论文标题
一个主动不确定性驱动的模型，用于卸载
A Proactive Uncertainty driven Model for Tasks Offloading
论文作者
论文摘要
最终用户在物联网（IoT）上的需求不断增加，通常会导致节点上满足其要求的充血。因此，出现了节点过载的问题。在本文中，我们试图通过提出一种机制来解决节点中的流量繁忙的问题，该机制使节点无法重载，无论输入其中的负载如何，并且都考虑到优先级和任务需求。更具体地说，我们引入了一种积极主动的自我修复机制，该机制利用模糊系统，结合了非参数统计方法。通过我们的方法，我们设法确保基于积极的方法，无论节点可能会收到什么负载，都可以确保不间断的高需求或优先任务的服务。另外，我们通过高优先级和高需求敏感机制确保最快的结果传递给请求者。一系列实验场景用于评估建议模型的性能，同时我们呈现相关的数值结果。
The ever-increasing demands of end-users on the Internet of Things (IoT), often cause great congestion in the nodes that serve their requests. Therefore, the problem of node overloading arises. In this article we attempt to solve the problem of heavy traffic in a node, by proposing a mechanism that keeps the node from overloading, regardless of the load entering in it, and which takes into consideration both the priority and the task demand. More specifically, we introduce a proactive, self-healing mechanism that utilizes fuzzy systems, in combination to a non-parametric statistic method. Through our approach, we manage to ensure the uninterrupted service of high demand or priority tasks, regardless of the load the node may receive, based on a proactive approach. Also, we ensure the fastest possible result delivery to the requestors, through the high priority and high demand sensitive mechanism. A series of experimental scenarios are used to evaluate the performance of the suggested model, while we present the relevant numerical results.
