Abstract
Cloud computing is one of the most successful technologies that offer on-demand services through the Internet. However, datacenters of the clouds may not have unlimited capacity which can fulfill the demanded services in peak hours. Therefore, scheduling workloads across multiple clouds in a federated manner has gained a significant attention in the recent years. In this paper, we present four task scheduling algorithms, called CZSN, CDSN, CDN and CNRSN for heterogeneous multi-cloud environment. The first two algorithms are based on traditional normalization techniques, namely z-score and decimal scaling respectively which are hired from data mining. The next two algorithms are based on two newly proposed normalization techniques, called distribution scaling and nearest radix scaling respectively. All the proposed algorithms are shown to work on-line. We perform rigorous experiments on the proposed algorithms using various synthetic as well as benchmark datasets. Their performances are evaluated through simulation run by measuring two performance metrics, namely makespan and average cloud utilization. The experimental results are compared with that of existing algorithms to show the efficacy of the proposed algorithms.
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References
Amazon’s Elastic Compute Cloud (EC2), aws.amazon.com/ec2/, Accessed on 31st March 2014.
Bajaj, R., & Agrawal, D. P. (2004). “Improving Scheduling of Tasks in a Heterogeneous Environment”. IEEE Transactions on Parallel and Distributed Systems, 15(2), 107–118.
Begnum, K. (2012). “Simplified Cloud-Oriented Virtual Machine Management with MLN”. The Journal of Supercomputing, 61(Issue 2), 251–266.
Bittencourt, L. F., Madeira, E. R. M., & Fonseca, N. L. S. D. (2012). “Scheduling in Hybrid Clouds”. IEEE Communications Magazine, 50(9), 42–47.
Bozdag, D., Ozguner, F., & Catalyurek, U. (2009). “Compaction of Schedules and a Two-Stage Approach for Duplication-Based DAG Scheduling”. IEEE Transactions on Parallel and Distributed Systems, 20(6), 857–871.
Braun Data Set, https://code.google.com/p/hcsp-chc/source/browse/trunk/AE/ ProblemInstances/HCSP/, Accessed on 31st March 2014.
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.
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). “Cloud Computing and Emerging IT Platforms: Vision, Hype and Reality for Delivering Computing as the 5th Utility”. Future Generation Computer Systems, Elsevier, 25, 599–616.
CloudSigma, www.cloudsigma.com/, Accessed on 31st March 2014.
Durao, F., Carvalho, J. F. S., Fonseka, A., & Garcia, V. C. (2014). “A Systematic Review on Cloud Computing”. The Journal of Supercomputing, 68, 1321–1346.
Fan, P., Chen, Z., Wang, J., & Zheng, Z. (2012). “Online Optimization of VM Deployment in IaaS Cloud”. 18th IEEE International Conference on Parallel and Distributed Systems, 760–765.
Fang, D., Liu, X., Liu, L., & Yang, H. (2014). “OCSO: Off-the-Cloud Service Optimization for Green Efficient Service Resource Utilization”. Journal of Cloud Computing, Springer, 3, 1–17.
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, Computing Environments with SmartNet”. 7th IEEE Heterogeneous Computing Workshop,, 184–199.
Gerasoulis, A., & Yang, T. (1992). “A Comparison of Clustering Heuristics for Scheduling Directed Acyclic Graphs on Multiprocessors”. Journal of Parallel and Distributed Computing, Academic Press, 16, 276–291.
GoGrid, http://www.gogrid.com/, Accessed on 31st March 2014.
Haizea, http://haizea.cs.uchicago.edu/manual/node9.html, Accessed on 31st March 2014.
Han, J., & Kamber, M. (2006). “Data Mining Concepts and Techniques”, (Second ed.). Morgan Kaufmann Publishers: Elsevier.
Huang, W., Liu, J., Abali, B., & Panda, D. K. (2006). “A Case for High Performance Computing with Virtual Machines”. 20th Annual International Conference on Supercomputing, 125–134.
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.
Khan, A. A., Mccreary, C. L., & Jones, M. S. (1994). “A Comparison of Multiprocessor Scheduling Heuristics”. International Conference on Parallel Processing, IEEE, 243–250.
Kwok, Y. K., & Ahmad, I. (1998). “Benchmarking the Task Graph Scheduling Algorithms”. Parallel Processing Symposium, IEEE, 531–537.
Li, J., Qiu, M., Niu, J. W., Chen, Y., & Ming, Z. (2010). “Adaptive Resource Allocation for Preemptable Jobs in Cloud Systems”. 10th IEEE International Conference on Intelligent Systems Design and Applications, 31–36.
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.
