Abstract
One of the critical challenges facing the cloud computing industry today is to increase the profitability of cloud services. In this paper, we deal with the problem of scheduling parallelizable batch type jobs in commercial data centers to maximize cloud providers’ profit. We propose a novel and efficient two-step on-line scheduler. The first step is to rank the arrival jobs to decide an eligible set based on their inherent profitability and pre-allocate resources to them; and the second step is to re-allocate resources between the waiting jobs from the eligible set, based on threshold profit-effectiveness ratio as a cut-off point, which is decided dynamically by solving an aggregated revenue maximization problem. The results of numerical experiments and simulations show that our approach are efficient in scheduling parallelizable batch type jobs in clouds and our scheduler can outperform other scheduling algorithms used for comparison based on classical heuristics from literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Volker, C.E., Hamscher, V., Yahyapour, R.: Economic scheduling in grid computing. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 128–152. Springer, Heidelberg (2002)
Lartigau, J., Nie, L., Xu, X., Zhan, D., Mou., T.: Scheduling methodology for production services in cloud manufacturing. In: International Joint Conference on Service Sciences, pp. 34–39. IEEE Press, New York (2012)
Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., Gu, Z.: Online optimization for scheduling preempt able tasks on IaaS cloud systems. J. Parallel Distrib. Comput. 72, 666–677 (2012)
Lee, G.: Resource allocation and scheduling in heterogeneous cloud environments. Dissertations and Theses-Grad works, University of California, Berkeley (2012)
Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2, 222–235 (2014)
Zhao, H., Tian, L.: Resource schedule algorithm based on artificial fish swarm in cloud computing environment. In: 4th International Conference on Advanced Design and Manufacturing Engineering, pp. 1614–1617. Trans Tech Publications, Switzerland (2014)
Irwin, D.E., Grit, L.E., Chase, J.S.: Balancing risk and reward in a market-based task service. In: 13th IEEE International Symposium on High Performance Distributed Computing, pp. 160–169. IEEE Press, New York (2004)
Yeo, C., Buyya, R.: Service level agreement based allocation of cluster resources: handling penalty to enhance utility. In: IEEE International Conference on Cluster Computing. IEEE Press, New York (2005)
Garg, S.K., Toosi, A.N., Gopalaiyengar, S.K., Buyya, R.: SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J. Netw. Comput. Appl. 45, 108–120 (2014)
Tsakalozos, K., Kllapi, H., Sitaridi, E., Roussopoulos, M., Paparas, D., Delis, A.: Flexible use of cloud resources through profit maximization and price discrimination. In: 27th International Conference on Data Engineering, pp. 75–86. IEEE Computer Society, US (2011)
Eager, D.L., Zahorjan, J., Lozowska, E.D.: Speedup versus efficiency in parallel systems. IEEE Trans. Comput. 38, 408–423 (1989)
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (61402263), and the Science & Technology Development Projects of Shandong Province (2014GGX101028, 2014GGH20100).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, S., Pan, L., Liu, S., Wu, L., Meng, X. (2016). Profit Based Two-Step Job Scheduling in Clouds. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-39958-4_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39957-7
Online ISBN: 978-3-319-39958-4
eBook Packages: Computer ScienceComputer Science (R0)