Article

Scheduling jobs in the cloud using on-demand and reserved instances

Authors:

Alexandru Iosup,

Dick EpemaAuthors Info & Claims

Euro-Par'13: Proceedings of the 19th international conference on Parallel Processing

Pages 242 - 254

https://doi.org/10.1007/978-3-642-40047-6_27

Published: 26 August 2013 Publication History

Abstract

Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy -- larger or faster instances? on-demand or reserved instances? etc.-- and to configure the leasing strategy with appropriate scheduling policies is still daunting for the (potential) cloud user. In this work, we investigate leasing strategies and their policies from a broker's perspective. We propose, CoH, a family of Cloud-based, online, Hybrid scheduling policies that minimizes rental cost by making use of both on-demand and reserved instances. We formulate the resource provisioning and job allocation policies as Integer Programming problems. As the policies need to be executed online, we limit the time to explore the optimal solution of the integer program, and compare the obtained solution with various heuristics-based policies; then automatically pick the best one. We show, via simulation and using multiple real-world traces, that the hybrid leasing policy can obtain significantly lower cost than typical heuristics-based policies.

References

[1]

Marshall, P., Keahey, K., Freeman, T.: Elastic site: Using clouds to elastically extend site resources. In: CCGrid 2010, pp. 43-52 (2010)

Digital Library

[2]

Schwiegelshohn, U., Badia, R. M., Bubak, M., et al.: Perspectives on grid computing. In: FGCS 2010, vol. 26(8) (2010)

Digital Library

[3]

Murphy, M., Kagey, B., Fenn, M., Goasguen, S.: Dynamic provisioning of virtual organization clusters. In: CCGrid 2009, pp. 364-371 (2009)

Digital Library

[4]

Ben-Yehuda, O. A., Schuster, A., Sharov, A., Silberstein, M., Iosup, A.: Expert: Pareto-efficient task replication on grids and a cloud. In: IPDPS 2012, pp. 167-178 (2012)

Digital Library

[5]

Fölling, A., Hofmann, M.: Improving scheduling performance using a Q-learningbased leasing policy for clouds. In: Kaklamanis, C., Papatheodorou, T., Spirakis, P. G. (eds.) Euro-Par 2012. LNCS, vol. 7484, pp. 337-349. Springer, Heidelberg (2012)

Digital Library

[6]

de Assuncao, M. D., Costanzo, A.d., Buyya, R.: Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters. In: HPDC 2009, pp. 141-150 (2009)

Digital Library

[7]

Warneke, D., Kao, O.: Exploiting dynamic resource allocation for efficient parallel data processing in the cloud. In: TPDS 2011, pp. 985-997 (2011)

Digital Library

[8]

Webb, J.: How the cloud helps Netflix (May 2011), http://radar.oreilly.com/2011/05/netflix-cloud.html

[9]

Sharma, U., Shenoy, P., Sahu, S., Shaikh, A.: A cost-aware elasticity provisioning system for the cloud. In: ICDCS 2011, pp. 559-570 (2011)

Digital Library

[10]

Nicolae, B., Cappello, F., Antoniu, G.: Optimizing multi-deployment on clouds by means of self-adaptive prefetching. In: Euro-Par 2011, pp. 503-513 (2011)

Digital Library

[11]

Villegas, D., Antoniou, A., Sadjadi, S. M., Iosup, A.: An analysis of provisioning and allocation policies for infrastructure-as-a-service clouds. In: CCGrid 2012 (2012)

Digital Library

[12]

Huberman, B. A.: An Economics Approach to Hard Computational Problems. Science 275, 51-54 (1997)

[13]

Stillwell, M., Vivien, F., Casanova, H.: Dynamic fractional resource scheduling for hpc workloads. In: IPDPS 2010 (2010)

[14]

Iosup, A., Ostermann, S., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. TPDS (2010)

Digital Library

[15]

