论文标题
基于集合模型的智能边缘以中心查询分配方案
An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models
论文作者
论文摘要
物联网（IoT）和Edge Computing（EC）的结合可以帮助提供新的应用程序，从而促进最终用户的活动。 IoT基础架构中存在的许多设备收集的数据可以托管到一组EC节点中，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得出的。可以在可用的EC节点中执行此类查询，以限制提供响应的延迟。在本文中，我们提出了一个元组成的学习计划，该计划支持将查询分配给适当的EC节点的决策。我们的学习模型决定了查询和节点的特征。在总结了我们的荟萃安装方案中采用的每个设想的特征的上下文信息之后，我们提供了查询和节点之间匹配过程的描述。我们依靠广为人知的合奏模型，将它们组合在一起并提供额外的处理层以提高性能。目的是导致将托管每个传入查询的EC节点的子集。除了对拟议模型的描述外，我们还报告了其评估和相应的结果。通过大量实验和数值分析，我们旨在揭示所提出的方案的利弊。
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.
