一种基于包围模型的智能边中心分配策略
An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models
摘要：
物联网（IoT）和边缘计算（EC）的结合可以帮助交付新的应用程序，从而促进最终用户的活动。物联网基础设施中存在的许多设备收集的数据可以托管到一组EC节点中，这些节点成为提供分析的处理任务的主题。分析是由最终用户或应用程序定义的各种查询的结果。这样的查询可以在可用EC节点中执行，以限制提供响应的延迟。在本文中，我们提出了一个元集成学习计划，支持决策分配查询到适当的EC节点。我们的学习模型决定了查询和节点的特征。我们提供了一个查询和节点之间的匹配过程的描述后，得出结论的上下文信息为每个设想的特征，在我们的元集成方案。我们依赖于广为人知的集成模型，将它们结合起来，并提供一个额外的处理层来提高性能。其目的是产生一个EC节点的子集，将托管每个传入的查询。除了所提出的模型的描述，我们报告其评估和相应的结果。通过大量的实验和数值分析，我们旨在揭示所提出的方案的优点和缺点。
英文摘要：
The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities. Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics. Analytics are derived as the result of various queries defined by end users or applications. Such queries can be executed in the available EC nodes to limit the latency in the provision of responses. In this paper, we propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes. Our learning model decides over queries' and nodes' characteristics. We provide the description of a matching process between queries and nodes after concluding the contextual information for each envisioned characteristic adopted in our meta-ensemble scheme. We rely on widely known ensemble models, combine them and offer an additional processing layer to increase the performance. The aim is to result a subset of EC nodes that will host each incoming query. Apart from the description of the proposed model, we report on its evaluation and the corresponding results. Through a large set of experiments and a numerical analysis, we aim at revealing the pros and cons of the proposed scheme.
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。
