A Multi-Objective Optimization Algorithm for Batching Tasks in MEC
Year: 2026
Type: article
Abstract: For multi-source data fusion analysis applications in Multi-access Edge Computing (MEC) systems, an edge node processes the tasks offloaded by multiple devices within the same time window in the form of task batches. Batching tasks amortizes the computational overhead on the edge, but it also prolongs task waiting time and therefore reduces the system throughput within a given time budget. This paper investigates the offloading problem for batching tasks and jointly optimizes transmission power of each device and edge computing resource allocation. The trade-off between system throughput maximization and total energy consumption minimization is modeled as a multi-objective optimization problem. Then, a two-phase algorithm is proposed to obtain a Pareto optimal solution. In the first phase, the transmission power and edge computing resource are set to their upper bounds so as to obtain the maximum system throughput. In the second phase, by setting the task batch number and taking variable substitution, the energy minimization problem is transformed into an easily solved convex problem. Simulation results show that the proposed algorithm attains 79.69%∼199.70% higher system throughput than Batch-MinEnergy, and 9.10%∼41.15% lower energy consumption than Batch-MaxTps for batching tasks.
Authors: Xuemei Yang, Junjun Zhang
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225009975