D-Factor: A Quantitative Model of Application Slow-Down in Multi-Resource Shared Systems
Abstract
Scheduling multiple jobs onto a platform enhances system utilization by sharing resources. The benefits from higher resource utilization include reduced cost to construct, operate, and maintain a system, which often include energy consumption. Maximizing these benefits comes at a price - resource contention among jobs increases job completion time. In this paper, we analyze slow-downs of jobs due to contention for multiple resources in a system; referred to as dilation factor. We observe that multiple-resource contention creates non-linear dilation factors of jobs. From this observation, we establish a general quantitative model for dilation factors of jobs in multi-resource systems. A job is characterized by a vector-valued loading statistics and dilation factors of a job set are given by a quadratic function of their loading vectors. We demonstrate how to systematically characterize a job, maintain the data structure to calculate the dilation factor (loading matrix), and calculate the dilation factor of each job. We validate the accuracy of the model with multiple processes running on a native Linux server, virtualized servers, and with multiple MapReduce workloads co-scheduled in a cluster. Evaluation with measured data shows that the D-factor model has an error margin of less than 16%. We also show thatmore »
- Authors:
-
- ORNL
- Pennsylvania State University, University Park, PA
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- OSTI Identifier:
- 1049812
- DOE Contract Number:
- DE-AC05-00OR22725
- Resource Type:
- Conference
- Resource Relation:
- Conference: 12th joint ACM SIGMETRICS / Performance conference, London, United Kingdom, 20120611, 20120615
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ACCURACY; ENERGY CONSUMPTION; EVALUATION; PERFORMANCE; PRICES; STATISTICS; VECTORS
Citation Formats
Lim, Seung-Hwan, Huh, Jae-Seok, Kim, Youngjae, Shipman, Galen M, and Das, Chita. D-Factor: A Quantitative Model of Application Slow-Down in Multi-Resource Shared Systems. United States: N. p., 2012.
Web.
Lim, Seung-Hwan, Huh, Jae-Seok, Kim, Youngjae, Shipman, Galen M, & Das, Chita. D-Factor: A Quantitative Model of Application Slow-Down in Multi-Resource Shared Systems. United States.
Lim, Seung-Hwan, Huh, Jae-Seok, Kim, Youngjae, Shipman, Galen M, and Das, Chita. 2012.
"D-Factor: A Quantitative Model of Application Slow-Down in Multi-Resource Shared Systems". United States.
@article{osti_1049812,
title = {D-Factor: A Quantitative Model of Application Slow-Down in Multi-Resource Shared Systems},
author = {Lim, Seung-Hwan and Huh, Jae-Seok and Kim, Youngjae and Shipman, Galen M and Das, Chita},
abstractNote = {Scheduling multiple jobs onto a platform enhances system utilization by sharing resources. The benefits from higher resource utilization include reduced cost to construct, operate, and maintain a system, which often include energy consumption. Maximizing these benefits comes at a price - resource contention among jobs increases job completion time. In this paper, we analyze slow-downs of jobs due to contention for multiple resources in a system; referred to as dilation factor. We observe that multiple-resource contention creates non-linear dilation factors of jobs. From this observation, we establish a general quantitative model for dilation factors of jobs in multi-resource systems. A job is characterized by a vector-valued loading statistics and dilation factors of a job set are given by a quadratic function of their loading vectors. We demonstrate how to systematically characterize a job, maintain the data structure to calculate the dilation factor (loading matrix), and calculate the dilation factor of each job. We validate the accuracy of the model with multiple processes running on a native Linux server, virtualized servers, and with multiple MapReduce workloads co-scheduled in a cluster. Evaluation with measured data shows that the D-factor model has an error margin of less than 16%. We also show that the model can be integrated with an existing on-line scheduler to minimize the makespan of workloads.},
doi = {},
url = {https://www.osti.gov/biblio/1049812},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2012},
month = {Sun Jan 01 00:00:00 EST 2012}
}