CREW: Cost and Reliability aware Eagle‐Whale optimiser for service placement in Fog

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 o...

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Bibliographic Details
Published inSoftware, practice & experience Vol. 50; no. 12; pp. 2337 - 2360
Main Authors Paul Martin, John, Kandasamy, A., Chandrasekaran, K.
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 01.12.2020
Subjects
Cloud computing
Decisions
End users
Fog computing
Fogonomics
Internet of Things
monetary cost
Multiple objective analysis
multi‐Whale optimisation algorithm
NSGA‐II
Optimization
Placement
Reliability
resource management
service placement
Solution space
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Summary: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.
Bibliography:Funding information
IoT, Internet of Things; MOWOA, multiobjective whale optimisation algorithm; NSGA, nondominated sorting genetic algorithm
Abbreviations
Ministry of Electronics & Information Technology, Government of India, VISPHD‐MEITY‐1661
ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2896