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Task scheduling algorithms for multi-cloud systems: allocation-aware approach

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Abstract

Cloud computing has gained enormous popularity for on-demand services on a pay-per-use basis. However, a single data center may be limited in providing such services, particularly in the peak demand time as it may not have unlimited resource capacity. Therefore, multi-cloud environment has been introduced in which multiple clouds can be integrated together to provide a unified service in a collaborative fashion. However, task scheduling in such environment is much more challenging than that is used in the single cloud environment. In this paper, we propose three allocation-aware task scheduling algorithms for a multi-cloud environment. The algorithms are based on the traditional Min-Min and Max-Min algorithm and extended for multi-cloud environment. All the algorithms undergo three common phases, namely matching, allocating and scheduling to fit them in the multi-cloud environment. We perform extensive simulations on the proposed algorithms and test with various benchmark and synthetic datasets. We evaluate the performance of the proposed algorithms in terms of makespan, average cloud utilization and throughput and compare the results with the existing algorithms in such system. The comparison results clearly demonstrate the efficacy of the proposed algorithms.

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References

  • Avetisyan, A.I., Campbell, R., Gupta, I., Heath, M.T., Ko, S.Y., Ganger, G.R., Kozuch, M.A., O’Hallaron, D., Kunze, M., Kwan, T.T., Lai, K., Lyons, M., Milojicic, D.S., Lee, H.Y., Soh, Y.C., Ming, N.K., Luke, J., & Namgoong, H. (2010). Open Cirrus: A Global Cloud Computing Testbed. IEEE Computer Society, 35–43 .

  • Braun, T.D. (2015). https://code.google.com/p/hcsp-chc/source/browse/trunk/AE/ProblemInstances/HCSP/Braun_et_al/u_c_hihi.0?r=93. Accessed on 9th May 2015.

  • Braun, T.D., Siegel, H.J., Beck, N., Boloni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., & Freund, R.F. (2001). A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed computing, 61(6), 810–837.

    Article  Google Scholar 

  • Chhetri, M.B., Chichin, S., Vo, Q.B., & Kowalczyk, R. (2016). Smart CloudBench - A Framework for Evaluating Cloud Infrastructure Performance. Information Systems Frontiers, Springer, 18(3), 413–428.

    Article  Google Scholar 

  • Chunlin, L., & LaYuan, L. (2015). Optimal Scheduling Across Public and Private Clouds in Complex Hybrid Cloud Environment, Information Systems Frontiers, Springer, pp. 1–12.

  • Di, S., Kondo, D., & Cappello, F. (2014). Characterizing and Modeling Cloud Applications/Jobs on a Google Data Center. The Journal of Supercomputing, Springer, 69(1), 139– 160.

    Article  Google Scholar 

  • Durao, F., Carvalho, J.F.S., Fonseka, A., & Garcia, V.C. (2015). A Systematic Review on Cloud Computing. The Journal of Supercomputing, Springer, 68, 1321–1346.

    Article  Google Scholar 

  • Ergu, D., Kou, G., Peng, Y., Shi, Y., & Shi, Y. (2013). The Analytic Hierarchy Process: Task Scheduling and Resource Allocation in Cloud Computing Environment. The Journal of Supercomputing, Springer, 64, 835–848.

    Article  Google Scholar 

  • Eucalyptus (2015). http://manpages.ubuntu.com/manpages/precise/man5/eucalyptus.conf.5.html, Accessed on 17th June 2015.

  • Freund, R.F., Gherrity, M., Ambrosius, S., Campbell, M., Halderman, M., Hensgen, D., Keith, E., Kidd, T., Kussow, M., Lima, J.D., Mirabile, F., Moore, L., Rust, B., & Siegel, H.J. (1998). Scheduling Resources in Multi-User, Heterogeneous, 7 th IEEE Heterogeneous Computing Workshop Computing Environments with SmartNet (pp. 184–199).

  • Forell, T., Milojicic, D., & Talwar, V. (2011). Cloud Management: Challenges and Opportunities, In: IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (pp. 881–889).

  • Gartner (2016). http://www.gartner.com/newsroom/id/3188817, Accessed on 4th June 2016.

  • Goiri, I., Guitart, J., & Torres, J. (2012). Economic Model of a Cloud Provider Operating in a Federated Cloud. Information Systems Frontiers, Springer, 14(4), 827–843.

