Author : Tanusshri Sivakumar 1
Date of Publication :22nd March 2018
Abstract: The most important problem in the cloud service provider is to maintain the elastic property of the cloud in such a way that user will pretend the cloud as limitless. So the challenge is how to make the limited sources unlimited. Every task must be granted what it requires by any mean otherwise it will degrade the performance of cloud. So resource allocation has a lot of solution. Resource allocation is a NP hard problem so no particular solution can perform well always. But these kinds of problems are solved by nature in many ways such that such as ant colony optimization (ACO) algorithm, particle swarm optimization (PSO) algorithm and firefly algorithm. In this paper a particle swarm optimization technique have been used to resolve the most critical problem of the cloud service provider at cloud data centre. This technique is basically taken from the collective and collaborative nature of the nature swarm. This technique can be used to allocate the resource to the task request by minimizing the makes span, flow time and task execution cost. The simulation and test results show the better efficiency than the other similar existing technique.
Reference :
-
- J. Mitola, III, G. Q. “Maguire, Cognitive radio: making software radiosmore personal,” IEEE Personal Communication, vol. 6, no. 4, pp. 13-18,1999.
- S. Haykin, “Cognitive radio: brain-empowered wirelesscommunications,” IEEE Journal on Selected Areas in Communications,vol. 23, no. 2, pp. 201-220, 2005
- A. Goldsmith, S. A. Jafar, I. Maric, S. Srinivasa, “Breaking spectrumgridlock with cognitive radios: an information theoretic perspective,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 79-89, 2007.
- T. Yucek, H. Arslan, “A survey of spectrum sensing algorithms forcognitive radio applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009.
- Y. Zeng, Y.-C. Liang, A.T. Hoang, R. Zhang, “A review on spectrumsensing for cognitive radio: challenges and solutions,” EURASIP Journalon Advances in Signal Processing, vol. 2010, no. 1, pp. 1-15, 2010.
- A.S. Cacciapuoti, I.F. Akyildiz, L. Paura, “Correlationaware userselection for cooperative spectrum sensing in cognitive radio Ad Hoc networks,” IEEE Journal on Selected Areas in Communications, vol. 30,no. 2, pp. 297-306, 2012.
- W. Yue, B. Zheng, Q. Meng, J. Cui, P. Xie, “Robust cooperative spectrumsensing schemes for fading channels in cognitive radio networks,” Science China Information Sciences, vol. 54, no. 2, pp. 348- 359, 2011.
- W. Xia, W. Yuan, W. Chen, W. Liu, S. Wang, J. Xu, “Optimization ofcooperative spectrum sensing in Ad-Hoc cognitive radio networks,” in Proc. GLOBECOM, 2010, pp. 1-5.
- X. Kang, Y.-C. Liang, H.K. Garg, L. Zhang, “Sensingbased spectrumsharing in cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4649-4654, 2009
- X. Zhou, G. Y. Li, D. Li, D. Wang, A.C.K. Soong, “Probabilistic resourceallocation for opportunistic spectrum access,” IEEE Transactions onWireless Communications, vol. 9, no. 9, p. 2870-2879, 2010.