Nae, V., Iosup, A., Prodan, R.: Dynamic resource provisioning in massively multiplayer online games. TPDS 22(3) (2011)

Digital Library

[16]

Feitelson, D.: Parallel Workloads Archive, http://www.cs.huji.ac.il/labs/parallel/workload/

[17]

Iosup, A., Li, H., Jan, M., Anoep, S., Dumitrescu, C., Wolters, L., Epema, D. H. J.: The grid workloads archive. FGCS 2008 24(7), 672-686 (2008)

Digital Library

[18]

Guo, A.Y., Iosup: The game trace archive. In: NETGAMES (2012)

Digital Library

[19]

Guo, Y., Shen, S., Visser, O., Iosup, A.: An Analysis of Online Match-Based Games. In: MMVE 2012 (2012)

[20]

Zhang, T., Du, Z., Chen, Y., Ji, X., Wang, X.: Typical virtual appliances: An optimized mechanism for virtual appliances provisioning and management. Journal of Systems and Software 84(3), 377 (2011)

Digital Library

[21]

Hadji, M., Zeghlache, D.: Minimum cost maximum flow algorithm for dynamic resource allocation in clouds. In: CLOUD 2012, pp. 876-882 (2012)

Digital Library

[22]

Ren, S., He, Y., Xu, F.: Provably-efficient job scheduling for energy and fairness in geographically distributed data centers. In: ICDCS 2012, pp. 22-31 (2012)

Digital Library

[23]

Tordsson, J., Montero, R. S., Moreno-Vozmediano, R., Llorente, I. M.: Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener. Comput. Syst. 28(2) (2012)

Digital Library

[24]

Genaud, S., Gossa, J.: Cost-wait trade-offs in client-side resource provisioning with elastic clouds. In: CLOUD 2011 (2011)

Digital Library

[25]

Deng, K., Verboon, R., Iosup, A.: A Periodic Portfolio Scheduler for Scientific Computing in the Data Center. In: JSSPP (2013)

[26]

Oprescu, A., Kielmann, T.: Bag-of-tasks scheduling under budget constraints. In: CloudCom 2010, pp. 351-359 (2010)

Digital Library

[27]

Mao, M. M., Li, J., Humphrey: Cloud auto-scaling with deadline and budget constraints. In: GRID 2010, pp. 41-48 (2010)

[28]

Hong, Y. J., Xue, J., Thottethodi: Selective commitment and selective margin: Techniques to minimize cost in an iaas cloud. In: ISPASS 2012, pp. 99-109 (2012)

Digital Library

[29]

Chaisiri, S., Lee, B. S., Niyato, D.: Optimization of resource provisioning cost in cloud computing. Transactions on Services Computing, 164-177 (2012)

Digital Library

[30]

Ostermann, S., Prodan, R.: Impact of variable priced cloud resources on scientific workflow scheduling. In: Euro-Par 2012, pp. 350-362 (2012)

Digital Library

[31]

Song, Y., Zafer, M., Lee, K. W.: Optimal bidding in spot instance market. In: INFOCOM 2012, pp. 190-198 (2012)

Cited By

Mehboob TBashir NZink MIrwin DVieira MCardellini VDi Marco ATuma P(2023)Is Sharing Caring? Analyzing the Incentives for Shared Cloud ClustersProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583730(7-16)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578244.3583730
Kiessler MHaag VPittl BSchikuta E(2022)Optimization Heuristics for Cost-Efficient Long-Term Cloud Portfolio AllocationsInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_26(309-323)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-21047-1_26
Ambati PBashir NIrwin DShenoy PCuicchi CQualters IKramer W(2020)Waiting gameProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3433701.3433790(1-14)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.5555/3433701.3433790
Show More Cited By

Scheduling jobs in the cloud using on-demand and reserved instances
1. General and reference
  1. Cross-computing tools and techniques
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

