Abstract
The need to compute data in real-time and manage resources in environments with distributed computing has given edge computing significant importance. However, one of the most critical tasks regarding resources has been to schedule and optimize them in accordance with energy consumption and delay time. These challenges has been addressed in this paper with the introduction of a new integrated method that assumes the Cellular Potts Model and Particle Swarm Optimization. The Cellular Potts Model is used to capture local interaction and dependencies of resources, while PSO acts as a global optimizer for scheduling reducing latency and energy consumption. Based on these considerations, the primary research goal of this work is to mitigate the QoS requirements like energy consumption and end-to-end delay using CPM-spatial modeling complemented by PSO - the global optimization. Based on experimental analysis, the authors of the paper argue that the newly proposed Hybrid model consumes less energy and has less processing time than Round-Robin, Random Offloading, and Threshold-Based techniques. In addition, the approach achieves higher scalability and can perform a large of tasks and edge nodes with a high QoS while working in a resource-limited environment. This paper contributes to presenting the integration procedure of the CPM's local optimization with the PSO's global search, which offers high-performance and real-time solutions for resource scheduling in the edge computing environment. The results presented in the paper show that the proposed hybrid CPM-PSO model can offer greater potential as a tool for energy-constrained and time-sensitive applications within the future development of edge computing.