Abstract
With the advances in virtualization technology, cloud has become the most powerful and promising platform for business, academia, public and government organizations. The cloud users do not require to maintain any IT infrastructure such as hardware, software and network resources in their premises. They can rent the services on demand from anywhere in the world just by paying for that service. In cloud computing, task allocation is a well-known problem. Many algorithms have been developed for the same. However, task allocation in a heterogeneous multi-cloud environment is much more challenging due to the dynamic nature of the cloud resources. In this paper, we present an algorithm, called uncertainty-based quality of service (QoS) Min–Min (UQMM) algorithm which considers QoS based on uncertainty parameters in heterogeneous multi-cloud environment. To the best of our knowledge, this is the first paper which deals with the task allocation problem with uncertainty-based QoS in a heterogeneous multi-cloud systems. We perform extensive simulations on the proposed algorithm using benchmark as well as synthetic datasets and measure performance in terms of various metrics. The results are compared with the popular cloud min–min scheduling, cloud min–max normalization and smoothing-based task scheduling algorithm to show the effectiveness of the proposed algorithm. Moreover, we evaluate the results using two statistical tests, namely t test and ANOVA.
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Furht, B.; Escalante, A.(eds): Handbook of Cloud Computing. Springer, ISBN 978-1-4419-6523-3 (2010)
Amazon Web Services. http://aws.amazon.com/. Accessed 11 April 2015
IBM Cloud. http://www.ibm.com/cloud-computing/in/en/. Accessed 11 April 2015
Microsoft Azure. http://azure.microsoft.com/en-us/. Accessed 15 April 2015
Cloud Consulting. http://www.cloudconsulting.com/saas/. Accessed 29 April 2015
Amazon Web Services (AWS). http://en.wikipedia.org/wiki/Amazon_Web_Services. Accessed 30 April 2015
Montage: An Astronomical Image Mosaic Engine. http://montage.ipac.caltech.edu/index.html. Accessed 30 April 2015
Southern California Earthquake Center. http://www.scec.org/. Accessed 30 April 2015
Illumina. http://www.illumina.com/. Accessed 30 April 2015
Brown D.A., Brady P.R., Dietz A., Cao J., Johnson B., McNabb J.: A Case Study on the Use of Workflow Technologies for Scientific Analysis: Gravitational Wave Data Analysis. Workflows for E-Science, pp. 39–59. Springer, Berlin (2007)
Livny J., Teonadi H., Livny M., Waldor M.K.: High-throughput, kingdom-wide prediction and annotation of bacterial non-coding RNAs. PLoS One 3(9), 1–12 (2008)
Ullman J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)
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.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)
Maheswaran M., Ali S., Siegel H.J., Hensgen D., Freund R.F.: Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 59, 107–131 (1999)
Ibarra O.H., Kim C.E.: Heuristic algorithms for scheduling independent tasks on nonidentical processors. J. Assoc. Comput. Mach. 24(2), 280–289 (1977)
Armstrong, R.; Hensgen, D.; Kidd, T.: The Relative Performance of Various Mapping Algorithms is Independent of Sizable Variances in Run-time Predictions. In: 7th IEEE Heterogeneous Computing Workshop, pp. 79–87 (1998)
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.: Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet. In: 7th IEEE Heterogeneous Computing Workshop, pp. 184–199 (1998)
Topcuoglu H., Hariri S., Wu M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)
Bajaj R., Agrawal D.P.: Improving scheduling of tasks in a heterogeneous environment. IEEE Trans. Parallel Distrib. Syst. 15(2), 107–118 (2004)
Li J., Qiu M., Ming Z., Quan G., Qin X., Gu Z.: Online optimization for scheduling preemptable tasks on IaaS cloud system. J. Parallel Distrib. Comput. 72, 666–677 (2012)
Wen, H.; Hai-ying, Z.; Chuang, L.; Yang, Y.: Effective Load Balancing for Cloud-based Multimedia System. In: International Conference on Electronic and Mechanical Engineering and Information Technology, pp. 165–168 (2011)
Wang, S.; Yan, K.; Liao, W.; Wang, S.: Towards a Load Balancing in a Three-level Cloud Computing Network. In: 3rd IEEE International Conference on Computer Science and Information Technology, Vol. 1, pp. 108–113 (2010)
Ergu D., Kou G., Peng Y., Shi Y., Shi Y.: The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J. Supercomput. 64, 835–848 (2013)
Rai, A.; Bhagwan, R., Guha, S.: Generalized Resource Allocation for the Cloud. In: 3rd ACM Symposium on Cloud Computing (2012)
Bozdag D., Ozguner F., Catalyurek U.: Compaction of schedules and a two-stage approach for duplication-based DAG scheduling. IEEE Trans. Parallel Distrib. Syst. 20(6), 857–871 (2009)
