Equality Workloads with Priority Based Association in the Cloud

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)
 
© 2014 by IJCTT Journal
Volume-16 Number-2
Year of Publication : 2014
Authors : Raju Goud , Bhaludra Raveendranadh Singh , Moligi Sangeetha

MLA

Raju Goud , Bhaludra Raveendranadh Singh , Moligi Sangeetha. "Equality Workloads with Priority Based Association in the Cloud". International Journal of Computer Trends and Technology (IJCTT) V16(2):40-44, Oct 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The complex applications are attracted by cloud computing is increased in day to day manner to run in remote data centers. Many applications needs parallel processing capabilities. The nature of parallel application is decrease the utilization of CPU resources as parallelism grows, because of the communication and synchronization between parallel processes. It challenging task but important for the data centers to reach a certain level of utilization of its nodes at the time of maintaining the level of responsiveness of parallel jobs. The existing parallel scheduling mechanisms take irresponsibleness as the top important and need nontrivial effort to make them work for the data centers in the cloud era. In this we introduced a parallel priority based technique to consolidate parallel workload in the cloud. We influence virtualization technology to partition the computing capacity of every node into two tiers, the fore virtual machine (VM) tier (with high CPU priority) and the background VM tier (with low CPU priority). They provided scheduling algorithms for parallel jobs to make effective utilization of the two tier VMs to improve the responsiveness of these jobs. Our wide range experiments display that our parallel scheduling algorithm expressively outperforms commonly used algorithms such as extensible Argonne scheduling system in a data center setting. This technique is practically and experimentally effective for consolidating parallel workload in data centers.

References
[1] D. Feitelson, A Survey of Scheduling in Multiprogrammed Parallel Systems. IBM TJ Watson Research Center, 1994.
[2] J. Hamilton, “Cloud Computing Economies of Scale,” Proc. AWS Genomics Cloud Computing Workshop, http://www.mvdirona. com/jrh/Talk sandpapers/JamesHamilton_GenomicsCloud 20100608.pdf, 2010.
[3] L. Barroso and U. Holzle, “The Case for Energy-Proportional Computing,” Computer, vol. 40, no. 12, pp. 33-37, Dec. 2007.
[4] “High Performance Computing (HPC) on AWS,”Amazon Inc., http://aws.amazon.com/hpc-applications/, 2011.
[5] J. Jones and B. Nitzberg, “Scheduling for Parallel Supercomputing: A Historical Perspective of Achievable Utilization,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 1-16, 1999.
[6] D. Feitelson and B. Nitzberg, “Job Characteristics of a Production Parallel Scientific Workload on the Nasa Ames ipsc /860,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 337- 360, 1995.
[7] U. Schwiegelshohn and R. Yahyapour, “Analysis of First-Come- First-Serve Parallel Job Scheduling,” Proc. Ninth Ann. ACM-SIAM Symp. Discrete Algorithms, pp. 629-638, 1998.
[8] L.G. Valiant, “A Bridging Model for Parallel Computation,” Comm. ACM, vol. 33, no. 8, pp. 103-111, 1990.
[9] D. Feitelson and M. Jettee, “Improved Utilization and Responsiveness with Gang Scheduling,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 238-261, 1997.
[10] D. Lifka, “The Anl/Ibm SP Scheduling System,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 295-303, 1995.
[11] Y. Lin, “Parallelism Analyzers for Parallel Discrete Event Simulation,” ACM Trans. Modeling and Computer Simulation, vol. 2, no. 3, pp. 239-264, 1992.
[12] R. Fujimoto, “Parallel and Distributed Simulation,” Proc. 31st Conf. Winter Simulation: Simulation—A Bridge to the Future, vol. 1, pp. 122-131, 1999.
[13] Z. Juhasz, S. Turner, K. Kuntner, and M. Gerzson, “A Performance Analyser and Prediction Tool for Parallel Discrete Event Simulation,” J. Simulation, vol. 4, no. 1, pp. 7-22, 2003.
[14] R. Fujimoto, A. Malik, and A. Park, “Parallel and Distributed Simulation in the Cloud,” Int’l Simulation Magazine, Soc. For Modeling and Simulation, vol. 1, no. 3, 2010.
[15] G. D’Angelo, “Parallel and Distributed Simulation from Many Cores to the Public Cloud,” Proc. Int’l Conf. High Performance Computing and Simulation (HPCS), pp. 14-23, 2011. 1882 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 9, SEPTEMBER 2013

Keywords
cloud computing, consolidation, scheduling technique, parallel priority.