The penetration of connected devices in today’s society has reached overwhelming figures particularly in the last decade. At present connected devices are not only becoming available everywhere, but they are rapidly gaining complexity in terms of their ability to hold significant compute and storage capacities. Thus, computing ceases to be confined to certain stationary compute devices in order to allow compute to be embedded and pervasive to everything. This feature combined with the fact that AI processes are multiplying the need for timely processing at the Edge, reveals the urgency to exploit all compute capacity available at the Edge of the network. This represents a significant breakthrough to initial Edge computing developments concentrated in providing low latency compute environments for which IoT devices are solely considered as data sources. Ad-hoc Edge Cloud is a distributed and decentralized Edge computing system dynamically formed out of IoT Edge computing resources, which aims to exploit increasingly available compute capacity at the Edge. The marked characteristics of the IoT devices, which constitute this infrastructure, pose specific challenges to resource management in this context especially due to heterogeneity, dynamicity, and volatility of resources, resulting in the probability of node churn. In this paper , we address particular admission control and service placement issues for Ad-hoc Edge Cloud by presenting a specific Admission Control mechanism for which we define and validate an associated resource availability prediction model.

互联设备在当今社会中的渗透已达到惊人的数字，尤其是在最近十年中。目前，连接的设备不仅在任何地方都变得可用，而且就其持有大量计算和存储容量的能力而言，它们正在迅速变得越来越复杂。因此，计算不再局限于某些固定的计算设备，以便允许将计算嵌入并普及到所有内容。此功能与AI流程使Edge上的及时处理需求成倍增长的事实相结合，揭示了利用网络Edge上所有可用计算能力的紧迫性。这代表着最初的Edge计算开发的重大突破，该开发集中于提供低延迟的计算环境，对于这些环境，IoT设备仅被视为数据源。Ad-hoc Edge Cloud是由IoT Edge计算资源动态组成的分布式分散式Edge计算系统，旨在利用Edge上越来越多的可用计算能力。在这种情况下，构成此基础架构的IoT设备的显着特征给资源管理带来了特定的挑战，特别是由于资源的异质性，动态性和易变性，导致节点混乱的可能性。在本文中，