Abstract
In cloud computing, scheduling plays an eminent role while processing enormous jobs. The paralle jobs utmost need parallel processing capabilities which leads to CPU underutilization mainly due to synchronization and communication among parallel processes. Researchers introduced several algorithms for scheduleing parallel jobs namely, Conservative Migration Consolidation supported Backfilling (CMCBF) and Aggressive Migration Consolidation supported Backfilling (AMCBF). The greatest challenge of a existing scheduling algorithm is to improve the data center utilization without affecting job responsiveness. Hence, this work proposes an Effective Multiphase Scheduling Approach (EMSA) to process the jobs. In EMSA, the jobs are initially preprocessed and batched together to avoid starvation and to mitigate unwanted delay. Later, an Associate Priority Method has been proposed which prioritizes the batch jobs to minimize the number of migrations. Finally, the prioritized jobs are scheduled using Priority Scheduling with BackFilling algorithm to utilize the intermediate idle nodes. Moreover, the virtualization technology partitions the computing capacity of the Virtual Machine (VM) into two-tier VM as foreground VM (FVM) and Background VM (BVM) to improve node utilization. Hence, Priority Scheduling with Consolidation based BackFilling algorithm has been deployed in a two-tier VM that processes the jobs by utilizing the VMs effectively. Experimental results show that the performance of the proposed work performs better than other existing algorithms by increasing the resource utilization by 8%.
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References
Baraglia, R., Capannini, G., Dazzi, P., Pagano, G.: A multi-criteria job scheduling framework for large computing farms. J. Comput. Syst. Sci. 79(2), 230–244 (2013)
Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: challenges and opportunities. International Conference on High Performance Computing & Simulation, 1–11 (2009)
Chunxiao, L., Raghunathan, A., Jha, N.K.: A trusted virtual machine in an untrusted management environment. IEEE Trans. Serv. Comput. 5(4), 472–483 (2012)
Dinesh, K., Vijayalakshmi, M.: Deadline constrained adaptive multilevel scheduling system in cloud environment. KSII TIIS. 9(4), 1302–1320 (2015)
Dinesh, K., Poornima, G., Kiruthika, K.: Efficient resources allocation for different jobs. Int. J. Comput. Appl. 56(10), 30–35 (2012)
Frachtenberg, E., Feitelson, D.G., Petrini, F., Fernandez, J.: Adaptive parallel job scheduling with flexible coscheduling. IEEE TPDS. 16(11), 1066–1077 (2005)
Ghanbari, S., Othman, M.: A priority based job scheduling algorithm in cloud computing. Procedia Engineering. 50, 778–785 (2012)
Kangkang, L., Huanyang, Z., Jie, W.: Migration-based virtual machine placement in cloud systems. IEEE 2nd International Conference Cloud Networking (CloudNet), 83–90 (2013)
Lee, Y., Len, S., Chang, R.: Improving job scheduling algorithm in a grid environment. Futur. Gener. Comput. Syst. 27(8), 991–998 (2011)
Mell, P., Grance, T.: The NIST definition of cloud computing. NIST Special publication. 800–145 (2011)
Moschakis, I.A., Karatza, H.D.: Performance and cost evaluation of gang scheduling in a cloud computing system with job migrations and starvation handling. IEEE Symposium on Computers and Communications (ISCC), pp.418–423 (2011)
Palanisamy, B., Singh, A., Ling, L.: Cost-effective resource provisioning for MapReduce in a cloud. IEEE TPDS. 26(5), 1265–1279 (2015)
Schwiegelshohn, U., Yahyapour, R.: Analysis of first come first serve parallel job scheduling. Proc. Ninth Ann. ACM SIAM Symp. Discrete Algorithms, 629–638 (1998)
Silberschatz, A., Galvin, P. B., Gagne, G.: Operating System Concepts. 8th edition. John Wiley & Sons (2011)
Utilization of Data Center: http://www.uniassignment.com/essay-samples/information-technology/aws-as-a-recommended-cloud-platform-information-technology-essay.php
Xiaocheng, L.., Bin, C., Xiaogang, Q., Ying, C., Kedi, H.: Scheduling parallel jobs using migration and consolidation in the cloud. Math. Probl. Eng. Article ID 695757 (2012)
Xiaocheng, L., Wang, C., et al.: Backfilling under two-tier virtual machines. IEEE International Conference on Cluster Computing (CLUSTER). 514–522 (2012)
Xiaocheng, L., Xiaogang, Q., Bin, C., Kedi, H.: Cloud-based simulation: the state-of-the-art Computer simulation paradigm. ACM/IEEE/SCS 26th Workshop on Principles of Advanced and Distributed Simulation (PADS), 71–74 (2012)
Xiaocheng, L., Chen, W., Zhou, B.B., Junliang, C., Ting, Y., Zomaya, A.Y.: Priority-based consolidation of parallel workloads in the cloud. IEEE TPDS. 24(9), 1874–1883 (2013)
Xiaocheng, L., Yabing, Z., Quanjun, Y., Yong, P., Long, Q.: Scheduling parallel jobs with tentative runs and consolidation in the cloud. J. Syst. Softw. 104, 141–151 (2015)
Zhang, Y., Franke, H., Moreira, J., Sivasubramaniam, A.: An integrated approach to parallel scheduling using gang-scheduling, backfilling, and migration. IEEE Transactions on Parallel and Distributed Systems. 14(3), 236–247 (2003)
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This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data
Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell
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Komarasamy, D., Muthuswamy, V. Priority scheduling with consolidation based backfilling algorithm in cloud. World Wide Web 21, 1453–1471 (2018). https://doi.org/10.1007/s11280-018-0612-z
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DOI: https://doi.org/10.1007/s11280-018-0612-z