Yingzhuo Deng, Zicheng Hu, Weihao Xu, Ningning Han, Haibin Cai
{"title":"计算能力网络中的协作资源分配:博弈论双重拍卖视角","authors":"Yingzhuo Deng, Zicheng Hu, Weihao Xu, Ningning Han, Haibin Cai","doi":"10.1016/j.comnet.2024.110850","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective\",\"authors\":\"Yingzhuo Deng, Zicheng Hu, Weihao Xu, Ningning Han, Haibin Cai\",\"doi\":\"10.1016/j.comnet.2024.110850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":50637,\"journal\":{\"name\":\"Computer Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2024-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Networks\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1389128624006820\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128624006820","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective
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.
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.