Evaluation of Load Prediction Techniques for Distributed Stream Processing

Gontarska, K., Geldenhuys, M., Scheinert, D., Wiesner, P., Polze, A. and Thamsen, L. (2021) Evaluation of Load Prediction Techniques for Distributed Stream Processing. In: 9th International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 91-98. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00023)

[img] Text
268173.pdf - Accepted Version

436kB

Abstract

Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events arrive at DSP systems can vary considerably over time, which may be due to trends, cyclic, and seasonal patterns within the data streams. A priori knowledge of incoming workloads enables proactive approaches to resource management and optimization tasks such as dynamic scaling, live migration of resources, and the tuning of configuration parameters during run-times, thus leading to a potentially better Quality of Service. In this paper we conduct a comprehensive evaluation of different load prediction techniques for DSP jobs. We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs. Automatically optimized classical and Deep Learning methods are being evaluated on nine different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster monitoring. We compare model performance with respect to overall accuracy and training duration. Our results show that the Deep Learning methods provide the most accurate load predictions for the majority of the evaluated datasets.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Thamsen, Dr Lauritz
Authors: Gontarska, K., Geldenhuys, M., Scheinert, D., Wiesner, P., Polze, A., and Thamsen, L.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
College/School:College of Science and Engineering > School of Computing Science
Publisher:IEEE
ISBN:9781665449700
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in 9th International Conference on Cloud Engineering (IC2E): 91-98
Publisher Policy:Reproduced in accordance with the publisher copyright policy
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record