Cloud computing is a new computing technology
which is developing drastically. Scheduling becomes more
crucial and essential in this pay as you go model. Analyzing and
evaluating the performance of various heuristics and Meta
heuristics scheduling algorithms is a crucial work in this large
scale distributed systems. Though various scheduling algorithms
exist, the paper exposes a comparative analysis and performance
of 2 soft computing algorithms in cloud computing. The
algorithms considered are Bee Colony Optimization (BCO), and
Particle Swarm Optimization (PSO). The algorithms performance
is evaluated using cloudsim simulator to provide Quality of
Service (QoS) in this task to resource mapping. The measures
considered for evaluation are makespan and resource utilization.
R. Jemina Priyadarsini : Department of Computer science, St. Joseph’s College, Trichirapalli,
Tamil Nadu, 620002, India
Dr. L. Arockiam : Associate Professor, Department, of Computer science, St. Joseph’s College, Trichirapalli,
Tamil Nadu, 620002, India
Cloud Computing
Task Scheduling
Makespan
Resource Utilization
Bee Colony Optimization (BCO),
Particle
Swarm Optimization (PSO).
As the number of cloud users increase with their increase
in needs, a good scheduling algorithm is needed to
improve the performance. For performance evaluation, we
have considered 2 task scheduling soft computing
algorithms namely BCO and PSO. The results were also
compared with other two heuristics algorithms namely
MinMin and MaxMin. The algorithms performance have
been evaluated using cloudsim simulator . We found that
the Bee Colony Optimization (BCO) gives optimized
makespan with better resource utilization. This leads to a
need for further optimization and improvement of the
solution by providing proper fitness criteria. Also
hybridization may lead to better performance. Thus our
future work focus on hybrid optimization for efficient
Meta task scheduling.
[1] Mayur S Pilavare and Amish Desa i, "A Survey Of
Soft Computing Techniques Based Load Balancing In
Cloud Computing", International Journal Of Computer
Applications (IJCA),(0975-8887), Vol. 110,No
14,January 2015,pp.22-25
[2] Vijaypal S R., Pateriya R R., Rajeev K G,"Survey on
load balancing through virtual machine scheduling in
cloud computing environment", international journal of
cloud computing and services science, (IJ-CLOSER),
Vol.3, No.1,February 2014,pp.37-43.
[3] Gunvr Kaur and Sugandha Sharma, "Research Paper
on Optimization of Resources Using PSO and
Improived Particle Swarm Optimization (IPSO)
Algorithms in Cloud Computing", International Journal
of Advanced Research in Computer Science &
Technology (IJARCST), Vol.2, June 2014 ,pp. 499-
505.
[4] L. Guo, S. Zhao, S. Shen and C. Jiang, C, "Task
Scheduling Optimization in Cloud Computing Based
on Heuristic Algorithm", Journal Of Networks, Vol. 7,
No. 3, March 2012, pp. 547-553.
[5] Chen, H., Wang, F., Helian, N., and Akanmu, G.
(2013, February)." User-priority guided Min-Min
scheduling algorithm for load balancing in cloud
computing", National Conference on Parallel
Computing Technologies (PARCOMPTECH), 2013
pp. 1-8. IEEE.
[6] S. Mohana Priya and B. Subramani,” A New Approach
For Load Balancing In Cloud Computing”
International Journal Of Engineering And Computer
Science( IJEACS ),ISSN:2319-7242,Volume 2 Issue 5
May,2013 pp.1636-1640.
[7] Arash Delvar and Yalda Aryan , "A scheduling
heuristics algorithm for independent task scheduling in
cloud systems ", (IJCSI) International Journal of
Computer Science Issues, Vol. 8, Issue 6, No 2,
November 2011,pp.289-295.
[8] Sourav Banerjee, Mainak Adhikari and Utpal Biswas,
"Advanced Task Scheduling for Cloud Service
Provider Using Genetic Algorithm", IOSR Journal of
Engineering, Vol. 2, No. 7, 2012, pp. 141-147.
[9] Pop, F., Cristea, V., Bessis, N., and Sotiriadis, S.,
"Reputation guided Genetic Scheduling Algorithm for
Independent Tasks in Inter-Clouds Environments", In
Proceedings of 27th IEEE International Conference on
Advanced Information Networking and Applications
Workshops, 2013,pp.772-776.
[10] Linan Zhu ,Qingshui Li, and Lingna He, "Study on
Cloud Computing Resource Scheduling Stratergy
Based On Ant Colony Optimization Algorithm ",
International Journal of Computer Science Issues
(IJCSI),Vol 9, Issue 5, September 2012.
[11] Pinal Salot , “A survey of various scheduling algorithm
in cloud computing environment”, International
Journal Of Research Engineering and Technology
(IJRET),February 2013,Vol 2,pp131-135
[12] Bitam, S., "Bees Life Algorithm for Job Scheduling in
Cloud Computing", In Proceedings of the Third International Conference on Communications and
Information Technology, 2012, pp. 186-191.
[13] Mizan, T., Masud, S.M.R.A., Latip, R., “Modified
Bees Life Algorithm for Job Scheduling in Hybrid
Cloud”, International Journal of Engineering and
Technology Volume 2 No. 6, June, 2012, 974-979.
[14] Pandey, S., Wu, L., Guru, S.M., and Buyya, R, "A
Particle Swarm Optimization-based Heuristic for
Scheduling Workflow Applications in Cloud
Computing Environments", In Proceedings of 24th
IEEE International Conference on Advanced
Information Networking and Applications,2010,
pp.400-407
[15] Shaobin Zhan and Hongying HUo, “Improved PSO –
based Task Scheduling Algorithm in Cloud
Computing”, Journal of Information and
Computational Science, 2012 November, pp.3821-
3829.
[16] Salim Bitam, "Bees Life Algorithm for Job Scheduling
in Cloud Computing", Proceedings of The Third
International Conference on Communications and
Information Technology, 2012, pp. 186-191.
[17] J. Kennedy and R. Eberhart. Particle swarm
optimization.In IEEE International Conference on
Neural Networks, volume4, pages 1942–1948, 1995.
[18] W. Qing, and Z. Han-Chao, "Optimization of Task
Allocation And Knowledge Workers Scheduling
Based-on Particle Swarm Optimization, "In
Proceedings of IEEE International Conference on
Electric Information and Control Engineering, 2011,
pp. 574-578.
[19] Jemina Priyadarsini R and Arockiam L, “Performance
Evaluation of Min-Min and Max-Min algorithms for
job scheduling in federated cloud”, International
Journal of Computer Applications (IJCA),Vol.
99,Number 18 August 2014, ISSN: 0975-8887, pp. 47-
54.(IF 0.824)
