面向云边连续体的自适应可扩展CEP框架
A Self-Adaptive Extensible CEP framework for the Cloud-Edge Continuum
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中文总结 AI 辅助
针对云边连续体上CEP系统的异构节点负载失衡问题，提出带负载调节功能的自适应可扩展CEP框架，可使过载算子持续运行，便于其部署于异构或资源受限环境。
中文摘要 AI 辅助
传统复杂事件处理（CEP）系统专注于从输入事件流的简单事件中提取信息，以检测复杂事件模式。在CEP语境中，执行复杂数据转换任务的计算单元称为算子。通常，CEP系统由可分布在云边连续体上的算子图构成。然而，分布在异构节点上的算子图会带来一系列特有挑战：算子图的节点通常承受动态变化的工作负载，异构节点间的负载分布不平衡可能导致单个算子出现处理过载，而这些算子缺乏足够资源以恰当适应不断增长的工作负载。因此，亟需一个能在云边连续体各节点间实时检测并适应动态变化工作负载的框架。为此，本研究旨在提出一种新型可扩展自适应CEP框架，该框架具备负载调节功能，可帮助算子即使面对过载也能持续运行，从而便于将算子部署到异构或资源受限的环境中。
英文摘要
Traditional complex event processing (CEP) systems focus on extracting information out of simple events in the input event streams to detect complex event patterns. In the context of CEP, a computing unit that executes a complex data transformation task is called an operator. Typically, a CEP system consists of an operator graph that could be distributed over the cloud-edge continuum. However, an operator graph distributed over heterogeneous nodes comes with its own set of challenges. In fact, the nodes of an operator graph are typically subjected to dynamically changing workloads. Unbalanced load distributions across heterogeneous nodes may lead to processing overloads in individual operators that do not have enough resources to properly adapt to an increasing workload. As a result, a framework that can detect and adapt to dynamically changing workload in real time across the various nodes of the cloud-edge continuum becomes paramount. To this end, the goal of this work is to propose a new extensible self-adaptive CEP framework with load regulatory features that help operators continue working even in face of an overload, thus facilitating the deployment of operators in a heterogeneous and/or resource-limited environment.
