Hierarchical Reverse Games for the Resource Ecosystem in Cloud–Edge Computing

IF 10.9 2区 计算机科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Consumer Electronics Pub Date : 2024-08-21 DOI:10.1109/TCE.2024.3415572
Manlin Pei;Chenyu Wang;Xia Wang;Zhigao Zheng;Jianhui Huang;Shengling Wang;Yufei Guo
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Abstract

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences.
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云边缘计算资源生态系统的分层反向博弈
边缘计算由于其分布式架构和接近客户的特性,已经成为超连接世界的杀手级技术。边缘节点与云数据中心相结合,形成了云边缘计算范式,其资源生态系统要求具有竞争力的定价和最优的资源配置。然而,由于某些信息对其所有者来说是私有的,因此实现全局信息优化是不可行的。为了解决这一难题,本文提出了用户独立的分层逆向博弈和用户辅助的分层逆向博弈来描述资源提供者、资源承租者和资源使用者之间的关系。其目的是在不泄露任何私人信息的情况下为玩家寻求最佳策略。这两个博弈的共同特点是迫使参与者根据他们的实际私人信息报告最优策略。这一显著特征可以产生隐私保护优化,因为参与人不需要直接发布个人信息,其他参与人的最优策略可以自然地推导出来。用户独立的分层逆向博弈与用户辅助的分层逆向博弈的区别在于,前者的博弈规则完全由提供者和租户(边缘节点)来规定,而后者的一些博弈规则可以根据用户的建议进行自定义。这使得用户独立的分层逆向博弈更简洁,更容易保证激励兼容约束,而用户辅助的分层逆向博弈由于游戏规则能够适应用户的经济状况、需求和偏好,能够吸引更多的用户。
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
CiteScore
7.70
自引率
9.30%
发文量
59
审稿时长
3.3 months
期刊介绍: The main focus for the IEEE Transactions on Consumer Electronics is the engineering and research aspects of the theory, design, construction, manufacture or end use of mass market electronics, systems, software and services for consumers.
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