Liou, J. C., & Palis, M. A. (1997). “A Comparison of General Approaches to Multiprocessor Scheduling”. 11th International Parallel Processing Symposium, IEEE, 152–156.
Liu, H., & Orban, D. (2008). “GridBatch: Cloud Computing for Large-Scale Data-Intensive Batch Applications”. Eighth IEEE International Symposium on Cluster Computing and the Grid, 295–305.
Maheswaran, M., Ali, S., Siegel, H. J., Hensgen, D., & Freund, R. F. (1999). “Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems”. Journal of Parallel and Distributed Computing, 59, 107–131.
Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y. C., Talbi, E. G., Zomaya, A. Y., & Tuyttens, D. (2011). “A Parallel Bi-Objective Hybrid Metaheuristic for Energy-Aware Scheduling for Cloud Computing Systems”. Journal of Parallel and Distributed Computing, Elsevier, 71, 1497–1508.
Microsoft’s Windows Azure, https://www.windowsazure.com/en-us/, Accessed on 31st March 2014.
Nathani, A., Chaudhary, S., & Somani, G. (2012). “Policy Based Resource Allocation in IaaS Cloud”. Future Generation Computer Systems, Elsevier,, 28, 94–103.
Panda, S. K., & Jana, P. K. (2014). “An Efficient Task Scheduling Algorithm for Heterogeneous Multi-cloud Environment”. 3rd International Conference on Advances in Computing, Communications & Informatics, IEEE, 1204–1209.
Panda, S. K., & Jana, P. K. (2015a). “Efficient Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment”. The Journal of Supercomputing, 71(Issue 4), 1505–1533.
Panda, S. K., & Jana, P. K. (2015b). “A Multi-Objective Task Scheduling Algorithm for Heterogeneous Multi-cloud Environment”, International Conference on Electronic Design. Computer Networks and Automated Verification, IEEE, 82–87.
Ramezani, F., Lu, J., & Hussain, F. (2013). “Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization”. 11th International Conference on Service Oriented Computing, Lecture Notes in Computer Science, 8274, 237–251.
Ramezani, F., Lu, J., & Hussain, F. (2014). “Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization”. International Journal of Parallel Programming, Springer, 42, 739–754.
Rimal, B. P., Choi, E., & Lumb, I. (2009). “A Taxonomy and Survey of Cloud Computing Systems”. Fifth International Joint Conference on INC, IMS and IDC, IEEE, 44–51.
Rimal, B. P., Jukan, A., Katsaros, D., & Goeleven, Y. (2010). “Architectural Requirements for Cloud Computing Systems: An Enterprise Cloud Approach”. Journal of Grid Computing, Springer, 9, 3–26.
Shalabi, L. A., Shaaban, Z., & Kasasbeh, B. (2006). “Data Mining: A Preprocessing Engine”. Journal of Computer Science, 2, 735–739.
Sotomayor, B., Keahey, K., & Foster, I. (2008). “Combining Batch Execution and Leasing Using Virtual Machines”. 17th International Symposium on High Performance Distributed Computing, ACM, 87–96.
Sotomayor, B., Montero, R. S., Llorente, I. M., & Foster, I. (2011). “Resource Leasing and the Art of Suspending Virtual Machines”. 11th IEEE International Conference on High Performance Computing and Communications, 59–68.
Three-sigma Rule of Thumb, http://en.wikipedia.org/wiki/68-95-99.7_rule, Accessed on 31st March 2014.
Topcuoglu, H., Hariri, S., & Wu, M. (2002). Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on Parallel and Distributed Systems, 13(3), 260–274.
Wu, C., Chang, R., & Chan, H. (2014). “A Green Energy-Efficient Scheduling Algorithm using the DVFS Technique for Cloud Datacenters”. Future Generation Computer Systems, Elsevier, 37, 141–147.
Xhafa, F., Barolli, L., & Durresi, A. (2007a). “Batch Mode Scheduling in Grid Systems”. International Journal of Web and Grid Services, 3(1), 19–37.
Xhafa, F., Carretero, J., Barolli, L., & Durresi, A. (2007b). “Immediate Mode Scheduling in Grid Systems”. International Journal of Web and Grid Services, 3(2), 219–236.
Zeng, L., Veeravalli, B., & Zomaya, A. Y. (2015). “An Integrated Task Computation and Data Management Scheduling Strategy for Workflow Applications in Cloud Environments”. Journal of Network and Computer Applications, 50, 39–48.
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Panda, S.K., Jana, P.K. Normalization-Based Task Scheduling Algorithms for Heterogeneous Multi-Cloud Environment. Inf Syst Front 20, 373–399 (2018). https://doi.org/10.1007/s10796-016-9683-5
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DOI: https://doi.org/10.1007/s10796-016-9683-5