Abstract
Cloud Computing refers to application and services offered over Internet using pay-as-you-go model. The services are offered from data centers all over the world, which jointly are referred to as the “Cloud”. The data centers use scheduling techniques to effectively allocate virtual machines to cloud applications. The cloud applications in area such as business enterprises, bio-informatics and astronomy need workflow processing in which tasks are executed based on data dependencies. The cloud users impose QoS constraints while executing their workflow applications on cloud. The QoS parameters are defined in SLA (Service Level Agreement) document which is signed between cloud user and cloud provider. In this paper, a genetic algorithm has been proposed that schedules workflow applications in unreliable cloud environment and meet user defined QoS constraints. A budget constrained time minimization genetic algorithm has been proposed which reduces the failure rate and makespan of workflow applications. It allocates those resources to workflow application which are reliable and cost of execution is under user budget. The performance of genetic algorithm has been compared with max-min and min-min scheduling algorithms in unreliable cloud environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yu, J., Buyya, R., Kotagiri, A.: Workflow Scheduling Algorithms for Grid Computing, vol. 146, pp. 173–214. Springer, Heidelberg (2008)
Hou, E.S.H., Ansari, N., Ren, H.: A Genetic Algorithm for Multiprocessor Scheduling. In: IEEE Proceeding on Parallel and Distributed Systems, vol. 5 (1994)
Wang, P.C., Korfhage, W.: Process Scheduling using Genetic Algorithm. In: Parallel and Distributed Proceeding Seventh IEEE Symposium, pp. 638–641 (1995)
Wang, L., Siegel, H.J., Roychowdhury, V.P.: A Genetic Algorithm Based Approach for Task Matching and Scheduling in Heterogeneous Computing Environments. Journal of Parallel and Distributed Computing-Special Issue on Parallel Evolutionary Computing Archive 47, 8–22 (1997)
Liu, D., Li, Y., Yu, M.: A Genetic Algorithm for Task Scheduling in Network Computing Environment. In: Algorithms and Architectures for Parallel Processing Proceeding IEEE Fifth International Conference, pp. 126–129 (2002)
Page, A.J., Naughton, T.J.: Dynamic Task Scheduling using Genetic Algorithm for Heterogeneous Distributed Computing. In: Proceedings 19th IEEE Conference on Parallel and Distributed Processing Symposium (2005)
Moattar, E.Z., Rahmani, A.M., Derakhshi, M.R.F.: Job Scheduling in Multiprocessor Architecture using Genetic Algorithm. In: 4th IEEE Conference on Innovations in Information Technology, pp. 248–251 (2007)
Mocanu, E.M., Florea, M., Ionut, M.: Cloud Computing Task Scheduling Based on Genetic Algorithm. In: System IEEE Conference, pp. 1–6 (2012)
Dogan, A., Ozguner, F.: Bi-Objective Scheduling Algorithms for Execution Time and Reliability Trade off in Heterogeneous Computing System. The Computer Journal 48, 300–314 (2005)
Wang, X.F., Yeo, C.S., Buyya, R., Su, J.: Optimizing the Makespan and Reliability for Workflow Applications with Reputation and a Look-ahead Genetic Algorithm. Future Generation Computer Systems 27, 1124–1134 (2011)
Delavar, A.G., Aryan, Y.: A Goal-Oriented Workflow Scheduling in Heterogeneous Distributed System. IJCA 52, 27–33 (2012)
Yu, J., Buyya, R.: A Budget Constraint Scheduling of Workflow Application on Utility Grid Using Genetic Algorithm. In: 15th IEEE International Symposium on High Performance Distributed Computing (HPDC 2006), Paris (2006)
Calheiros, R.N., Ranjan, R., De Rose, C.A.F., Buyya, R.K.: CloudSim: A Novel Framework for Modelling and Simulation of Cloud Computing Infrastructures and Services. GRIDS Laboratory. The University of Melbourne, Australia (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Singh, L., Singh, S. (2014). A Genetic Algorithm for Scheduling Workflow Applications in Unreliable Cloud Environment. In: Martínez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-54525-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54524-5
Online ISBN: 978-3-642-54525-2
eBook Packages: Computer ScienceComputer Science (R0)