Survey on Scheduling Algorithms in Cloud Computing

Authors

  • A. Sathya Sofia PSNA College of Engg and Tech, Dindigul, Tamil Nadu, India
  • M. Backialakshmi PSNA College of Engg and Tech, Dindigul, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajes-2014.3.2.1930

Keywords:

Scheduling, Cloud computing, Resource allocation, Efficiency

Abstract

Cloud computing is a general term used to describe a new class of network based computing that takes place over the internet. The primary benefit of moving to Clouds is application scalability. Cloud computing is very beneficial for the application which are sharing their resources on different nodes. Scheduling the task is quite a challenging in cloud environment. Usually tasks are scheduled by user requirements. New scheduling strategies need to be proposed to overcome the problems proposed by network properties between user and resources. New scheduling strategies may use some of the conventional scheduling concepts to merge them together with some network aware strategies to provide solutions for better and more efficient job scheduling. Scheduling strategy is the key technology in cloud computing. This paper provides the survey on scheduling algorithms. There working with respect to the resource sharing. We systemize the scheduling problem in cloud computing, and present a cloud scheduling hierarchy.

References

C.-M. Wu, R.-S. Chang, and H.-Y. Chan, "A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters," Future Generation Computer Systems, vol. 37, pp. 141-147, 2014.

X. Wang, Y. Wang, and Y. Cui, "A new multi-objective bilevel programming model for energy and locality aware multi-job scheduling in cloud computing," Future Generation Computer Systems, vol. 36, pp. 91-101, 2014.

S. Su et al., "Cost-efficient task scheduling for executing large programs in the cloud," Parallel Computing, vol. 39, pp. 177-188, 2013.

S. Patel and U. Bhoi, "Priority Based Job Scheduling Techniques In Cloud Computing," Int. J. Sci. Technol. Res., vol. 2, no. 11, pp. 2277-8616, Nov. 2013.

Y. Feng et al., "CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy aware cloud service scheduling," Appl. Soft Comput., vol. 19, pp. 264-279, 2014.

C. Lin and S. Lu, "Scheduling Scientific Workflows Elastically for Cloud Computing," in Proc. IEEE 4th Int. Conf. Cloud Comput., 2011.

B. Xu et al., "Job scheduling algorithm based on Berger model in cloud environment," Adv. Eng. Software, vol. 42, pp. 419-425, 2011.

X. Kong et al., "Efficient dynamic task scheduling in virtualized data centers with fuzzy prediction," J. Netw. Comput. Appl., vol. 34, pp. 1068-1077, 2011.

A. Nathani et al., "Policy based resource allocation in IaaS cloud," Future Gener. Comput. Syst., vol. 28, pp. 94-103, 2012.

D. Babu et al., "Honey bee behavior inspired load balancing of tasks in cloud computing environments," Appl. Soft Comput., vol. 13, pp. 2292-2303, 2013.

L. Lu et al., "Morpho: A decoupled MapReduce framework for elastic cloud computing," Future Gener. Comput. Syst., vol. 36, pp. 80-90, 2014.

C. Liu et al., "CCBKE - Session key negotiation for fast and secure scheduling of scientific applications in cloud computing," Future Gener. Comput. Syst., vol. 29, pp. 1300-1308, 2013.

Y. Lai et al., "A Ranking Chaos Algorithm for dual scheduling of cloud service and computing resource in private cloud," Computers in Industry, vol. 64, pp. 448-463, 2013.

M. Gahlawat and P. Sharma, "Analysis and Performance Assessment of CPU Scheduling Algorithms in Cloud using Cloud Sim," Int. J. Appl. Inf. Syst., vol. 5, no. 9, July 2013.

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Published

09-09-2014

How to Cite

Sathya Sofia, A., & Backialakshmi, M. (2014). Survey on Scheduling Algorithms in Cloud Computing. Asian Journal of Electrical Sciences, 3(2), 27–33. https://doi.org/10.51983/ajes-2014.3.2.1930