Job Scheduling Based on Virtual Abstractions in Cloud

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-10 Number-7
Year of Publication : 2014
Authors : B.Parkavi , G.Malathy
  10.14445/22315381/IJETT-V10P267

MLA 

B.Parkavi , G.Malathy. "Job Scheduling Based on Virtual Abstractions in Cloud", International Journal of Engineering Trends and Technology (IJETT), V10(7),352-356 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

Cloud computing is a provisioning of services in a timely, on-demand manner, to allow the scaling up and down of resources. Job scheduling is one of the major issues in the public cloud which concerns availability of resources in the datacenter. Data center need to achieve certain level of utilization of its nodes while maintaining level of responsiveness of parallel jobs. Existing scheduling schemes make use of backfilling strategies which pre-empt shortest jobs to execute when jobs at head of the queue have unavailable of resources. This results in starvation of larger jobs, reduced throughput and underutilization of resources. In this paper, job scheduling based on virtual abstraction scheme is proposed for efficient scheduling of jobs in k- cloud data center with multiple computing capacities which solves large-scale static scheduling problem in cloud.

References

[1] Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I. ‘‘Cloud computing and emerging IT platforms: vision, hype and reality for delivering computing as the 5th utility’’, Future Gener. Comput. Syst., 25(6), pp. 599–616 (2009).
[2] Peter Mell, Timothy Grance, “The NIST Definition of Cloud Computing”, NIST (National Institute of Standards and Technology) Special Publication 800-145.
[3] 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.
[4] D. Feitelson and M. Jettee, “Improved Utilization and Responsiveness with Gang Scheduling,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 238-261, 1997.
[5] Y. Wiseman and D. Feitelson, “Paired Gang Scheduling,” IEEE Trans. Parallel and Distributed Systems, vol. 14, no. 6, pp. 581-592, June 2003.
[6] Y. Zhang, H. Franke, J. Moreira, and A. Sivasubramaniam, “An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration,” IEEE Trans. Parallel and Distributed Systems, vol. 14, no. 3, pp. 236-247, Mar. 2003.
[7] Xiaocheng Liu, Chen Wang, Bing Zhou, Junliang Chen, “Priority-Based Consolidation of Parallel Workloads in the Cloud”, IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 9, September 2013.
[8] Shachee V Parikh, RichaSinha, “Double Level Priority Based Task Scheduling with Energy Awareness in Cloud Computing”, International Journal of Engineering and Technology, 2011.
[9] D. Jackson, Q. Snell, and M. Clement, “Core Algorithms of the Maui Scheduler,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 87-102, 2001.
[10] J. Jann, P. Pattnaik, H. Franke, F. Wang, J. Skovira, and J. Riordan, “Modeling of Workload in Mpps,” Proc. Workshop Job Scheduling Strategies for Parallel Processing, pp. 95-116, 1997.
[11] 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.
[12] T. A. Henzinger, V. Singh, T.Wies, and D. Zufferey, “Scheduling large jobs by abstraction refinement”, in EuroSYS, pages 329–342, 2011.
[13] Thomas A. Henzinger, Anmol V. Singh, Vasu Singh, Thomas Wies, and Damien Zufferey, “Flex-PRICE: Flexible provisioning of resources in a cloud environment”, IEEE International Conference on Cloud Computing, 2010.

Keywords
Abstraction scheduler, cloud computing, parallel workload, virtual machine