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On the Throughput Optimization in Large-scale Batch-processing Systems

Kar, Sounak ; Rehrmann, Robin ; Mukhopadhyay, Arpan ; Alt, Bastian ; Ciucu, Florin ; Koeppl, Heinz ; Binnig, Carsten ; Rizk, Amr (2022)
On the Throughput Optimization in Large-scale Batch-processing Systems.
In: Performance Evaluation, 2020, 144
doi: 10.26083/tuprints-00021522
Article, Secondary publication, Postprint

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Item Type: Article
Type of entry: Secondary publication
Title: On the Throughput Optimization in Large-scale Batch-processing Systems
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2020
Publisher: Elsevier
Journal or Publication Title: Performance Evaluation
Volume of the journal: 144
Collation: 15 Seiten
DOI: 10.26083/tuprints-00021522
Corresponding Links:
Origin: Secondary publication service

We analyse a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function. In practice, throughput optimization relies on numerical searches for the optimal batch size, a process that can take up to multiple days in existing commercial systems. In this paper, we model the system in terms of a closed queueing network; a standard Markovian analysis yields the optimal throughput in ω(n⁴) time. Our main contribution is a mean-field model of the system for the regime where the system size is large. We show that the mean-field model has a unique, globally attractive stationary point which can be found in closed form and which characterizes the asymptotic throughput of the system as a function of the batch size. Using this expression we find the asymptotically optimal throughput in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

Uncontrolled Keywords: Batch queueing systems, Mean-field analysis, Database systems
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-215223
Classification DDC: 000 Generalities, computers, information > 004 Computer science
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
20 Department of Computer Science > Data Management (2022 umbenannt in Data and AI Systems)
Date Deposited: 20 Jul 2022 13:48
Last Modified: 13 Apr 2023 10:54
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21522
PPN: 506767876
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