    Article  Google Scholar 

  • Gorbenko, A., & Popov, V. (2012). Task-Resource Scheduling Problem. International Journal of Automation and Computing, 9, 429–441.

    Article  Google Scholar 

  • Gutierrez-Garcia, J.O., & Sim, K.M. (2012). GA-based Cloud Resource Estimation for Agent-Based Execution of Bag-of-Tasks Applications. Information Systems Frontiers, Springer, 14(4), 925–951.

    Article  Google Scholar 

  • Haizea (2015). http://haizea.cs.uchicago.edu/pydoc/haizea.core.scheduler.policy.HostSelectionPolicy-class.html, Accessed on 17th June 2015.

  • Hassan, M.M., Hossain, M.S., Sarkar, A.M.J., & Huh, E. (2014). Cooperative Game-Based Distributed Resource Allocation in Horizontal Dynamic Cloud Federation Platform. Information Systems Frontiers, Springer, 16 (4), 523–542.

    Article  Google Scholar 

  • Ibarra, O.H., & Kim, C.E. (1977). Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors. Journal of the Association for Computing Machinery, 24(2), 280–289.

    Article  Google Scholar 

  • Krishnaswamy, V., & Sundarraj, R.P. (2015). Organizational Implications of a Comprehensive Approach for Cloud-Storage Sourcing, Information Systems Frontiers, Springer, pp. 1–17.

  • Lacheheub, M.N., & Maamri, R. (2016). Towards a Construction of an Intelligent Business Process based on Cloud Services and Driven by Degree of Similarity and QoS. Information Systems Frontiers, Springer, 18(6), 1085–1102.

    Article  Google Scholar 

  • Lai, K., & Yu, Y. (2012). A Scalable Multi-Attribute Hybrid Overlay for Range Queries on the Cloud. Information Systems Frontiers, Springer, 14(4), 895–908.

    Article  Google Scholar 

  • Liao, J., Yang, D., Li, T., Wang, J., Qi, Q., & Zhu, X. (2014). A Scalable Approach for Content Based Image Retrieval in Cloud Datacenter. Information Systems Frontiers, Springer, 16(1), 129–141.

    Article  Google Scholar 

  • Li, G., & Wei, M. (2014). Everything-as-a-Service Platform for On-Demand Virtual Enterprises. Information Systems Frontiers, Springer, 16(3), 435–452.

    Article  Google Scholar 

  • Li, W., Tordsson, J., & Elmroth, E. (2012). Virtual Machine Placement for Predictable and Time-Constrained Peak Loads, Economics of Grids, Clouds, Systems and Services. Lecture Notes in Computer Science, 7150, 120–134.

    Article  Google Scholar 

  • Li, J., Qiu, M., Ming, Z., Quan, G., Qin, X., & Gu, Z. (2012). Online Optimization for Scheduling Preemptable Tasks on IaaS Cloud System. Journal of Parallel Distributed Computing, Elsevier, 72, 666–677.

    Article  Google Scholar 

  • Lim, J., Suh, T., Gil, J., & Yu, H. (2014). Scalable and Leaderless Byzantine Consensus in Cloud Computing Environments. Information Systems Frontiers, Springer, 16(1), 19–34.

    Article  Google Scholar 

  • Liu, Y., Zhang, C., Li, B., & Niu, J. (2015). DeMS: A Hybrid Scheme of Task Scheduling and Load Balancing in Computer Clusters, Journal of Network and Computer Applications, Elsevier.

  • Ming, G., & Li, H. (2012). An Improved Algorithm Based on Max-Min for Cloud Task Scheduling, Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, 125, 217–223.

    Article  Google Scholar 

  • Nimbus (2015). http://www.nimbusproject.org/docs/2.5/changelog.html, Accessed on 16th June 2015.

  • OpenNebula (2015). http://archives.opennebula.org/documentation:rel4.4:schg, Accessed on 15th June 2015.

  • Panda, S.K., & Jana, P.K. (2014). An Efficient Task Scheduling Algorithm for Heterogeneous Multi-cloud Environment, Third International Conference on Advances in Computing, Communications & Informatics, IEEE (pp. 1204–1209).

