Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP achieves this using a simple specification of analytical tasks on data streams called operators and their distributed execution on a set of infrastructure. The adaptivity of DCEP to the dynamics of IoT applications is essential and very challenging in the face of changing demands concerning Quality of Service. In our previous work, we addressed this issue by enabling transitions, which allow for the adaptive use of multiple operator placement mechanisms. In this article, we extend the transition methodology by optimizing the costs of transition and analyzing the behaviour using multiple operator placement mechanisms. Furthermore, we provide an extensive evaluation on the costs of transition imposed by operator migrations and learning, as it can inflict overhead on the performance if operated uncoordinatedly.
翻译:分散的复杂事件处理(DCEP)是一个常用的范例,用以发现许多应用,包括物的互联网(IoT)的形势变化,并就此采取行动。DCEP通过简单说明关于数据流的分析性任务,即所谓的操作者及其在一套基础设施上分布的执行。DCEP适应IoT应用的动态,在面临服务质方面不断变化的要求时,是必要和非常具有挑战性的。在我们以前的工作中,我们通过促成过渡来解决这一问题,允许适应性地使用多个操作者安置机制。在本条中,我们通过优化过渡费用,并利用多个操作者安置机制分析行为来扩展过渡方法。此外,我们对操作者移徙和学习带来的过渡费用进行了广泛的评估,因为如果操作不协调,会给业绩造成间接费用。