A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real-time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time-sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well-known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice-based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real-time IoT applications.

中文翻译：

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以物联网为中心的雾环境中去中心化服务放置策略的强化优化

去中心化的服务放置策略在分布式系统中发挥着关键作用，例如雾计算，其中在活动计算节点之间公平地共享工作负载至关重要。去中心化策略是服务放置流程的固有特征，可以改善计算机之间的负载平衡，并可以减少许多实时物联网 (IoT) 应用程序的延迟。本文提出了去中心化服务放置策略的强化优化，试图减轻现有服务放置策略的一些缺点。将任务大小与节点规格相匹配以及在雾层中分配不太流行但时间敏感的应用程序是本研究的主要贡献。使用 iFogSim 模拟器对所提出的算法与其他知名算法在服务延迟、网络使用和计算使用方面进行了广泛的实验比较。对具有不同大小计算请求的基于微服务的应用程序进行了实验测试，结果表明所提出的算法有效地服务于更靠近用户的计算实例，减少了服务延迟和网络使用。与现有模型相比，所提出的修改算法将服务延迟减少了 24.1%，网络使用量减少了 4%，计算使用量减少了 20%，从而凸显了在未来实时物联网应用中使用所提出的雾分析算法时的积极成果。