Xu Y., Hu H., Yihe S.: Data dependence graph directed scheduling for clustered VLIW architectures. IEEE Tsinghua Sci. Technol. 15(3), 299–306 (2010)
Bittencourt L.F., Madeira E.R.M., Fonseca N.L.S.D.: Scheduling in hybrid clouds. IEEE Commun. Mag. 50(9), 42–47 (2012)
Xhafa F., Carretero J., Barolli L., Durresi A.: Immediate mode scheduling in grid systems. Int. J. Web Grid Serv. 3(2), 219–236 (2007)
Xhafa F., Barolli L., Durresi A.: Batch mode scheduling in grid systems. Int. J. Web Grid Serv. 3(1), 19–37 (2007)
Panda S.K., Jana P.K.: Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J. Supercomput. 71(4), 1505–1533 (2015)
Panda, S.K.; Nag, S.; Jana, P.K.: A smoothing based task scheduling algorithm for heterogeneous multi-cloud environment. In: Third IEEE International Conference on Parallel, Distributed and Grid Computing, IEEE, pp. 62–67 (2014)
Panda, S.K.; Jana, P.K.: An Efficient Resource Allocation Algorithm for IaaS Cloud. In: 11th International Conference on Distributed Computing and Internet Technology. Lecture Notes in Computer Science, Springer, vol. 8956, pp. 351–355 (2015)
Cisco: Quality of Service. http://www.cisco.com/c/en/us/products/ios-nx-os-software/quality-of-service-qos/index.html. Accessed 12 May 2015
He X.S., Sun X.H., Von Laszewski G.: QoS guided min-min heuristic for grid task scheduling. J. Comput. Sci. Technol. 18(4), 442–451 (2003)
Decai H., Yuan Y., Li-jun Z., Ke-qin Z.: Research on tasks scheduling algorithms for dynamic and uncertain computing grid based on a+bi connection number of SPA. J. Softw. 4(10), 1102–1109 (2009)
F. N. Braun. https://code.google.com/p/hcsp-chc/source/browse/trunk/AE/ProblemInstances/HCSP/Braun_et_al/u_c_hihi.0?r=93. Accessed 9th May 2015
Calyam P., Rajagopalan S., Seetharam S., Selvadhurai A., Salah K., Ramnath R.: VDC-analyst: design and verification of virtual desktop cloud resource allocations. Comput. Netw. 68, 110–122 (2014)
Al-Haidari, F.; Sqalli, M.; Salah, K.: Impact of CPU Utilization Thresholds and Scaling Size on Autoscaling Cloud Resources. In: 5th IEEE International Conference on Cloud Computing Technology and Science, vol. 2, pp. 256–261 (2013)
Salah K., Calero J.M.A., Bernabe J.B., Perez J.M.M., Zeadally S.: Analyzing the security of windows 7 and Linux for cloud computing. Comput. Secur. 34, 113–122 (2013)
Salah K., Calero J.M.A., Zeadally S., Al-Mulla S., Alzaabi M.: Using cloud computing to implement a security overlay network. IEEE Secur. Priv. 11(1), 44–53 (2013)
Al-Qawasmeh A.M., Maciejewski A.A., Wang H., Smith J., Siegel H.J., Potter J.: Statistical measures for quantifying task and machine heterogeneities. J. Supercomput. 57, 34–50 (2011)
Miriam D.D.H., Easwarakumar K.S.: SPA-based task scheduling for hypercubic P2P grid systems. Int. J. Commun. Netw. Distrib. Syst. 9, 117–139 (2012)
Foster, I.; Zhao, Y.; Raicu, I.; Lu, S.: Cloud Computing and Grid Computing 360-Degree Compared. In: Workshop on Grid Computing, Environment, pp 1–10 (2008)
Ardagna D., Casale G., Ciavotta M., Perez J.F., Wang W.: Quality-of-service in cloud computing: modeling techniques and their applications. J. Internet Serv. Appl. 5, 1–17 (2014)
Abdelmaboud A., Jawawi D.N.A., Ghani I., Elsafi A., Kitchenham B.: Quality of service approaches in cloud computing: a systematic mapping study. J. Syst. Softw. 101, 159–179 (2015)
Chen J., Abedin F., Chao K., Godwin N., Li Y., Tsai C.: A hybrid model for cloud providers and consumers to agree on QoS of cloud services. Future Gener. Comput. Syst. 50, 38–48 (2015)
Javadi B., Abawajy J., Buyya R.: Failure-aware resource provisioning for hybrid cloud infrastructure. J. Parallel Distrib. Comput. 72, 1318–1331 (2012)
Muhuri P.K., Shukla K.K.: Real-time task scheduling with fuzzy uncertainty in processing times and deadlines. Appl. Soft Comput. 8, 1–13 (2008)
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.: Open Cirrus: A Global Cloud Computing Testbed. In: IEEE Computer Society, pp. 35–43 (2010)
Greenberg A., Hamilton J., Maltz D.A., Patel P.: The cost of cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2009)
Ali, S.; Siegel, H.J.; Maheswaran, M.; Hensgen, D.; Ali, S.: Task Execution Time Modeling for Heterogeneous Computing Systems. In: 9th Heterogeneous Computing Workshop, IEEE Computer Society, pp. 185-200 (2000)
Ott R.L., Longnecker M.: An Introduction to Statistical Methods and Data Analysis, 6th Edition. Duxbury Press, Boston (2010)
Muller K.E., Fetterman B.A.: Regression and ANOVA: An Integrated Approach Using SAS Software. SAS Publisher, Cary (2002)
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Panda, S.K., Jana, P.K. Uncertainty-Based QoS Min–Min Algorithm for Heterogeneous Multi-cloud Environment. Arab J Sci Eng 41, 3003–3025 (2016). https://doi.org/10.1007/s13369-016-2069-7
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DOI: https://doi.org/10.1007/s13369-016-2069-7