An Ant Colony Optimization-Based Multiobjective Service Replicas Placement Strategy for Fog Computing

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.

Automated summary

This article investigates the placement of service replicas in fog computing and proposes an ant colony optimization-based solution, MRPACO, which is validated through extensive experiments. The results show that the solutions obtained are qualified in terms of diversity and accuracy, the main evaluation metrics of a multiobjective algorithm.