Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

如今，雾计算作为云计算的补充功能已引起了研究界的广泛关注，因为雾化具有巨大的潜力，可以为用户附近的边缘网络上的应用程序提供所需的资源和处理服务。最近的研究人员专注于如何有效地利用边缘网络功能来执行和支持IoT应用程序及相关需求。但是，在雾计算基础架构上应用程序组件的低效率部署会导致带宽和资源浪费，最大功耗以及令人不快的服务质量（QoS）级别。本文考虑减少应用程序组件在其分布式部署中的依赖性方面的带宽浪费。另一方面，如果出于功耗管理的考虑，如果将应用程序的组件部署在单个节点上，则会降低服务的可靠性。因此，提出了一种解决单点故障的机制以及针对故障的应用程序可靠性增强。然后，将组件部署公式化为一个多目标优化问题，并从最小化角度来考虑与应用程序关联的每对组件之间的功耗和总延迟。为了解决这个组合优化问题，提出了一种多目标布谷鸟搜索算法（MOCSA）。为了验证这项工作，该算法是根据一些最新技术在不同条件下进行评估的。仿真结果证明该量为42％，29％，46％，13％，相对于MOGWO，MOGWO-I，MOPSO，MOBA和NSGA-II算法，分别在平均总延迟方面提高了建议的MOCSA 5％。同样，就平均总功耗而言，改进分别约为43％，28％，41％，30％和32％。