Multi-Round Stackelberg Game-Based Pricing and Offloading in Containerized MEC Networks

IF 5.3 2区 计算机科学 Q1 TELECOMMUNICATIONS
Mingxiong Zhao;Zhaojie Yang;Zhenli He;Fanhao Xue;Xianqi Zhang
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Abstract

Mobile Edge Computing (MEC) tackles the challenges associated with the rapid proliferation of User Equipment (UE) and limited computing resources. Containerization, essential for MEC deployments, encapsulates applications and dependencies, optimizing resource utilization. In containerized MEC networks, UEs offload computational tasks to edge server containers, enabling service providers to profit from offering scalable and portable services, thereby establishing a symbiotic economic ecosystem. However, traditional models, which often separate cost and delay assessments, fail to consider these factors holistically. Furthermore, they underutilize the potential of container images’ hierarchical structure, which could optimize storage and reduce costs. Our research introduces a novel multi-round Stackelberg game framework that incorporates the hierarchical structure of container images to enhance resource management in MEC networks. Additionally, we integrate discount rates to model long-term economic interactions accurately, and develop two innovative algorithms: the Distributed Ant Colony Pricing (DACP) and the Multi-Round Simulated Annealing Pricing (MRSAP). These algorithms account for both immediate and long-term impacts, redefining user utility and significantly improving system efficiency. Simulation results validate the effectiveness of our algorithms in optimizing resource allocation and enhancing efficiency in dynamic MEC scenarios.
集装箱MEC网络中基于多回合Stackelberg博弈的定价与卸载
移动边缘计算(MEC)解决了与用户设备(UE)快速扩散和有限计算资源相关的挑战。容器化对于MEC部署至关重要,它封装了应用程序和依赖项,优化了资源利用率。在容器化的MEC网络中,终端将计算任务卸载到边缘服务器容器,使服务提供商能够从提供可扩展和便携式服务中获利,从而建立一个共生的经济生态系统。然而,传统模型往往将成本和延迟评估分开,未能全面考虑这些因素。此外,它们充分利用了容器映像的分层结构的潜力,这可以优化存储并降低成本。我们的研究引入了一种新的多轮Stackelberg游戏框架,该框架结合了容器图像的分层结构,以增强MEC网络中的资源管理。此外,我们整合了折现率来准确地模拟长期经济相互作用,并开发了两种创新算法:分布式蚁群定价(DACP)和多轮模拟退火定价(MRSAP)。这些算法考虑了即时和长期影响,重新定义了用户效用并显著提高了系统效率。仿真结果验证了算法在动态MEC场景下优化资源分配和提高效率的有效性。
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 Green Communications and Networking
IEEE Transactions on Green Communications and Networking Computer Science-Computer Networks and Communications
CiteScore
9.30
自引率
6.20%
发文量
181
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