Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters

  • Authors

    • S Gokuldev Amrita Vishwa Vidyapeetham,Mysuru Campus,India
    • Jathin R Amrita Vishwa Vidyapeetham,Mysuru Campus,India
    2018-06-01
    https://doi.org/10.14419/ijet.v7i2.9531
  • Cluster Monitor, Energy Efficiency, Monitoring Based Resource Scheduling, Node States, Small Size Clusters.

  • Abstract

    Performing scheduling of tasks with low energy consumption with high performance is one of the major concerns in distributed computing. Most of the existing systems have achieved improved energy efficiency but compromised with QoS metrics such as makespan and resource utilization. A resource scheduling strategy for wireless clusters is proposed by making careful considerations on decisions that would im-prove the battery life of nodes. The proposed strategy also incorporates monitoring system with in the clusters for optimizing the system performance as well as energy consumption. The system ensures “Any case zero loss" performance wherein each cluster will be monitored by at least one cluster monitor. This is implemented by using predictive calculation at each cluster monitor to communicate only if absolutely essential, during assigning jobs to resources, selecting optimal resources by assigning the jobs to the most power efficient resource among the available idle resources within the cluster. The experimental result ensures improved system performance with low power consumption in homogeneous computing environment. The resource sharing strategy is experimentally analyzed, considering the important performance metrics such as starvation deadline, turnaround time, miss hit count through simulations. Significant results were observed with improved efficiency.

     

     

  • References

    1. [1] Swamy, S. R., & Mandapati, S. (2017). A Fuzzy Energy and Security Aware Scheduling In Cloud. International Journal of Engineering & Technology, 7(1.2), 117-124. https://doi.org/10.14419/ijet.v7i1.2.9021.

      [2] Yang, X. J., Liao, X. K., Lu, K., Hu, Q. F., Song, J. Q., & Su, J. S. (2011). The Tianhe-1a Supercomputer: Its Hardware and Software. Journal of Computer Science and Technology, 26(3), 344-351. https://doi.org/10.1007/s02011-011-1137-8.

      [3] Rakesh, N., Shakir, M., Kalamani, P., & Maheswari, B. U. (2017, January). An Energy Saving Algorithm Using Heterogeneity Aware Protocol In Wireless Sensor Networks To Sustain Lifetime Of Nodes. In Inventive Systems and Control (ICISC), 2017 International Conference on (Pp. 1-5). IEEE.

      [4] Duy, T. V. T., Sato, Y., & Inoguchi, Y. (2010, April). Performance Evaluation Of A Green Scheduling Algorithm For Energy Savings In Cloud Computing. In Parallel & Distributed Processing, Workshops and Phd Forum (Ipdpsw), 2010 IEEE International Symposium on (Pp. 1-8). IEEE.

      [5] Pillai, A. S., Singh, K., Saravanan, V., Anpalagan, A., Woungang, I., & Barolli, L. (2017). A Genetic Algorithm-Based Method for Optimizing the Energy Consumption and Performance of Multiprocessor Systems. Soft Computing, 1-15.

      [6] Vasudevan, S. K., Anandaram, S., Menon, A. J., & Aravinth, A. (2016). A Novel Improved Honey Bee Based Load Balancing Technique In Cloud Computing Environment. Asian Journal of Information Technology, 15(9), 1425-1430.

      [7] Valentini, G. L., Lassonde, W., Khan, S. U., Min-Allah, N., Madani, S. A., Li, J., ... & Li, H. (2013). An Overview of Energy Efficiency Techniques in Cluster Computing Systems. Cluster Computing, 16(1), 3-15. https://doi.org/10.1007/s10586-011-0171-x.

      [8] Luo, L., Wu, W., Di, D., Zhang, F., Yan, Y., & Mao, Y. (2012, June). A Resource Scheduling Algorithm of Cloud Computing Based On Energy Efficient Optimization Methods. In Green Computing Conference (IGCC), 2012 International (Pp. 1-6). IEEE.

      [9] Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M. Q., & Pentikousis, K. (2010). Energy-Efficient Cloud Computing. The Computer Journal, 53(7), 1045-1051. https://doi.org/10.1093/comjnl/bxp080.

