Cooperation: The Key to Unlock the True Potential of Edge Computing

对诸如在线游戏等计算应用的爆炸性需求使得边缘/雾计算成为未来网络的重要组成部分。通过使计算更接近网络，边缘计算减少了延迟，提高了资源效率，减轻了回程流量，降低了基础设施成本。人工智能和无线设备对设备通信的迅速发展使边缘/雾架构成为一个由智能实体组成的密集无线网络，这些智能实体可以学习、决策和通信。众所周知，合作使认知实体能够巧妙地划分成本、分担风险和分配效用。因此，它还释放了边缘计算在资源效率（自我优化）、稳定的分布式控制（自组织）和可持续性（自我诊断和自我修复）方面的真正潜力。尽管潜力巨大，但无线网络合作的实施仍面临几个重大障碍。这些问题包括信息短缺、边缘/雾节点的异质性、通信约束以及关键优化参数的随机性。在本项目中，我们将注意力集中在自主边缘/雾计算范式中的三个主要挑战：分布式任务管理、高效资源池和战略功能放置。目标是通过开发合作方法，在现实世界系统中解决这些问题，尽管存在上述限制。简而言之，该项目将多智能体系统中的合作理论与无线通信的实际方面联系起来，以解决边缘计算范式的一些主要挑战。其结果是计算效率高的决策方法，提高了边缘计算技术在能源效率和服务延迟等关键性能指标方面的效率和生产力。

The explosive demand for computationally involved applications such as online gaming renders edge/fog computing an essential block of future networks. By bringing the computation close to the network, edge computing reduces delay, improves resource efficiency, mitigates the backhaul traffic, and decreases infrastructure cost. Swift advances in artificial intelligence and wireless device-to-device communications enable the edge/fog architecture as a dense wireless network consisting of intelligent entities that can learn, make decisions, and communicate. It is well-known that cooperation enables cognitive entities to subtly divide the costs, share the risks, and distribute the utility. As such, it also unlocks the true potential of edge computing in terms of resource efficiency (self-optimization), stable distributed control (self-organization), and sustainability (self-diagnosis and self-healing). Despite great potential, the implementation of cooperation in wireless networks associates with several significant hurdles. These include information shortage, heterogeneity of edge/fog nodes, communication constraints, and randomness in crucial optimization parameters. In this project, we confine our attention to three main challenges in the autonomous edge/fog computing paradigm: distributed task management, efficient resource pooling, and strategic function placement. The objective is to address these problems in a real-world system despite the abovementioned constraints by developing cooperation methods. In a nutshell, the project bridges the theory of cooperation in multi-agent systems and the practical aspects of wireless communications to address some main challenges of the edge computing paradigm. The outcomes are computationally-efficient decision-making methods that enhance the efficiency and productivity of edge computing technology concerning crucial performance metrics such as energy efficiency and service delay.