TU Darmstadt / ULB / TUbiblio

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, 144
doi: 10.26083/tuprints-00021522
Artikel, Zweitveröffentlichung, Postprint

Kurzbeschreibung (Abstract)

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.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Kar, Sounak ; Rehrmann, Robin ; Mukhopadhyay, Arpan ; Alt, Bastian ; Ciucu, Florin ; Koeppl, Heinz ; Binnig, Carsten ; Rizk, Amr
Art des Eintrags: Zweitveröffentlichung
Titel: On the Throughput Optimization in Large-scale Batch-processing Systems
Sprache: Englisch
Publikationsjahr: 2022
Ort: Darmstadt
Verlag: Elsevier
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Performance Evaluation
Jahrgang/Volume einer Zeitschrift: 144
Kollation: 15 Seiten
DOI: 10.26083/tuprints-00021522
URL / URN: https://tuprints.ulb.tu-darmstadt.de/21522
Zugehörige Links:
Herkunft: Zweitveröffentlichungsservice
Kurzbeschreibung (Abstract):

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.

Freie Schlagworte: Batch queueing systems, Mean-field analysis, Database systems
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-215223
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Self-Organizing Systems Lab
20 Fachbereich Informatik
20 Fachbereich Informatik > Data Management (2022 umbenannt in Data and AI Systems)
Hinterlegungsdatum: 20 Jul 2022 13:48
Letzte Änderung: 26 Jul 2022 08:15
PPN:
Zugehörige Links:
Export:
Suche nach Titel in: TUfind oder in Google
Frage zum Eintrag Frage zum Eintrag

Optionen (nur für Redakteure)
Redaktionelle Details anzeigen Redaktionelle Details anzeigen