Dynamic Resource Allocation for Edge Computing

This research article puts forth a new approach towards dynamic resource allocation within edge computing. As edge computing tries to make computational power closer to where data comes from, it has caught a lot of attention since it could improve time-sensitive applications. Here is an edge computing simulation framework that unites quantum algorithms, game theory-based resource allocation, cross-layer optimization as wells as collaborative scheduling techniques. The framework includes Deutsch-Jozsa quantum algorithm implementations for function evaluation, game theory-based resource allocation, cross-layer optimization, and collaborative multi-edge scheduling algorithms. Also, there are visualization modules for analyzing simulation results including bar charts, pie charts, and heatmaps. The main goal of this framework is to optimize resource usage and satisfy quality of service (QoS) demands in rapidly changing edge environments. Simulation results demonstrate that proposed methodology can improve the efficiency of a dynamic edge computing system by resource utilization enhancement and decrease of latency. Simulations on a real edge computing environment, clearly shows the effectiveness of the proposed framework in improving resource utilization as well as adhering to QoS requirements as opposed to traditional methods. Significant improvements in experimental metrics such as resource utilization rates, task completion times and fairness indices validate the efficiency of the framework for practical implementations.