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PANDA: performance Prediction for Parallel and dynamic Stream Processing

Agnihotri, Pratyush ; Koldehofe, Boris ; Binnig, Carsten ; Luthra, Manisha (2022)
PANDA: performance Prediction for Parallel and dynamic Stream Processing.
16th ACM International Conference on Distributed and Event-Based Systems. Copenhagen, Denmark (27.-30.06.2022)
doi: 10.1145/3524860.3543281
Conference or Workshop Item, Bibliographie

Abstract

Distributed Stream Processing (DSP) systems highly rely on parallelism mechanisms to deliver high performance in terms of latency and throughput. Yet the development of such parallel systems altogether comes with numerous challenges. In this paper, we focus on how to select appropriate resources for parallel stream processing under the presence of highly dynamic and unseen workloads. We present PANDA that provides a novel learned approach for highly efficient and parallel DSP systems. The main idea is to provide accurate resource estimates and hence optimal parallelism degree using zero-shot cost models to ensure the performance demands.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Agnihotri, Pratyush ; Koldehofe, Boris ; Binnig, Carsten ; Luthra, Manisha
Type of entry: Bibliographie
Title: PANDA: performance Prediction for Parallel and dynamic Stream Processing
Language: English
Date: 15 July 2022
Publisher: ACM
Book Title: DEBS 2022: Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems
Event Title: 16th ACM International Conference on Distributed and Event-Based Systems
Event Location: Copenhagen, Denmark
Event Dates: 27.-30.06.2022
DOI: 10.1145/3524860.3543281
Abstract:

Distributed Stream Processing (DSP) systems highly rely on parallelism mechanisms to deliver high performance in terms of latency and throughput. Yet the development of such parallel systems altogether comes with numerous challenges. In this paper, we focus on how to select appropriate resources for parallel stream processing under the presence of highly dynamic and unseen workloads. We present PANDA that provides a novel learned approach for highly efficient and parallel DSP systems. The main idea is to provide accurate resource estimates and hence optimal parallelism degree using zero-shot cost models to ensure the performance demands.

Uncontrolled Keywords: parallel stream processing, zero-shot cost models
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Multimedia Communications
20 Department of Computer Science
20 Department of Computer Science > Data Management (2022 umbenannt in Data and AI Systems)
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C2: Information-centred perspective
Date Deposited: 16 Aug 2022 08:12
Last Modified: 17 Nov 2022 11:35
PPN: 501727604
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