic task scheduling with load balancing using hybrid PSO. Int J Open Problems Comput Math. 2009; 475–488.
Buyya RK, Guru SM, Pandey S, Wu L. A particle swarm optimization based heuristic for scheduling workflow applications in cloud computing environments. In: 24th IEEE international conference on advanced information networking and applications (AINA). 2010; 400–407.
Sultan EI. Quantum PSO technique for load balancing in cloud computing. PhD Thesis; 2013.
Jianfang C, Junjie C, Qingshan Z. An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm. Cybern Inform Technol. 2014; 25–39.
Buyya RK, Rodriguez MA. Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput. 2014; 222–235.
Channa I, Rajni: Bacterial foraging based hyper-heuristic for resource scheduling in grid computing. In: Future Generation Computer System; 2012. p. 751–762.
Dong Y, Li K, Wang D, Xu G, Zhao G. Cloud task scheduling based on load balancing ACO. In: Sixth annual chinagrid Conference; 2011. p. 3–9.
Shu W, Wang W, Wang Y. A novel energy efficient resource allocation algorithm based on Immune clonal optimization for green cloud computing. EURASIP J Wireless Commun Networking; 2014.
Cao J, Hwang K, Khan SU, Li K, Zhanga F. Multi-objective scheduling of many tasks in cloud platforms. In: Future generation computer systems; 2013.
Lin J, Lin X, Lin H, Zhong Y, Zeng Q. Hybrid ant colony algorithm clonal selection in the application of the cloud’s resource scheduling. In: Distributed parallel, and cluster computing (cs.DC). arXiv:1411.2528v1; 2014.
Liu J, Li B, Luo XG, Zhang XM, Zhang F. Job scheduling model for cloud computing based on multi-objective genetic algorithm. Int J Comput Sci. 2013; 134–9.
Abraham A, Amendola D, Cordeschi N, Javanmardi S, Liu H, Shojafar M. Hybrid job scheduling algorithm for cloud computing environment. In: Proceedings of the fifth international conference on innovations in bio-inspired computing and applications IBICA 2014, vol 303. Advances in Intelligent Systems and Computing; 2014. p. 43–52.
Buyya RK, Yu J. Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. In: Scientific Programming Journal IOS Press, Amsterdam; 2006. p. 217–230.
Chelouah R, Nacer MA, Sellami K, Tiako PF. Immune genetic algorithm for scheduling service workflows with QoS constraints in cloud computing. S Afr J Ind Eng. 2013;24:68–82.
Aryan Y, Delavar AG. HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. J Cluster Comput. Springer; 2013. p. 129–137.
Abraham A, Pooranian Z, Shojafar M, Singhal M, Tavoli R. A hybrid metaheuristics algorithm for job scheduling on computational grids. Informatica. 2013;37:157–64.
Fan Z, Li Y, Shen H, Wu Y. Simulated-annealing load balancing for resource allocation in cloud environments. In: International conference on parallel and distributed computing, applications and technologies. IEEE Computer Society, Washington, USA; 2013. p. 1–6.
Mathiyalagan P, Sivanandam SN, Saranya KS. Hybridization of modified ant colony optimization and intelligent water drops algorithm for job scheduling. In: Computational Grid. ICTACT Journal on Soft Computing; 2013. p. 651–655.
Johnson M, Preethima RA. Hybrid ACO-IWD optimization algorithm for minimizing weighted flow time in cloud based parameter sweep experiments. Int J Res Eng Technol. 2014; 317–321.
Rabiee M, Sajedi H. Job scheduling in grid computing with cuckoo optimization algorithm. Int J Comput Appl. 2013;62:38–43.
Rabiee M, Sajedi H. A metaheuristic algorithm for job scheduling in grid computing. Int J Mod Educ Comput Sci. 2014;05:52–9.
Bilgaiyan S, Das M, Sagnika S. A multi-objective cat swarm optimization algorithm for workflow scheduling in cloud computing environment. In: Proceedings of international conference on intelligent computing, communication and devices. Advances in Intelligent Systems and Computing. Springer; 2015. p. 73–84.
Basturk B, Karaboga D. A powerful and efficient algorithm for numerical function optimization- artificial bee colony (ABC) algorithm. J Global Optim. 2007; 459–471.
Niu SH, Nee AYC, Ong SK. An improved intelligent water drops algorithm for solving multi-objective job shop scheduling. Eng Appl Artif Intell. 2013; 2431–2442.
Buyya RK, Su J, Wang X, Yeo CS. Optimizing makespan and reliability for workflow applications with reputation and look-ahead genetic algorithm. Future Gener Comput Syst. 2011;27:1124–34.
Berriman B, Deelman E, Good J, Katz DS, Mehta G, Singh G, Su MH, Vahi K. Workflow task clustering for best effort systems with pegasus. In: Proceedings of the 15th ACM Mardi Gras conference; 2008.
Cooper K, Koelbel C, Mandal A, Zhang Y. Combined fault tolerance and scheduling techniques for workflow applications on computational grids. In: Proceedings of 9th IEEE/ACM international symposium on cluster computing and the grid; 2009. p. 244–251.
Lin X, Wu CQ. On scientific workflow scheduling in clouds under budget constraint. In: Proceedings of 42nd international conference on parallel processing. IEEE; 2013. p. 90–99.
Khanesar A, Sharafi Y, Teshnehlab M. Discrete binary cat swarm optimization algorithm. In: Proceedings of 3rd international conference on computer, control and communication; 2013. p. 1– 6.
Bouzidi A, Riffi ME. Discrete cat swarm optimization to resolve the traveling salesman problem. Int J Adv Res Comput Sci Softw Eng. 2013; 13–18.
Bahriye A, Karaboga D. A comparative study of artificial bee colony algorithm. Erciyes University, Department of Computer Engineering, Melikgazi, 38039 Kayseri, Turkey 2009.
Achalakul T, Banharnsakun A, Sirinaovakul B. Job shop scheduling with the best-so far ABC. Eng Appl Artif Intell 2011.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Poonam, Dutta, M., Aggarwal, N. (2016). Meta-Heuristics Based Approach for Workflow Scheduling in Cloud Computing: A Survey. In: Dash, S., Bhaskar, M., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 394. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2656-7_121
Download citation
DOI: https://doi.org/10.1007/978-81-322-2656-7_121
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2654-3
Online ISBN: 978-81-322-2656-7
eBook Packages: EngineeringEngineering (R0)