Cloud-aided mobile edge networks (CAMENs) allow edge servers (ESs) to purchase resources from remote cloud servers (CSs), while overcoming resource shortage when handling computation-intensive tasks of mobile users (MUs). Conventional trading mechanisms (e.g., onsite trading) confront many challenges, including decision-making overhead (e.g., latency) and potential trading failures. This paper investigates a series of cross-layer matching mechanisms to achieve stable and cost-effective resource provisioning across different layers (i.e., MUs, ESs, CSs), seamlessly integrated into a novel hybrid paradigm that incorporates futures and spot trading. In futures trading, we explore an overbooking-driven aforehand cross-layer matching (OA-CLM) mechanism, facilitating two future contract types: contract between MUs and ESs, and contract between ESs and CSs, while assessing potential risks under historical statistical analysis. In spot trading, we design two backup plans respond to current network/market conditions: determination on contractual MUs that should switch to local processing from edge/cloud services; and an onsite cross-layer matching (OS-CLM) mechanism that engages participants in real-time practical transactions. We next show that our matching mechanisms theoretically satisfy stability, individual rationality, competitive equilibrium, and weak Pareto optimality. Comprehensive simulations in real-world and numerical network settings confirm the corresponding efficacy, while revealing remarkable improvements in time/energy efficiency and social welfare.

翻译：云辅助移动边缘网络（CAMENs）允许边缘服务器（ESs）从远程云服务器（CSs）购买资源，从而克服处理移动用户（MUs）计算密集型任务时的资源短缺问题。传统交易机制（例如现场交易）面临诸多挑战，包括决策开销（如延迟）和潜在交易失败。本文研究了一系列跨层匹配机制，以实现不同层级（即MUs、ESs、CSs）间稳定且成本效益高的资源供给，并无缝集成于一种融合期货与现货交易的新型混合范式中。在期货交易中，我们探索了一种基于超额预订的事前跨层匹配（OA-CLM）机制，支持两种未来合约类型：MUs与ESs之间的合约，以及ESs与CSs之间的合约，同时基于历史统计分析评估潜在风险。在现货交易中，我们设计了两种应对当前网络/市场状况的备用方案：确定应从边缘/云服务切换到本地处理的合约MUs；以及一种让参与者进行实时实际交易的现场跨层匹配（OS-CLM）机制。接着，我们证明所提出的匹配机制在理论上满足稳定性、个体理性、竞争均衡和弱帕累托最优性。在真实世界和数值网络设置中的全面仿真验证了相应效能，同时在时间/能量效率和社会福利方面展现了显著改进。