Cooperative agents-based approach for workflow scheduling on fog-cloud computing

Abstract

Connected objects in the Internet of Things (IoT) domain are widespread everywhere. They interact with each other and cooperate with their neighbors to achieve a common goal. Most of these objects generate a huge amount of data, often requiring a process under strict time constraints. Being motivated by the question of optimizing the execution time of these IoT tasks, we remain aware of the sensitivity to latency and the volume of data generated. In this article, we propose a hybrid Cloud-Fog multi-agent approach to schedule a set of dependent IoT tasks modeled as a workflow. The major advantage of our approach is to allow to model IoT workflow planning as a multi-objetif optimization problem in order to create a compromise planning solution in terms of response time, cost and makespan. In addition to taking into account data communications between workflow tasks, during the planning process, our approach has two other advantages: (1) maximizing the use of Fog Computing in order to minimize response time, and (2) the use of elastic cloud computing resources at minimum cost. The implementation of the MAS-GA (Multi-Agent System based Genetic Algorithm), which we have proposed in this context; the series of experiments carried out on different corpora, as well as the analysis of the found results confirm the feasibility of our approach and its performance in terms of cost which represents an average gain of 21.38% compared to Fog and 25.49% compared to Cloud, makespan which represents a gain of 14.13% compared to Fog and a slight increase of 5.24% compared to Cloud and in response time which represents an average gain of 46.66% compared to Cloud with a slight increase of 6.66% compared to Fog, while strengthening the collaboration between Fog computing and Cloud computing.