Recommendations

Multi-queue scheduling of heterogeneous jobs in hybrid geo-distributed cloud environment

In hybrid geo-distributed clouds, there is a technique named cloud bursting in which applications are handled in the private cloud with less expenses and burst into public clouds when the resources of the private cloud run out. However, how to deploy ...
Hybrid Cloud Scheduling Method for Cloud Bursting
Abstract
In the paper, we consider the hybrid cloud model used for cloud bursting, when the computational capacity of the private cloud provider is insufficient to deal with the peak number of customers’ applications, the private cloud will rely on the ...
Amazon Elastic Compute Cloud User Guide for Linux Instances

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

Euro-Par'13: Proceedings of the 19th international conference on Parallel Processing

August 2013

885 pages

ISBN:9783642400469

Editors:
Felix Wolf
German Research School for Simulation Sciences, RWTH Aachen, Schinkelstr. 2a, Aachen, Germany
,
Bernd Mohr
Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Station 22,, Jülich, Germany
,
Dieter Mey
Center for Computing and Communication, RWTH Aachen, Seffenter Weg 23, Aachen, Germany

Sponsors

INTEL: Intel Corporation
Deutsche Forschungsgemeinschaft
NVIDIA
Bull GmbH: Bull GmbH

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 August 2013

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mehboob TBashir NZink MIrwin DVieira MCardellini VDi Marco ATuma P(2023)Is Sharing Caring? Analyzing the Incentives for Shared Cloud ClustersProceedings of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578244.3583730(7-16)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578244.3583730
Kiessler MHaag VPittl BSchikuta E(2022)Optimization Heuristics for Cost-Efficient Long-Term Cloud Portfolio AllocationsInformation Integration and Web Intelligence10.1007/978-3-031-21047-1_26(309-323)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-21047-1_26
Ambati PBashir NIrwin DShenoy PCuicchi CQualters IKramer W(2020)Waiting gameProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.5555/3433701.3433790(1-14)Online publication date: 9-Nov-2020
https://dl.acm.org/doi/10.5555/3433701.3433790
van Eyk EIosup AAbad CGrohmann JEismann SWolter KKnottenbelt Wvan Hoorn ANambiar MKoziolek H(2018)A SPEC RG Cloud Group's Vision on the Performance Challenges of FaaS Cloud ArchitecturesCompanion of the 2018 ACM/SPEC International Conference on Performance Engineering10.1145/3185768.3186308(21-24)Online publication date: 2-Apr-2018
https://dl.acm.org/doi/10.1145/3185768.3186308
Duque RArbelaez ADíaz J(2018)Online over time processing of combinatorial problemsConstraints10.1007/s10601-018-9287-423:3(310-334)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10601-018-9287-4
Madni SLatiff MCoulibaly YAbdulhamid S(2016)Resource scheduling for infrastructure as a service (IaaS) in cloud computingJournal of Network and Computer Applications10.1016/j.jnca.2016.04.01668:C(173-200)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.04.016
Vieira CBittencourt LMadeira E(2015)A Two-Dimensional SLA for Services Scheduling in Multiple IaaS Cloud ProvidersInternational Journal of Distributed Systems and Technologies10.4018/IJDST.20151001036:4(45-64)Online publication date: 1-Oct-2015
https://dl.acm.org/doi/10.4018/IJDST.2015100103
Shen SIosup AIsrael ACirne WRaz DEpema DBalaji PXu C(2015)An availability-on-demand mechanism for datacentersProceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing10.1109/CCGrid.2015.58(495-504)Online publication date: 4-May-2015
https://dl.acm.org/doi/10.1109/CCGrid.2015.58
Deng KSong JRen KIosup AGropp WMatsuoka S(2013)Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS cloudsProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.1145/2503210.2503244(1-12)Online publication date: 17-Nov-2013
https://dl.acm.org/doi/10.1145/2503210.2503244

View Options

View options

Figures

Tables

Media

View Table of Conten