    Google Scholar 

  • Panda, S.K., & Jana, P.K. (2015). Efficient Task Scheduling Algorithms for Heterogeneous Multi-cloud Environment. The Journal of Supercomputing, Springer, 71(4), 1505–1533.

    Article  Google Scholar 

  • Panda, S.K., Gupta, I., & Jana, P.K. (2015). Allocation-Aware Task Scheduling for Heterogeneous Multi-Cloud Systems. In Second International Symposium on Big Data and Cloud Computing Challenges, Procedia Computer Science, Elsevier, 50, 176–184.

    Google Scholar 

  • Panda, S.K., & Jana, P.K. (2016). Uncertainty-Based QoS Min–Min Algorithm for Heterogeneous Multi-cloud Environment, Arabian Journal of Science and Engineering, Springer, pp. 1–23.

  • Panda, S.K., & Jana, P.K. (2016). Normalization-Based Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment, Information Systems Frontiers, Springer, pp. 1–27.

  • Panda, S.K., & Jana, P.K. (2017). SLA-Based Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment. The Journal of Supercomputing (pp. 1–33). Springer.

  • Rackspace (2015). http://docs.rackspace.com/cas/api/v1.0/autoscale-devguide/content/Schedule_based_Policy.html, Accessed on 18th June 2015.

  • Rimal, B.P., Choi, E., & Lumb, I. (2009). A Taxonomy and Survey of Cloud Computing Systems, International Joint Conference on INC, IMS and IDC (pp. 44–51).

    Google Scholar 

  • Seethamraju, R. (2014). Adoption of Software as a Service (SaaS) Enterprise Resource Planning (ERP) Systems in Small and Medium Sized Enterprises (SMEs). Information Systems Frontiers, Springer, 17(3), 475–492.

    Article  Google Scholar 

  • Son, S., & Sim, K.M. (2015). Adaptive and Similarity-Based Tradeoff Algorithms in a Price-Timeslot-QoS Negotiation System to Establish Cloud SLAs. Information Systems Frontiers, Springer, 17(3), 565–589.

    Article  Google Scholar 

  • Tang, C., Steinder, M., Spreitzer, M., & Pacifici, G. (2007). A Scalable Application Placement Controller for Enterprise Data Centers, 16 th International Conference on World Wide Web (pp. 331– 340).

    Google Scholar 

  • Thomas, M., Costa, D., & Oliveira, T. (2016). Assessing the Role of IT-Enabled Process Virtualization on Green IT Adoption. Information Systems Frontiers, Springer, 18(4), 693–710.

    Article  Google Scholar 

  • Ullman, J.D. (1975). NP-Complete Scheduling Problems. Journal of Computer and System Sciences, 10(3), 384–393.

    Article  Google Scholar 

  • Wang, S., Yan, K., Liao, W., & Wang, S. (2010). Towards a Load Balancing in a Three-level Cloud Computing Network, 3 rd IEEE International Conference on Computer Science and Information Technology (vol. 1, pp. 108–113).

    Google Scholar 

  • Weighted Least-Connection Scheduling (2015). http://kb.linuxvirtualserver.org/wiki/Weighted_Least-Connection_Scheduling, Accessed on 17th June 2015.

  • Wu, H., Lu, G., Li, D., Guo, C., & Zhang, Y. (2009). MDCube: A High Performance Network Structure for Modular Data Center Interconnection, The 5 th ACM International Conference on Emerging Networking Experiments and Technologies (pp. 25– 36).

    Google Scholar 

  • Xhafa, F., Barolli, L., & Durresi, A. (2007). Batch Mode Scheduling in Grid Systems. International Journal Web and Grid Services, 3(1), 19–37.

    Article  Google Scholar 

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Acknowledgments

The first version of this paper has appeared in proceedings of 2ndInternational Symposium on Big Data and Cloud Computing (ISBCC 2015). We would like to thank the anonymous reviewers for their valuable comments and future research directions which greatly help us to extend this paper.

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Authors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    Sanjaya K. Panda

  2. Department of Computer Science & Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Indrajeet Gupta & Prasanta K. Jana

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  1. Sanjaya K. Panda
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  2. Indrajeet Gupta
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Correspondence to Sanjaya K. Panda.

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Panda, S.K., Gupta, I. & Jana, P.K. Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf Syst Front 21, 241–259 (2019). https://doi.org/10.1007/s10796-017-9742-6

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