QoS-Driven Optimal Multi-Cloud Service Composition using Discrete and Fuzzy Integrated Cuckoo Search Algorithm
A V L N Sujith1, a Rama Mohan Reddy2, K Madhavi3

1A V L N Sujith, Research Scholar Department of CSE, JNTUA University, Anantapuramu (Andhra Pradesh), India.
2Dr. A Rama Mohan Reddy, Professor Department of CSE, Sri Venkateswara University College of Engineering, Tirupati (Andhra Pradesh), India.
3Dr. K Madhavi, Associate Professor Department of CSE, JNTUA University College of Engineering , Anantapuramu (Andhra Pradesh), India.

Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2138-2146 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7567068519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Recent generations of service computing evidence that quality of Service (QoS) driven optimal composition of multi-cloud services (QoS-CSC) is considered as a vital problem in the context of manufacturing complex cloud services based on the client requirements. Due to the proliferation of the seamless cloud services, optimal service composition of the multi-cloud services is considered as an NP-Hard problem. The QoS-CSC problems are solved by using continuous optimization algorithms to make them discrete a suitable encoding scheme is adopted with continuous optimization. In this paper, we propose a fuzzy integrated numerical encoding scheme in which the solution is represented as a fuzzy matrix and the composition of cloud services is obtained using that fuzzy matrix and further the global QoS attributes are computed. Discrete and Fuzzy integrated Cuckoo Search algorithm (DFCS) is developed by using our proposed fuzzy solution encoding schema. Further, during the process of performance evaluation, an empirical comparison various existing metaheuristic algorithms and proposed DFCS algorithm is illustrated using a set of real-world cloud services to identify exceptional service composition that optimizes local along with the global QoS attributes.
Keywords: Service-Oriented Computing (Soc), Quality Of Service (Qos); Discrete Optimization; Cloud Service Composition; Cuckoo Search, Bat Algorithm; Fuzzy Encoding.

Scope of the Article: Discrete Optimization