NDLI: Prediction f based models for evaluating backfilling scheduling policies

Content Provider	IEEE Xplore Digital Library
Author	Guim, F. Corbalan, J. Labarta, J.
Copyright Year	2007
Description	Author affiliation: Barcelona Supercomput. Center, Barcelona (Guim, F.; Corbalan, J.; Labarta, J.)
Abstract	The research on the usage of prediction techniques in HPC scheduling policies rather than user estimates has increased it relevance these recent years. In the coming scheduling architectures, like grids and very heterogeneous computational resources, such techniques are having a crucial relevance due to users in most of the cases will not have enough information or enough skills for specify for how long will their jobs run. Many studies have analyzed the impact of the user runtime estimates accuracy in the performance of the scheduling policies. Using user runtime estimation models, such as the f-model, researchers have evaluated how the accuracy of the runtime estimates provided by the user at the job submission can affect the performance of the backfilling policies and its variants. However, these traditional estimation models can not applied to backfilling scheduling policies that use runtime predictions rather than user estimates. Clearly, predictions can not be characterized with these models. For instance because the underestimation of the runtime is not considered by them and obviously it can occurs. In this paper we describe and evaluate a set of f-model based prediction models that characterize the behavior that prediction techniques have shown in HPC centers. They have been designed for evaluate scheduling policies that use predictions rather than user estimates.
Starting Page	9
Ending Page	17
File Size	284911
Page Count	9
File Format	PDF
ISBN	0769530494
DOI	10.1109/PDCAT.2007.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Runtime Processor scheduling Computer architecture Grid computing Distributed computing Performance analysis Scheduling algorithm
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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