移动边缘云计算中的联合计算卸载与资源分配:一种双层博弈方法

IF 5.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhenli He;Ying Guo;Xiaolong Zhai;Mingxiong Zhao;Wei Zhou;Keqin Li
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引用次数: 0

摘要

移动边缘云计算(MECC)在平衡边缘低延迟服务与云数据中心(dc)的计算能力方面发挥着至关重要的作用。然而,许多现有研究侧重于单一提供商设置,或将其分析限制在移动设备(MDs)和边缘服务器(ESs)之间的交互上,往往忽略了来自不同提供商的ESs之间的竞争。本文介绍了一个创新的两层博弈框架,该框架捕捉了MDs和ESs之间独立的自利竞争,从而更准确地反映了多供应商环境。此外,该框架还探讨了云边缘协作对ES竞争的影响,为这些动态提供了新的见解。该模型扩展了先前的研究,开发了优化MDs和ESs的任务卸载和资源分配策略的算法,确保两层都收敛到纳什均衡。仿真结果证明了该框架在多提供商MECC环境中提高资源效率和系统响应能力的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Joint Computation Offloading and Resource Allocation in Mobile-Edge Cloud Computing: A Two-Layer Game Approach
Mobile-Edge Cloud Computing (MECC) plays a crucial role in balancing low-latency services at the edge with the computational capabilities of cloud data centers (DCs). However, many existing studies focus on single-provider settings or limit their analysis to interactions between mobile devices (MDs) and edge servers (ESs), often overlooking the competition that occurs among ESs from different providers. This article introduces an innovative two-layer game framework that captures independent self-interested competition among MDs and ESs, providing a more accurate reflection of multi-vendor environments. Additionally, the framework explores the influence of cloud-edge collaboration on ES competition, offering new insights into these dynamics. The proposed model extends previous research by developing algorithms that optimize task offloading and resource allocation strategies for both MDs and ESs, ensuring the convergence to Nash equilibrium in both layers. Simulation results demonstrate the potential of the framework to improve resource efficiency and system responsiveness in multi-provider MECC environments.
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来源期刊
IEEE Transactions on Cloud Computing
IEEE Transactions on Cloud Computing Computer Science-Software
CiteScore
9.40
自引率
6.20%
发文量
167
期刊介绍: The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.
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