TY - GEN
T1 - Improving job scheduling on a heterogeneous cluster by predicting job execution times using heuristics
AU - Brandstätter-Müller, Hannes
AU - Parsapour, Bahram
AU - Hölzlwimmer, Andreas
AU - Lirk, Gerald
AU - Kulczycki, Peter
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - In this paper, we propose the scheduling system for the Bioinformatics Resource Facility Hagenberg (BiRFH). This system takes advantage of the fact that the facility offers tailored solutions for the customers, which includes having a limited amount of different programs available. Additionally, the BiRFH system provides access to different hardware platforms (standard CPU, GPGPU on NVIDIA Cuda, and IMB Cell on Sony Playstation machines) with multiple versions of the same algorithm optimized for these platforms. The BiRFH scheduling system takes these into account and uses knowledge about past runs and run times to predict the expected run time of a job. That leads to a better scheduling and resource usage. The prediction and scheduling use heuristic and artificial intelligence methods to achieve acceptable results. The paper presents the proposed prediction method as well as an overview of the scheduling algorithm.
AB - In this paper, we propose the scheduling system for the Bioinformatics Resource Facility Hagenberg (BiRFH). This system takes advantage of the fact that the facility offers tailored solutions for the customers, which includes having a limited amount of different programs available. Additionally, the BiRFH system provides access to different hardware platforms (standard CPU, GPGPU on NVIDIA Cuda, and IMB Cell on Sony Playstation machines) with multiple versions of the same algorithm optimized for these platforms. The BiRFH scheduling system takes these into account and uses knowledge about past runs and run times to predict the expected run time of a job. That leads to a better scheduling and resource usage. The prediction and scheduling use heuristic and artificial intelligence methods to achieve acceptable results. The paper presents the proposed prediction method as well as an overview of the scheduling algorithm.
KW - Algorithms
KW - Bioinformatics
KW - High performance computing
KW - Molecular biology
UR - http://www.scopus.com/inward/record.url?scp=84871261210&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9788890372445
T3 - 23rd European Modeling and Simulation Symposium, EMSS 2011
SP - 488
EP - 495
BT - 23rd European Modeling and Simulation Symposium, EMSS 2011
T2 - 23rd European Modeling and Simulation Symposium, EMSS 2011
Y2 - 12 September 2011 through 14 September 2011
ER -