In recent years, fog computing has emerged as a new paradigm for the future Internet-of-Things (IoT) applications, but at the same time, ensuing new challenges. The geographically vast-distributed architecture in fog computing renders us almost infinite choices in terms of service orchestration. How to properly arrange the service replicas (or service instances) among the nodes remains a critical problem. To be specific, in this article, we investigate a generalized service replicas placement problem that has the potential to be applied to various industrial scenarios. We formulate the problem into a multiobjective model with two scheduling objectives, involving deployment cost and service latency. For problem solving, we propose an ant colony optimization-based solution, called multireplicas Pareto ant colony optimization (MRPACO). We have conducted extensive experiments on MRPACO. The experimental results show that the solutions obtained by our strategy are qualified in terms of both diversity and accuracy, which are the main evaluation metrics of a multiobjective algorithm.

中文翻译：

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基于蚁群优化的雾计算多目标服务副本放置策略


近年来，雾计算已成为未来物联网（IoT）应用的新范式，但同时也带来了新的挑战。雾计算的地理分布广泛的架构为我们在服务编排方面提供了几乎无限的选择。如何在节点之间正确安排服务副本（或服务实例）仍然是一个关键问题。具体来说，在本文中，我们研究了一个广义的服务副本放置问题，该问题有可能应用于各种工业场景。我们将问题表述为具有两个调度目标的多目标模型，涉及部署成本和服务延迟。为了解决问题，我们提出了一种基于蚁群优化的解决方案，称为多副本帕累托蚁群优化（MRPACO）。我们对 MRPACO 进行了广泛的实验。实验结果表明，我们的策略获得的解在多样性和准确性方面都是合格的，这是多目标算法的主要评价指标。