Provenance-aware optimization of workload for distributed data production

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00098484" target="_blank" >RIV/00216224:14330/17:00098484 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1088/1742-6596/898/5/052038" target="_blank" >http://dx.doi.org/10.1088/1742-6596/898/5/052038</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/898/5/052038" target="_blank" >10.1088/1742-6596/898/5/052038</a>

Result language

angličtina

Original language name

Provenance-aware optimization of workload for distributed data production

Original language description

Distributed data processing in High Energy and Nuclear Physics (HENP) is a prominent example of big data analysis. Having petabytes of data being processed at tens of computational sites with thousands of CPUs, standard job scheduling approaches either do not address well the problem complexity or are dedicated to one specific aspect of the problem only (CPU, network or storage). Previously we have developed a new job scheduling approach dedicated to distributed data production – an essential part of data processing in HENP (pre- processing in big data terminology). In this contribution, we discuss the load balancing with multiple data sources and data replication, present recent improvements made to our planner and provide results of simulations which demonstrate the advantage against standard scheduling policies for the new use case. Multi-source or provenance is common in computing models of many applications whereas the data may be copied to several destinations. The initial input data set would hence be already partially replicated to multiple locations and the task of the scheduler is to maximize overall computational throughput considering possible data movements and CPU allocation. The studies have shown that our approach can provide a significant gain in overall computational performance in a wide scope of simulations considering realistic size of computational Grid and various input data distribution.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Journal of Physics: Conference Series, vol. 898

ISBN

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ISSN

1742-6588

e-ISSN

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Number of pages

8

Pages from-to

052038

Publisher name

Institute of Physics Publishing

Place of publication

United Kingdom

Event location

United Kingdom

Event date

Jan 1, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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