CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog. (16th September 2020)

• Record Type: Journal Article
• Authors: Paul Martin, John; Kandasamy, A.; Chandrasekaran, K.
• Is Part Of: Software, practice & experience. Volume 50:Number 12(2020)
• Page Start: 2337
• Page End: 2360
• Publication Date: 2020-09-16
• Subjects: Cloud computing -- Fog computing -- Fogonomics -- monetary cost -- multi‐Whale optimisation algorithm -- NSGA‐II -- reliability -- resource management -- service placement; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2896
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Integration of Internet of Things (IoT) with industries revamps the traditional ways in which industries work. Fog computing extends Cloud services to the vicinity of end users. Fog reduces delays induced by communication with the distant clouds in IoT environments. The resource constrained nature of Fog computing nodes demands an efficient placement policy for deploying applications, or their services. The distributed and heterogeneous features of Fog environments deem it imperative to consider the reliability performance parameter in placement decisions to provide services without interruptions. Increasing reliability leads to an increase in the cost. In this article, we propose a service placement policy which addresses the conflicting criteria of service reliability and monetary cost. A multiobjective optimisation problem is formulated and a novel placement policy, Cost and Reliability‐aware Eagle‐Whale (CREW), is proposed to provide placement decisions ensuring timely service responses. Considering the exponentially large solution space, CREW adopts Eagle strategy based multi‐Whale optimisation for taking placement decisions. We have considered real time microservice applications for validating our approaches, and CREW has been experimentally shown to outperform the existing popular multiobjective meta‐heuristics such as NSGA‐II and MOWOA based placement strategies.