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.

中文翻译：

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CREW：具有成本和可靠性意识的 Eagle-Whale 优化器，用于在 Fog 中放置服务

物联网 (IoT) 与行业的整合改变了行业的传统运作方式。雾计算将云服务扩展到最终用户附近。雾减少了在物联网环境中与远程云通信引起的延迟。雾计算节点的资源受限特性需要一个有效的部署策略来部署应用程序或其服务。Fog 环境的分布式和异构特性认为在布局决策中必须考虑可靠性性能参数，以提供不间断的服务。提高可靠性导致成本增加。在本文中，我们提出了一种服务安置政策，该政策解决了服务可靠性和货币成本的冲突标准。提出了一个多目标优化问题，并提出了一种新颖的放置策略，成本和可靠性感知鹰鲸 (CREW)，以提供放置决策，确保及时的服务响应。考虑到指数级大的解决方案空间，CREW 采用基于 Eagle 策略的多鲸优化来进行布局决策。我们已经考虑使用实时微服务应用程序来验证我们的方法，并且 CREW 已被实验证明优于现有的流行的多目标元启发式算法，例如基于 NSGA-II 和 MOWOA 的放置策略。CREW 采用基于 Eagle 策略的 multi-Whale 优化来做出放置决策。我们已经考虑使用实时微服务应用程序来验证我们的方法，并且 CREW 已被实验证明优于现有的流行的多目标元启发式算法，例如基于 NSGA-II 和 MOWOA 的放置策略。CREW 采用基于 Eagle 策略的 multi-Whale 优化来做出放置决策。我们已经考虑使用实时微服务应用程序来验证我们的方法，并且 CREW 已被实验证明优于现有的流行多目标元启发式算法，例如基于 NSGA-II 和 MOWOA 的放置策略。