ERAP Optimization via Enhanced Constraints and Boundary Detection in GMRA

Edge computing allows edge devices to offload computational tasks to edge servers, utilizing various hardware resources for efficient computation. Unlike cloud facilities, edge servers have limited resources. A long-term challenge is to quickly evaluate all the edge server resources and select the suitable server for the task, with high requirements for both processing time and allocation effect. The Edge Resource Allocation Problem (ERAP) represents a typical agent evaluation in collaborative work and falls within the realm of the Group Multi-Role Assignment (GMRA) problem. Based on the GMRA model, we formalize the ERAP as an optimization problem with an improved Edge- GMRA model. Additionally, we investigate the feasibility of an enhanced constraint scheme in the improved model. By boundary detection scheme, we implement quickly eliminated the infeasible solutions within the search range for ERAP. Experimental results demonstrate that the enhanced constraint scheme improves the allocation of high-priority tasks with superior acceleration as the number of agents increases, and the boundary detection scheme performs effectively in scenarios with insufficient server resources. The combination of these two schemes significantly accelerates the solution process, achieving an acceleration ratio exceeding 50%. The proposed dynamic adaptation mechanism with asynchronous agent monitoring and sliding-window threshold adjustment maintains the stability of the system under fluctuation of 15% resources, while our task-type recognition system demonstrates 92. 4% classification accuracy in six workload categories. Extensive evaluation shows the framework sustains sub-100ms decision latency during 80% resource contention scenarios, achieving 23% higher throughput than conventional methods while reducing service-level agreement violations by 41% in dynamic edge environments.