      [10] Lim, D., Ong, Y. S., Jin, Y., Sendhoff, B., & Lee, B. S. (2007). Efficient Hierarchical Parallel Genetic Algorithms Using Grid Computing. Future Generation Computer Systems, 23(4), 658-670. https://doi.org/10.1016/j.future.2006.10.008.

      [11] Pandey, S., Wu, L., Guru, S. M., & Buyya, R. (2010, April). A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. In Advanced Information Networking and Applications (Aina), 2010 24th IEEE International Conference on (Pp. 400-407). IEEE. https://doi.org/10.1109/AINA.2010.31.

      [12] Chen, W. N., & Zhang, J. (2009). An ant colony optimization approach to a grid workflow-scheduling problem with various QoS requirements. IEEE transactions on systems, man, and cybernetics, part c (applications and reviews), 39(1), 29-43. https://doi.org/10.1109/TSMCC.2008.2001722.

      [13] Ku-Mahamud, K. R., & Nasir, H. J. A. (2010, May). Ant colony algorithm for job scheduling in grid computing. In Mathematical/Analytical Modelling and Computer Simulation (AMS), 2010 Fourth Asia International Conference on (pp. 40-45). IEEE.

      [14] Shah, S. N. M., Mahmood, A. K. B., & Oxley, A. (2011). Dynamic multilevel hybrid scheduling algorithms for grid computing. Procedia Computer Science, 4, 402-411. https://doi.org/10.1016/j.procs.2011.04.042.

      [15] Xiao, J., Zhang, Y., Chen, S., & Yu, H. (2012, September). An application-level scheduling with task bundling approach for many-task computing in heterogeneous environments. In IFIP International Conference on Network and Parallel Computing (pp. 1-13). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35606-3_1.

      [16] Zhang, Y., Chen, S., & Hu, Z. (2013, June). A scheduling algorithm for many-task computing optimized for IO contention in heterogeneous grid environment. In Computational and Information Sciences (ICCIS), 2013 Fifth International Conference on (pp. 1541-1544). IEEE.

      [17] Rodriguez, M. A., & Buyya, R. (2014). Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE transactions on cloud computing, 2(2), 222-235. https://doi.org/10.1109/TCC.2014.2314655.

      [18] Thangamani, M. (2016). Grid Computing for Effective performance of Job Scheduling. Science and Management (ISJCRESM), 1(1).

      [19] Garg, S. K., & Buyya, R. (2009, December). Exploiting heterogeneity in grid computing for energy-efficient resource allocation. In Proceedings of the 17th International Conference on Advanced Computing and Communications.

      [20] Ponciano, L., & Brasileiro, F. (2010, October). On the impact of energy-saving strategies in opportunistic grids. In Grid Computing (GRID), 2010 11th IEEE/ACM International Conference on (pp. 282-289). IEEE. https://doi.org/10.1109/GRID.2010.5698003.

      [21] Liu, W., Li, H., Du, W., & Shi, F. (2011, August). Energy-aware task clustering scheduling algorithm for heterogeneous clusters. In Green Computing and Communications (GreenCom), 2011 IEEE/ACM International Conference on (pp. 34-37). IEEE. https://doi.org/10.1109/GreenCom.2011.14.

      [22] Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor Crummey, J., & Tallent, N. R. (2010). HPC Toolkit: Tools for performance analysis of optimized parallel programs. Concurrency and Computation: Practice and Experience, 22(6), 685-701.

      [23] Nesmachnow, S., Dorronsoro, B., Pecero, J. E., & Bouvry, P. (2013). Energy-aware scheduling on multicore heterogeneous grid computing systems. Journal of grid computing, 11(4), 653-680. https://doi.org/10.1007/s10723-013-9258-3.

      [24] Lin, J., Cheng, A. M., & Song, W. (2014). A practical framework to study low-power scheduling algorithms on real-time and embedded systems. Journal of Low Power Electronics and Applications, 4(2), 90-109. https://doi.org/10.3390/jlpea4020090.

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  • How to Cite

    Gokuldev, S., & R, J. (2018). Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters. International Journal of Engineering & Technology, 7(2), 837-841. https://doi.org/10.14419/ijet.v7i2.9531

    Received date: 2018-02-14

    Accepted date: 2018-05-10

    Published date: 2018-06-01