Intelligent Resource Scheduling in Mobile Edge Clouds Using Adaptive Queueing and Meta-heuristics

The key aspect of Mobile Edge Computing (MEC) that has aroused considerable concern is how to efficiently schedule tasks and allocate resources to meet real-time applications' low latency and energy requirements. Classical scheduling algorithms such as First-Come-First-Served (FCFS), Shortest Job First (SJF), and Round Robin (RR) are largely ineffective for managing dynamic workloads and heterogeneous resource environments. This paper proposes A delay energy-efficient queuing-based Particle Swarm Optimization (DAEE-QPSO) algorithm that combines queuing theory and swarm intelligence to optimize task offloading decisions. Individual nodes of edges are represented by M/M/1 queues that allow the queuing delay and system congestion to be estimated perfectly. A hybrid fitness function is constructed that attempts to concurrently reduce the runtime and energy usage of tasks and constraints (meeting task deadlines and energy budgets). The proposed DAEE-QPSO algorithm shows the lowest average run time and energy consumption compared to FCFS and EADF-PSO. It also has high task throughput and maintains the greatest average energy saving of up to 29.8%, outperforming Energy Aware Double Fitness Function PSO (EADF-PSO) by only 23.3%. This result demonstrates its effectiveness in improving task efficiency in a mobile edge cloud. The suggested architecture is very applicable to energy-conscious and real-time usage in the cloud-to-edge environment.