research-article

A Stepwise Auto-Profiling Method for Performance Optimization of Streaming Applications

Authors:

Amir Vahid Dastjerdi,

Rodrigo N. Calheiros,

Rajkumar BuyyaAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 12, Issue 4

Article No.: 24, Pages 1 - 33

https://doi.org/10.1145/3132618

Published: 14 November 2017 Publication History

Abstract

Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant systems that aggregate and analyze real-time data in motion. To continuously perform analytics on the fly within the stream, state-of-the-art DSMSs host streaming applications as a set of interconnected operators, with each operator encapsulating the semantic of a specific operation. For parallel execution on a particular platform, these operators need to be appropriately replicated in multiple instances that split and process the workload simultaneously. Because the way operators are partitioned affects the resulting performance of streaming applications, it is essential for DSMSs to have a method to compare different operators and make holistic replication decisions to avoid performance bottlenecks and resource wastage. To this end, we propose a stepwise profiling approach to optimize application performance on a given execution platform. It automatically scales distributed computations over streams based on application features and processing power of provisioned resources and builds the relationship between provisioned resources and application performance metrics to evaluate the efficiency of the resulting configuration. Experimental results confirm that the proposed approach successfully fulfills its goals with minimal profiling overhead.

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Cited By

Siachamis GChristodoulou GPsarakis KFragkoulis Mvan Deursen AKatsifodimos A(2024)Evaluating Stream Processing AutoscalersProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666036(110-122)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3629104.3666036
Zhang LZheng WZheng KZhu HLi CGuo M(2024)Bayesian-Driven Automated Scaling in Stream Computing With Multiple QoS TargetsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339983435:7(1251-1267)Online publication date: Jul-2024
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Index Terms

A Stepwise Auto-Profiling Method for Performance Optimization of Streaming Applications

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Published In

cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 12, Issue 4

December 2017

224 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/3155314

Editor:
Bashar Nuseibeh
The Open University, UK

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Copyright © 2017 ACM.

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Publication History

Published: 14 November 2017

Accepted: 01 July 2017

Revised: 01 May 2017

Received: 01 November 2016

Published in TAAS Volume 12, Issue 4

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Cited By

Siachamis GChristodoulou GPsarakis KFragkoulis Mvan Deursen AKatsifodimos A(2024)Evaluating Stream Processing AutoscalersProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666036(110-122)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3629104.3666036
Zhang LZheng WZheng KZhu HLi CGuo M(2024)Bayesian-Driven Automated Scaling in Stream Computing With Multiple QoS TargetsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.339983435:7(1251-1267)Online publication date: Jul-2024
https://doi.org/10.1109/TPDS.2024.3399834
Russo Russo GCardellini VLo Presti F(2023)Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous ResourcesACM Transactions on Autonomous and Adaptive Systems10.1145/359743518:4(1-44)Online publication date: 16-May-2023
https://dl.acm.org/doi/10.1145/3597435
Hadian HFarrokh MSharifi MJafari A(2023)An elastic and traffic-aware scheduler for distributed data stream processing in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-022-04669-z79:1(461-498)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1007/s11227-022-04669-z
Odysseos LHerodotou H(2023)On combining system and machine learning performance tuning for distributed data stream applicationsDistributed and Parallel Databases10.1007/s10619-023-07434-041:3(411-438)Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s10619-023-07434-0
Ben Slimane NSagaama HMarwani MSkhiri S(2023)Mjolnir: A framework agnostic auto-tuning system with deep reinforcement learningApplied Intelligence10.1007/s10489-022-03956-953:11(14008-14022)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s10489-022-03956-9
Vogel AMencagli GGriebler DDanelutto MFernandes L(2023)Online and transparent self-adaptation of stream parallel patternsComputing10.1007/s00607-021-00998-8105:5(1039-1057)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s00607-021-00998-8
Cardellini VLo Presti FNardelli MRusso G(2022)Runtime Adaptation of Data Stream Processing Systems: The State of the ArtACM Computing Surveys10.1145/351449654:11s(1-36)Online publication date: 9-Sep-2022
https://dl.acm.org/doi/10.1145/3514496
Becker SScheinert DSchmidt FKao O(2022)Efficient Runtime Profiling for Black-box Machine Learning Services on Sensor Streams2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC)10.1109/ICFEC54809.2022.00020(88-93)Online publication date: May-2022
https://doi.org/10.1109/ICFEC54809.2022.00020
Herodotou HOdysseos LChen YLu J(2022)Automatic Performance Tuning for Distributed Data Stream Processing Systems2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00296(3194-3197)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00296
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