Abstract
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 heterogeneous jobs in heterogeneous hybrid cloud environment is still a challenge. In this paper, a multi-queue scheduling approach of heterogeneous jobs for cloud bursting is proposed. In the private cloud, jobs are classified into I/O-intensive and CPU-intensive jobs, and nodes are divided into main I/O and CPU resource pools. Jobs are dispatched to corresponding resource pools to reduce the job execution time in heterogeneous cloud environment. A genetic algorithm is applied to schedule jobs to optimal job queues, which can reduce the job waiting time. Then, the execution time of each task is predicted by BP neural network. Jobs with high priority will be allocated to resources with the earliest finish time in the private cloud according to the prediction results. If the private cloud cannot meet the demand of users, public clouds with minimal costs will be applied. Experiments show that our proposed algorithm can reduce the average job response time and improve the throughput of the private cloud. It also can reduce the average task waiting time, average task execution time and average task response time significantly. Moreover, the costs of the hybrid clouds are reduced.
Similar content being viewed by others
References
Hwang K, Bai X, Shi Y, Li M, Chen W, Yong Wu (2016) Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans Parallel Distrib Syst 27(1):130–143
Farahabady MRH, Lee YC, Zomaya AY (2014) Pareto-optimal cloud bursting. IEEE Trans Parallel Distrib Syst 25(10):2670–2682
Yuan H, Bi J, Tan W, Li BH (2017) Temporal task scheduling with constrained service delay for profit maximization in hybrid clouds. IEEE Trans Autom Sci Eng 14(1):337–348
TaoBao. https://www.taobao.com/. Accessed 24 June 2017
Amazon. https://www.amazon.com/. Accessed 24 June 2017
Clemente-Castello FJ, Mayo R, Fernandez JC (2017) cost model and analysis of iterative Mapreduce applications for hybrid cloud bursting. In: Proceedings of 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, pp 858–864
Lee YC, Lian B (2017) Cloud Bursting scheduler for cost efficiency. In: Proceedings of 10th IEEE International Conference on Cloud Computing (CLOUD). IEEE, pp 774–777
Nicolae B, Rafique MM, Mayo R (2017) Evaluation of data locality strategies for hybrid cloud bursting of iterative Mapreduce. In: Proceedings of 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, pp 181–185
Guo T, Sharma U, Shenoy P, Wood T, Sahu S (2014) Cost-aware cloud bursting for enterprise applications. ACM Trans Internet Technol 13(3):10
Lu P, Sun Q, Wu K, Zhu Z (2015) Distributed online hybrid cloud management for profit-driven multimedia cloud computing. IEEE Trans Multimed 17(8):1297–1308
Charrada F, Tata S (2016) An efficient algorithm for the bursting of service-based applications in hybrid Clouds. IEEE Trans Serv Comput 9(3):357–367
Malawski M, Figiela K, Nabrzyski J (2013) Cost minimization for computational applications on hybrid cloud infrastructures. Future Gener Comput Syst 29(7):1786–1794
Kailasam S, Gnanasambandam N, Dharanipragada J, Sharma N (2013) Optimizing ordered throughput using autonomic cloud bursting schedulers. IEEE Trans Softw Eng 39(11):1564–1581
Toosi AN, Sinnott R, Buyya R (2018) Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka. Future Gener Comput Syst 79(2):765–775
Van den Bossche R, Vanmechelen K, Broeckhove J (2013) Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds. Future Gener Comput Syst 29(4):973–985
Zhu J, Li X, Ruiz R, Xu X, Zhang Y (2016) Scheduling stochastic multi-stage jobs on elastic computing services in hybrid clouds. In: Proceedings of 23rd IEEE International Conference on Web Services (ICWS). IEEE, pp 678–681
Genez TAL, Bittencourt L, Fonseca N, Madeira E (2015) Estimation of the available bandwidth in inter-cloud links for task scheduling in hybrid clouds. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2015.2469650
Pelaez V, Campos A, Garcia DF, Entrialgo J (2016) Autonomic scheduling of deadline-constrained bag of tasks in hybrid clouds. In: Proceedings of International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS). IEEE. https://doi.org/10.1109/spects.2016.7570526
Champati JP, Liang B (2015) One-restart algorithm for scheduling and offloading in a hybrid cloud. In: Proceedings of 23rd IEEE International Symposium on Quality of Service (IWQoS). IEEE, pp 31–40
Duan R, Prodan R, Li X (2014) Multi-objective game theoretic scheduling of bag-of-tasks workflows on hybrid clouds. IEEE Trans Cloud Comput 2(1):29–42
Kliazovich D, Pecero JE, Tchernykh A, Bouvry P, Khan SU, Zomaya AY (2016) CA-DAG: modeling communication-aware applications for scheduling in cloud computing. J Grid Comput 14(1):23–39
Singh S, Chana I (2015) QRSF: QoS-aware resource scheduling framework in cloud computing. J supercomput 71(1):241–292
Wang X, Wang Y, Hao Z, Du J (2016) the research on resource scheduling based on fuzzy clustering in cloud computing. In: Proceedings of 8th International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, pp 1025–1028
Spicuglia S, Chen LY, Serazzi G, Binder W, Smirni E (2013) On load balancing: a mix-aware algorithm for heterogeneous systems. In: Proceedings of 4th ACM/SPEC International Conference on Performance Engineering (ICPE). ACM, pp 71–76
Stanford large network dataset collection (SNAP). http://snap.stanford.edu/data/index.html. Accessed 16 July 2017
Rasooli A, Down DG (2014) COSHH: a classification and optimization based scheduler for heterogeneous Hadoop systems. Future Gener Comput Syst 36(3):1–15
Wang WJ, Chang YS, Lo WT, Lee YK (2013) Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments. J supercomput 66(2):783–811
C. Software, SourceMonitor Version 3.4. http://www.campwoodsw.com/sourcemonitor.html. Accessed 2 June 2016
Teng F, Yu L, Li T (2014) A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud. J supercomput 69(2):739–765
Acknowledgements
The work was supported by the National Natural Science Foundation of China (NSFC) under Grants (Nos. 61472294, 61672397), Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography (Grant No. 2017B030314138), Fundamental Research Funds for the Central Universities (WUT No.2017-YB-029), State Key Laboratory of Software Development Environment, Beihang University, (Grant No. SKLSDE-2017KF-04). Any opinions, findings, and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chunlin, L., Jianhang, T. & Youlong, L. Multi-queue scheduling of heterogeneous jobs in hybrid geo-distributed cloud environment. J Supercomput 74, 5263–5292 (2018). https://doi.org/10.1007/s11227-018-2420-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-018-2420-8