An evaluation of linear models for host load prediction

Dinda, Peter A.; O’Hallaron, David R.

doi:10.1109/hpdc.1999.805285

Cited by 76 publications

(20 citation statements)

References 22 publications

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“…In [8] the authors present a comparison between different classic linear models for time series forecasting used to predict load on a single Unix machine. However, the parameters of these models need to be selected by the user according to the characteristics of the particular time series of values that is taken into consideration.…”

Section: Control Experimentsmentioning

confidence: 99%

Predictive Resource Scheduling in Computational Grids

Chapman

Musolesi

Emmerich

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

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Abstract-The integration of clusters of computers into computational grids has recently gained the attention of many computational scientists. While considerable progress has been made in building middleware and workflow tools that facilitate the sharing of compute resources, little attention has been paid to grid scheduling and load balancing techniques to reduce job waiting time. Based on a detailed analysis of usage characteristics of an existing grid that involves a large CPU cluster, we observe that grid scheduling decisions can be significantly improved if the characteristics of current usage patterns are understood and extrapolated into the future. We describe a formal framework that uses Kalman filter theory to predict future CPU resource utilisation. This ability to predict future resource utilisation forms the basis for significantly improved grid scheduling decisions. The paper describes the architecture for such a prediction and grid scheduling framework and its implementation using Condor. By way of replicated experiments we demonstrate that the prediction achieves a precision within 15-20% of the utilisation later observed and can significantly improve scheduling quality, compared to approaches that only take into account current load indicators.

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Section: Control Experimentsmentioning

confidence: 99%

Predictive Resource Scheduling in Computational Grids

Chapman

Musolesi

Emmerich

et al. 2007

2007 IEEE International Parallel and Distributed Processing Symposium

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“…Computing these is not the subject of the paper but is an important component of any resource selection framework that has been addressed in related research [8,14,26].…”

Section: Overview and Validation Frameworkmentioning

confidence: 99%

Performance Estimation for Scheduling on Shared Networks

Subhlok

Venkataramaiah

2003

Job Scheduling Strategies for Parallel Processing

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“…Measurement and prediction of CPU availability has been studied in [11,37]. Measurement and modeling of network bandwidth and latency is a very active area of research [7,14,21,30].…”

Section: Related Workmentioning

confidence: 99%

Performance prediction with skeletons

Sodhi

Subhlok

2007

Cluster Comput

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The performance skeleton of an application is a short running program whose performance in any scenario reflects the performance of the application it represents. Specifically, the execution time of the performance skeleton is a small fixed fraction of the execution time of the corresponding application in any execution environment. Such a skeleton can be employed to quickly estimate the performance of a large application under existing network and node sharing. This paper presents a framework for automatic construction of performance skeletons of a specified execution time and evaluates their use in performance prediction with CPU and network sharing. The approach is based on capturing the execution behavior of an application and automatically generating a synthetic skeleton program that reflects that execution behavior.The paper demonstrates that performance skeletons running for a few seconds can predict the application execution time fairly accurately. Relationship of skeleton execution time, application characteristics, and nature of resource sharing, to accuracy of skeleton based performance prediction, is analyzed in detail. The goal of this research is accurate performance estimation in heterogeneous and shared computational grids.

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An evaluation of linear models for host load prediction

Cited by 76 publications

References 22 publications

Predictive Resource Scheduling in Computational Grids

Predictive Resource Scheduling in Computational Grids

Performance Estimation for Scheduling on Shared Networks

Performance prediction with skeletons

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