DDPS: Dynamic Differential Pricing-Based Edge Offloading System With Energy Harvesting Devices

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-19 DOI:10.1109/TNSE.2025.3550251

Hai Xue;Yun Xia;Neal N. Xiong;Di Zhang;Songwen Pei

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引用次数: 0

Abstract

Mobile edge computing (MEC) mitigates the energy and computation burdens on mobile users (MUs) by offloading tasks to the network edge. To optimize MEC server utilization through effective resource allocation, a well-designed pricing strategy is indispensable. In this paper, we propose a dynamic differential pricing scheme (DDPS) for an edge offloading scenario with energy harvesting devices, which determines prices based on computing resource usage to enhance edge server (ES) utilization. First, an offloading decision algorithm is proposed to balance harvested and consumed energy, determining whether and how much data to offload. Second, a Stackelberg game-based differential pricing algorithm is proposed to optimize computing resource allocation for MUs and reallocate surplus resources to delay-sensitive devices. Extensive simulations are conducted to demonstrate the effectiveness of the proposed DDPS scheme. Specifically, in comparison to the existing best-performing pricing scheme, for different task arrival rates, DDPS can achieve a 5.3% decrease in average execution delay, a 1.7% increase in ES utility (

$U_{\text{server}}$

, which represents the payment from MUs minus penalties for discarded tasks), and a 2.1% increase in the average ratio of service for MUs. In addition, DDPS also improves 2.8%

$U_{\text{server}}$

on average with different ES computation capacities.

查看原文本刊更多论文

带能量收集装置的动态差分定价边缘卸载系统

移动边缘计算（MEC）通过将任务转移到网络边缘，减轻了移动用户（mu）的能量和计算负担。为了通过有效的资源配置来优化MEC服务器的利用率，设计一个合理的定价策略是必不可少的。在本文中，我们提出了一种动态差异定价方案（DDPS），该方案基于计算资源的使用来确定价格，以提高边缘服务器（ES）的利用率。首先，提出了一种卸载决策算法来平衡收集和消耗的能量，确定是否以及多少数据需要卸载。其次，提出了一种基于Stackelberg博弈的差分定价算法，优化微处理器的计算资源分配，并将剩余资源重新分配给延迟敏感设备。大量的仿真验证了所提出的DDPS方案的有效性。具体来说，与现有性能最佳的定价方案相比，对于不同的任务到达率，DDPS可以实现平均执行延迟减少5.3%，ES效用增加1.7% （$U_{\text{server}}$，表示来自mu的支付减去丢弃任务的惩罚），并且mu的平均服务比率增加2.1%。此外，在不同ES计算能力下，DDPS还平均提高了2.8% $U_{\text{server}}$。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.