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Deadline Constrained Task Scheduling Based on Standard-PSO in a Hybrid Cloud

  • Guoxiang Zhang
  • Xingquan Zuo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

Public cloud providers provide Infrastructure as a Service (IaaS) to remote users. For IaaS providers, how to schedule tasks to meet peak demand is a big challenge. Previous researches proposed purchasing machines in advance or building cloud federation to resolve this problem. However, the former is not economic and the latter is hard to be put into practice at present. In this paper, we propose a hybrid cloud architecture, in which an IaaS provider can outsource its tasks to External Clouds (ECs) without establishing any agreement or standard when its local resources are not sufficient. The key issue is how to allocate users’ tasks to maximize its profit while guarantee QoS. The problem is formulated as a Deadline Constrained Task Scheduling (DCTS) problem which is resolved by standard particle swarm optimization (PSO), and compared with an exact approach (CPLEX). Experiment results show that Standard-PSO is very effective for this problem.

Keywords

IaaS cloud task scheduling hybrid cloud Standard-PSO 

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References

  1. 1.
    Bhardwaj, S., Jain, L., Jain, S.: Cloud computing: a study of infrastructure as a service (IaaS). International Journal of Engineering and Information Technology 2(1), 60–63 (2010)Google Scholar
  2. 2.
    Liu, H., Orban, D.: GridBatch: cloud computing for large-scale data-intensive batch applications. In: IEEE International Symposium on Cluster Computing and the Grid, Lyon, France, pp. 295–305 (2008)Google Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: IEEE Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
  4. 4.
    Liu, B., Wang, L., Jin, Y.: An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Transactions on System, Man, and Cybernetics, Part B: Cybernetics 37(1), 985–997 (2007)Google Scholar
  5. 5.
    Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing 6(2), 154–160 (1994)Google Scholar
  6. 6.
    Bossche, R.V., Vanmechelen, K., Broeckhove, J.: Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workload. In: IEEE International Conference on Cloud Computing, Miami, Florida, pp. 228–235 (2010)Google Scholar
  7. 7.
    He, S., Guo, L., Guo, Y.: Real time elastic cloud management for limited resources. In: IEEE International Conference on Cloud Computing, Washington D. C., USA, pp. 622–629 (2011)Google Scholar
  8. 8.
    Doctor, S., Venayagamoorthy, G.K., Gudise, V.G.: Optimal PSO for collective robotic search applications. In: IEEE Congress on Evolutionary Computation, San Diego, CA, USA, pp. 1390–1395 (2004)Google Scholar
  9. 9.
    Nathani, A., Chaudhary, S., Somani, G.: Policy based resource allocation in IaaS cloud. Future Generation Computer System 28(1), 94–103 (2012)Google Scholar
  10. 10.
    Zhao, C., Zhang, S., Liu, Q., Xie, J., Hu, J.: Independent tasks scheduling based on genetic algorithm in cloud computing. In: International Conference on Wireless Communications, Networking and Mobile Computing, Marrakech, Morocco, pp. 1–4 (2009)Google Scholar
  11. 11.
    Li, L.: An optimistic differentiated service job scheduling system for cloud computing service users and providers. In: International Conference on Multimedia and Ubiquitous Engineering, Qingdao, China, pp. 295–299 (2009)Google Scholar
  12. 12.
    Li, C., Li, L.: A distributed multiple dimensional QoS constrained resource scheduling optimization policy in computational grid. Journal of Computer and System Science 72(4), 706–726 (2006)zbMATHCrossRefGoogle Scholar
  13. 13.
    Toosi, A.N., Calheiros, R.N., Thulasiram, P.K., Buyya, R.: Resource provisioning policies to increase IaaS provider’s profit in a federated cloud environment. In: IEEE International Conference on High Performance Computing and Communications, Banff, Canada, pp. 279–287 (2011)Google Scholar
  14. 14.
    Breitgand, D., Maraschini, A., Tordsson, J.: Policy-driven service placement optimization in federated cloud. IBM Research Report (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guoxiang Zhang
    • 1
  • Xingquan Zuo
    • 2
  1. 1.Institute of Microelectronics of Chinese Academy of SciencesBeijingChina
  2. 2.Beijing University of Posts and TelecommunicationsBeijingChina

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