Artikel in Zeitschrift ART-2020-21

Bibliograph.
Daten
Slo, Ahmad; Bhowmik, Sukanya; Rothermel, Kurt: State-Aware Load Shedding from Input Event Streams in Complex Event Processing.
In: IEEE Transactions on Big Data.
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
S. 1-18, englisch.
IEEE, 25. Dezember 2020.
ISBN: 10.1109/TBDATA.2020.3047438.
Artikel in Zeitschrift.
CR-Klassif.C.2.4 (Distributed Systems)
Kurzfassung

In complex event processing (CEP), load shedding is performed to maintain a given latency bound during overload situations when there is a limitation on resources. However, shedding load implies degradation in the quality of results (QoR). Therefore, it is crucial to perform load shedding in a way that has the lowest impact on QoR. Researchers, in the CEP domain, propose to drop either events or partial matches (PMs) in overload cases. They assign utilities to events or PMs by considering either the importance of events or the importance of PMs but not both together. In this paper, we combine these approaches where we propose to assign a utility to an event by considering both the event importance and the importance of PMs. We propose two load shedding approaches for CEP systems. The first approach drops events from PMs, while the second approach drops events from windows. We adopt a probabilistic model that uses the type and position of an event in a window and the state of a PM to assign a utility to an event. We, also, propose an approach to predict a utility threshold that is used to drop the required amount of events to maintain a given latency bound. By extensive evaluations on two real-world datasets and several representative queries, we show that, in the majority of cases, our load shedding approach outperforms state-of-the-art load shedding approaches, w.r.t. QoR.

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CopyrightCopyright IEEE, [2020]. This is the author’s version of the work. It is posted here by permission of IEEE for your personal use. Not for redistribution. The definitive version will be published in IEEE Transactions on Big Data.
Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Verteilte Systeme
Projekt(e)PRECEPT II
Eingabedatum30. Dezember 2020
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