Authors: Punit Gupta; Satya Prakash Ghrera
Addresses: Department of Computer Science Engineering, Jaypee University of Information Technology, Himachal Pradesh, India ' Department of Computer Science Engineering, Jaypee University of Information Technology, Himachal Pradesh, India
Abstract: Cloud computing is now an industrial standard for large scale computing and solving problems with high reliability. This has been accepted by companies worldwide like Google, Microsoft and Apple for resource computing and resource sharing. But as the number of request over the data centres in cloud increases, load and failure probability over a data centre increases. So the requests need to be balanced in such an efficient manner which having more effective strategy for resources utilisation, request failure and improved system reliability. Moreover a survey on cloud computing shows that failure probability increases if the load over the distributed independent resources increases. So to overcome these issues in cloud infrastructure as a service (IaaS), we have proposing a learning-based fault aware big bang-big crunch algorithm for task allocation to minimise the request failure and improve quality of service (QoS) over a data centre. The proposed algorithm has been inspired from theory of evolution in astrology. The proposed strategy has proven to have better performance in term of execution time, scheduling time and request failure rate as compared to previously proposed task allocation algorithm.
Keywords: cloud computing; quality of service; QoS; resource utilisation; failure probability; reliability; cloud infrastructure as a service; makespan.
International Journal of Advanced Intelligence Paradigms, 2018 Vol.10 No.4, pp.329 - 343
Received: 21 Mar 2016
Accepted: 03 Oct 2016
Published online: 23 Apr 2018 *