Genetic Programming for Dynamic Workflow Scheduling in Fog Computing

Dynamic Workflow Scheduling in Fog Computing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective schedule. In this article, a new problem model for DWSFC is considered and a new simulator is designed for the new DWSFC problem model. The designed simulator takes the mobile devices, edge, and cloud servers as a whole system, where they all can execute tasks. In the designed simulator, two kinds of decision points are considered, which are the routing decision points and the sequencing decision points. To solve this problem, a new Multi-Tree Genetic Programming (MTGP) method is developed to automatically evolve scheduling heuristics that can make effective real-time decisions on these decision points. The proposed MTGP method with a multi-tree representation can handle the routing decision points and sequencing decision points simultaneously. The experimental results show that the proposed MTGP can achieve significantly better test performance (reduce the makespan by up to 50%) on all the tested scenarios than existing state-of-the-art methods.

Automated summary

Dynamic Workflow Scheduling in Fog Computing is a significant optimization problem that involves the coordination of cloud servers, mobile devices, and edge servers. This article proposes a new problem model and simulator, as well as a Multi-Tree Genetic Programming method to address the problem. Experimental results demonstrate that the proposed method achieves significantly better performance across all tested scenarios.