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Lookup NU author(s): Dr Paul Omoregbee, Dr Matthew ForshawORCiD, Dr Nigel Thomas
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer , 2023.
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In stream processing applications, accurately measuring a system’s processing capacity is critical for ensuring optimal performance and meeting Service Level Objectives (SLOs). Traditionally, operator throughput has been used as a proxy for the application’s state size, but this approach can be misleading when dealing with window-based applications. In this paper, we explore the impact of window selectivity on the performance of streaming applications, demonstrating how a growing application state can artificially decrease the operators’ throughput, resulting in false positives that could trigger premature scaling-down decisions. To address this problem, we conduct empirical evaluations to assess the relationship between operators’ throughput and state size, showcasing the state size pattern typically does not correspond to the operator’s processing rate in window-based applications. Our findings highlight the importance of considering the state size of the application in performance monitoring and decision-making, particularly in the context of window-based applications.
Author(s): Omoregbee P, Forshaw M, Thomas N
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Computer Performance Engineering and Stochastic Modelling: 19th European Performance Engineering Workshop (EPEW 2023)
Year of Conference: 2023
Pages: 325-339
Print publication date: 07/10/2023
Online publication date: 07/10/2023
Acceptance date: 24/05/2023
Date deposited: 27/11/2023
ISSN: 0302-9743
Publisher: Springer
URL: https://doi.org/10.1007/978-3-031-43185-2_22
DOI: 10.1007/978-3-031-43185-2_22
ePrints DOI: 10.57711/3vhg-e880
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science
ISBN: 9783031431845