Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Yingzhuo Deng, Zicheng Hu, Weihao Xu, Ningning Han, Haibin Cai
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Abstract

The growth of global data is increasing exponentially, leading to a greater demand for computing power. To address this requirement, expanding computing power from the cloud to the edge is essential. However, this transformation presents two significant challenges: how to share computing resources more efficiently and how to optimize resource allocation. To tackle these challenges, we propose a three-layer Computing Power Network (CPN) framework that focuses on implementing the collaborative allocation of computing nodes and user tasks. We formulate the resource allocation problem in CPN as a double auction game and use an experience-weighted attraction algorithm that enables participants to adjust bidding strategies based on environmental interactions. We implemented a prototype of our proposed CPN framework and conducted extensive experiments to verify our algorithm’s convergence and evaluate the benefits obtained by buyers (users) and sellers (computing nodes) from the perspective of transaction prices, rewards, and average pricing. The comprehensive experimental results demonstrate the effectiveness of our proposed method. Compared with state-of-the-art pricing strategies, our approach achieves a 20% increase in convergence speed and an 88% increase in overall returns. Furthermore, it also exhibits a 2.5% increase in deal prices and a substantial 83% rise in the income of individual users. These outcomes convincingly prove the superiority of our method in achieving better convergence, improving overall returns, and benefiting both buyers and sellers in the CPN resource auction market.
计算能力网络中的协作资源分配:博弈论双重拍卖视角
全球数据呈指数级增长,导致对计算能力的需求越来越大。为了满足这一需求,必须将计算能力从云端扩展到边缘。然而,这种转变带来了两个重大挑战:如何更有效地共享计算资源以及如何优化资源分配。为了应对这些挑战,我们提出了一个三层计算能力网络(CPN)框架,重点是实现计算节点和用户任务的协同分配。我们将 CPN 中的资源分配问题表述为双重拍卖游戏,并使用经验加权吸引算法,使参与者能够根据环境互动调整竞标策略。我们实现了所提 CPN 框架的原型,并进行了大量实验来验证算法的收敛性,并从交易价格、奖励和平均定价的角度评估了买方(用户)和卖方(计算节点)获得的收益。全面的实验结果证明了我们提出的方法的有效性。与最先进的定价策略相比,我们的方法收敛速度提高了 20%,总体回报提高了 88%。此外,它还使交易价格提高了 2.5%,单个用户的收入大幅提高了 83%。这些结果令人信服地证明了我们的方法在实现更好的收敛性、提高整体收益以及使 CPN 资源拍卖市场的买卖双方受益方面的优越性。
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
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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