Open Access   Article

Performance analysis of Fuzzy VM Management techniques for Task scheduling on Cloud systems

R.A. Kulkarni1 , S.B. Patil2 , N. Balaji3

1 Comp.Dept,PICT,Pune University,Pune,India.
2 CSE Dept, BVCOE, BV university,PUNE, India.
3 ECE Dept, JNTU Kakinada University,COE, VIZIANAGARAM, INDIA.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-4 , Page no. 14-19, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i4.1419

Online published on Apr 30, 2018

Copyright © R.A. Kulkarni, S.B. Patil, N. Balaji . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: R.A. Kulkarni, S.B. Patil, N. Balaji, “Performance analysis of Fuzzy VM Management techniques for Task scheduling on Cloud systems”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.14-19, 2018.

MLA Style Citation: R.A. Kulkarni, S.B. Patil, N. Balaji "Performance analysis of Fuzzy VM Management techniques for Task scheduling on Cloud systems." International Journal of Computer Sciences and Engineering 6.4 (2018): 14-19.

APA Style Citation: R.A. Kulkarni, S.B. Patil, N. Balaji, (2018). Performance analysis of Fuzzy VM Management techniques for Task scheduling on Cloud systems. International Journal of Computer Sciences and Engineering, 6(4), 14-19.

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Abstract

Cloud Computing has been widely adopted by many industries as a platform to support distributed applications. Cloud provides the advantages of reduced operation costs, flexible system configuration and elastic resource provisioning. Even though cloud has been rapidly getting adopted there are various open challenges in areas such as management of virtual resources, security and organizational issues. One of the prominent technologies used by cloud computing is the virtualization. The virtualization technology faces tremendous challenges in supporting real-time applications on cloud as these applications demand real-time performance in open, shared and virtualized computing environments. In this paper we are analyzing the usage of fuzzy logic in improving the performance of time constrained tasks. Our proposed system makes use of fuzzy logic in scheduling of tasks to Virtual machines and in identification of destination host in migrating the overloaded virtual machines which can give better performance than the traditional scheduling algorithms used on cloud systems.

Key-Words / Index Term

: Cloud Computing, Fuzzy logic, VM management, Performance metrics.

References

[1] P. Mell and T. Grance, “The NIST Definition of Cloud
Computing,” US Nat’l Inst. of Science and Technology, 2011;
http://csrc.nist.gov/publications/nist pubs/800-145/SP800-
145.pdf.
[2] Ehab NabielAlkhanak, Sai Peck Lee, Saif Ur Rehman Khan,
“Cost- aware challenges for workflow scheduling
approaches in cloud computing environments: Taxonomy and
opportunities”,Future Generation Computer Systems,ElseVier 50
(2015) 3–21
[3] Marisol García-Valls, Tommaso Cucinotta, Chenyang
Lu“Challenges in real-time virtualization and predictable cloud
computing”.
[4] Mohammad A H, Monil and Rashedur M. R,”VM consolidation
approach based on heuristics, fuzzy logic, and migration control”
Journal of Cloud Computing: Advances, Systems and
Applications (2016) 5:8DOI 10.1186/s13677-016-0059-7
[5] Ehab NabielAlkhanak, Sai Peck Lee, Saif Ur Rehman Khan,
“Cost-aware challenges for workflow scheduling approaches in
Cloud computing environments: Taxonomy and
opportunities”,Future Generation Computer Systems,ElseVier 50
(2015) 3–21
[6] D. Chitra Devi and V. RhymendUthariaraj “Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks”,The Scientific World Journal Volume 2016, Article ID 3896065, 14 pages
[7] Chun-Wei Tsai,Wei-chang Huang, M-S Chieng,Ming-chao Chiang and Chu-Sing Yang,” A Hyper-heuristic Scheduling Algorithm for Cloud” ,IEEE transactions on cloud computing Vol-2 No2 April -June 2014.
[8] M.M.M. Fahmy, “A fuzzy algorithm for scheduling non-periodic job on soft real-time single processor system” ,Ain Shams Engineering
Journal (2010) 1, 31–38
[9] Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, César A. F. De Rose, and R Buyy,“ CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning”, Algorithms Software: Practice and Experience (SPE), Volume 41, Number 1, Pages: 23-50, ISSN: 0038-0644, Wiley Press, New York, USA, January, 2011.
[10] JawwadShamsi,• Muhammad Ali Khojaye • Mohammad Ali Qasmi“Data-Intensive Cloud Computing: Requirements, Expectations,
Challenges, and Solutions “, J Grid Computing (2013) 11:281–310 DOI 10.1007/s10723-013-9255-6
[11] Brendan Jennings Rolf Stadler “ Resource Management in Clouds: Survey and Research Challenges “ J NetwSyst Manage DOI
10.1007/s10922-014-9307-7
[12] Fei Teng, Frédéric Magoulès • Lei Yu • Tianrui Li “ A novel real-time scheduling algorithm and performance analysis of a MapReduce-based cloud “,J Supercomput (2014) 69:739–765 DOI 10.1007/s11227-014-1115-z
[13] Avtar Singh and Kamlesh Dutta “ A novel real-time scheduling
algorithm. and performance analysis of a MapReduce-based cloud” IEEK Transactions on Smart Processing and Computing, vol. 2, no. 6,December 2013.
[14] Tom Springer, Steffen Peter Tony Givargis “Fuzzy Logic Based
Adaptive HierarchicalScheduling for Periodic Real-Time Tasks”,
EWiLi’15, October 8th, 2015, Amsterdam, The Netherlands
[15] Kai Hwang , Xiaoying Bai , Yue Shi , Muyang Li, Wen-Guang