支持边缘计算的IoV中的资源交易：一种基于期货的高效方法
Resource Trading in Edge Computing-enabled IoV: An Efficient Futures-based Approach
摘要：
移动边缘计算（MEC）已经成为利用分布式计算资源来支持动态驾驶环境中的计算密集型车辆应用的有前途的解决方案。为了促进这种模式，现场资源交易是一个关键的推动者。然而，动态的通信和资源条件可能会导致不可预测的交易延迟，交易失败，不公平的定价传统的资源交易过程。为了克服这些挑战，我们在边缘计算支持的车辆互联网（IoV）中引入了一种新的基于期货的资源交易方法，其中远期合约用于促进MEC服务器（卖方）和车辆（买方）之间在给定的未来期限内进行资源交易相关的谈判。通过估计未来资源供应和网络状况的历史统计数据，我们制定了基于期货的资源交易的优化问题，旨在最大化卖方和买方的期望效用，同时应用风险评估，以减轻可能的损失，在系统中的不确定性。为了解决这个问题，我们提出了一个有效的双边谈判的方法，促进参与者达成共识。大量的模拟表明，拟议的基于期货的资源交易带来了相当大的效用，双方的参与者，而显着优于基线方法的关键因素，如，交易失败和公平性、协商延迟和成本。
英文摘要：
Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, the onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (IoV), where a forward contract is used to facilitate resource trading related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties in the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings considerable utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
