This paper studies and develops multiple auction-based algorithms for resource exchange among decentralized systems in federated networks with distributed computational resources. Decentralized resource owners and users use processing, storage, and communication units to perform the available computational tasks at each time step while an auctioneer facilitates allocating resources. The auctioneer communicates with federates and receives bids for buying and selling resources, solves combinatorial problems, and proposes prices to federates. Multiple auction-based mechanisms are formulated and assessed using collective performance metrics in a networked federation. The auction-based algorithms include four reverse-bid and double-sided auctions: (1) first-price auction, (2) sequential non-linear pricing auction, (3) min–max closed-form pricing auction, and (4) balanced and maximizing closed-form pricing auction. For results, we assess algorithms for economic and computational efficiency using extensive simulation runs in hundreds of network topologies and initial conditions. The metrics introduced for our numerical validation include normalized bids and prices, collective values, and convergence rates.

中文翻译：

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联邦网络中基于拍卖的路由和任务调度算法

本文研究和开发了多种基于拍卖的算法，用于在具有分布式计算资源的联邦网络中的分散系统之间进行资源交换。去中心化的资源所有者和用户使用处理、存储和通信单元在每个时间步执行可用的计算任务，而拍卖人则有助于分配资源。拍卖师与联盟沟通并接收买卖资源的出价，解决组合问题，并向联盟提出价格。使用网络联盟中的集体绩效指标来制定和评估多种基于拍卖的机制。基于拍卖的算法包括四种反向竞价和双面拍卖：(1) 首价拍卖，(2) 顺序非线性定价拍卖，(3) 最小-最大封闭式定价拍卖，以及 (4) 平衡和最大化封闭式定价拍卖。对于结果，我们使用在数百个网络拓扑和初始条件下的大量模拟运行来评估经济和计算效率的算法。为我们的数值验证引入的指标包括标准化出价和价格、集体价值和收敛率。