Optimization and Machine Learning

2MAS-aware Approach for QoS-based IoT Workflow Scheduling in Fog-Cloud Computing

Marwa MOKNI^1,2 and Sonia YASSA²

¹MARS Laboratory LR17ES05, University of Sousse, Tunisia

²ETIS Laboratory CNRS UMR8051, CY Cergy Paris University, France

Scheduling latency-sensitive Internet of Things (IoT) applications that generate a considerable amount of data is a challenge. Despite the vital computing and storage capacities, Cloud computing affects latency values due to the distance between end-users and Cloud servers. Therefore, this limitation of the Cloud has led to the development of the Fog Computing paradigm in order to build the new Fog-Cloud Computing architecture. In this chapter, we make use of the collaboration between Fog-Cloud Computing to schedule IoT applications, formed as a workflow, by considering the relationships and communications between IoT objects. The proposed scheduling approach is supported by a multi-agent system (MAS) to exploit each agent’s independent functionalities. The main objective of our work is to create the most appropriate scheduling solution that optimizes several QoS metrics simultaneously; thus, we adopt the widely used metaheuristic “genetic algorithm” as an optimization method. The proposed scheduling approach is tested by simulating a healthcare IoT application modeled as a workflow and several scientific workflow benchmarks. The results demonstrate the effectiveness of the proposed approach; it generates a scheduling plan that better optimizes ...