research-article

Keep calm and react with foresight: strategies for low-latency and energy-efficient elastic data stream processing

Authors:

Tiziano De Matteis,

Gabriele MencagliAuthors Info & Claims

PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Article No.: 13, Pages 1 - 12

https://doi.org/10.1145/2851141.2851148

Published: 27 February 2016 Publication History

Abstract

This paper addresses the problem of designing scaling strategies for elastic data stream processing. Elasticity allows applications to rapidly change their configuration on-the-fly (e.g., the amount of used resources) in response to dynamic workload fluctuations. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon in the future by solving an online optimization problem. Our control strategies are designed to address latency constraints, using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support available in the modern multicore CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. To validate our methodology, we develop a thorough set of experiments on a high-frequency trading application. The results demonstrate the high-degree of flexibility and configurability of our approach, and show the effectiveness of our elastic scaling strategies compared with existing state-of-the-art techniques used in similar scenarios.

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cover image ACM Conferences

PPoPP '16: Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 2016

420 pages

ISBN:9781450340922

DOI:10.1145/2851141

General Chair:
Rafael Asenjo
University of Málaga, Spain
,
Program Chair:
Tim Harris
Oracle Labs, Cambridge, UK

ACM SIGPLAN Notices Volume 51, Issue 8
PPoPP '16
August 2016
405 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/3016078
Editor:
Matthew Fluet
Issue’s Table of Contents

Copyright © 2016 ACM.

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Published: 27 February 2016

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PPoPP '16: 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

March 12 - 16, 2016

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Cited By

Zou BZhang TZhu CXiao LZeng MChen Z(2022)Alps: An Adaptive Load Partitioning Scaling Solution for Stream Processing System on Skewed StreamDatabase and Expert Systems Applications10.1007/978-3-031-12426-6_2(17-31)Online publication date: 29-Jul-2022
https://doi.org/10.1007/978-3-031-12426-6_2
HoseinyFarahabady MTaheri JZomaya ATari Z(2021)Data-Intensive Workload Consolidation in Serverless (Lambda/FaaS) Platforms2021 IEEE 20th International Symposium on Network Computing and Applications (NCA)10.1109/NCA53618.2021.9685244(1-8)Online publication date: 23-Nov-2021
https://doi.org/10.1109/NCA53618.2021.9685244
HoseinyFarahabady MTaheri JZomaya ATari Z(2021)Graceful Performance Degradation in Apache StormParallel and Distributed Computing, Applications and Technologies10.1007/978-3-030-69244-5_35(389-400)Online publication date: 21-Feb-2021
https://doi.org/10.1007/978-3-030-69244-5_35
Liu XBuyya R(2020)Resource Management and Scheduling in Distributed Stream Processing SystemsACM Computing Surveys10.1145/335539953:3(1-41)Online publication date: 28-May-2020
https://dl.acm.org/doi/10.1145/3355399
Mortazavi-Dehkordi MZamanifar K(2019)Efficient resource scheduling for the analysis of Big Data streamsIntelligent Data Analysis10.3233/IDA-17369123:1(77-102)Online publication date: 20-Feb-2019
https://doi.org/10.3233/IDA-173691
Röger HBhowmik SRothermel K(2019)Combining it allProceedings of the 20th International Middleware Conference10.1145/3361525.3361551(255-267)Online publication date: 9-Dec-2019
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Röger HMayer R(2019)A Comprehensive Survey on Parallelization and Elasticity in Stream ProcessingACM Computing Surveys10.1145/330384952:2(1-37)Online publication date: 30-Apr-2019
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Chen WPaik IHung P(2019)Transformation-Based Streaming Workflow Allocation on Geo-Distributed Datacenters for Streaming Big Data ProcessingIEEE Transactions on Services Computing10.1109/TSC.2016.261429712:4(654-668)Online publication date: 1-Jul-2019
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Wei XLi LLi XWang XGao SLi H(2019)Pec: Proactive Elastic Collaborative Resource Scheduling in Data Stream ProcessingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2019.289158730:7(1628-1642)Online publication date: 1-Jul-2019
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Liu XBuyya R(2019)Performance-Oriented Deployment of Streaming Applications on CloudIEEE Transactions on Big Data10.1109/TBDATA.2017.27206225:1(46-59)Online publication date: 1-Mar-2019
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