In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11\%, increasing the final user utility function value by 0.24\%, and decreasing the variance of results by 95.45\%.

翻译：在移动边缘计算（MEC）中，任务卸载能够显著降低终端用户（EU）的任务执行延迟与能耗。然而，边缘服务器（ES）资源有限，需要高效分配以确保MEC系统的可持续健康发展。本文提出一种基于动态定价机制的弹性边缘卸载近最优资源分配方法。首先，我们构建了资源定价模型，并据此分别建立了EU与ES的效用函数；通过优化这些效用函数，推导出最优定价模型参数。同时，理论分析表明，EU的效用函数在搜索范围内达到局部最大值，但超过此点后，随着资源分配的增加，其增长微乎其微。为此，我们进一步提出了动态惯性速度约束粒子群优化（DISC-PSO）算法，该算法能够高效地识别近最优资源分配方案。综合仿真结果验证了DISC-PSO算法的有效性：与现有方案相比，该算法将达到近最优解所需的平均迭代次数降低了92.11%，最终用户效用函数值提高了0.24%，且结果方差降低了95.45%。