Paper Info
Title
An Offline Demand Estimation Method for Multi-threaded Applications
Abstract
Parameterizing performance models for multi-threaded enterprise applications requires finding the service rates offered by worker threads to the incoming requests. Statistical inference on monitoring data is here helpful to reduce the overheads of application profiling and to infer missing information. While linear regression of utilization data is often used to estimate service rates, it suffers erratic performance and also ignores a large part of application monitoring data, e.g., response times. Yet inference from other metrics, such as response times or queue-length samples, is complicated by the dependence on scheduling policies. To address these issues, we propose novel scheduling-aware estimation approaches for multi-threaded applications based on linear regression and maximum likelihood estimators. The proposed methods estimate demands from samples of the number of requests in execution in the worker threads at the admission instant of a new request. Validation results are presented on simulated and real application datasets for systems with multi-class requests, class switching, and admission control.
Year
DOI
Venue
2013
10.1109/MASCOTS.2013.10
MASCOTS
Keywords
Field
DocType
application profiling,response time,multi-threaded applications,multi-threaded enterprise application,utilization data,service rate,linear regression,offline demand,estimation method,application monitoring data,multi-threaded application,worker thread,real application datasets,multi threading,scheduling,regression analysis,maximum likelihood estimation
Multithreading,Admission control,Profiling (computer programming),Scheduling (computing),Computer science,Inference,Real-time computing,Thread (computing),Statistical inference,Estimator
Conference
ISSN
Citations 
PageRank 
1526-7539
9
0.61
References 
Authors
19
3
Name
Order
Citations
PageRank
Juan F. Pérez110611.80
Sergio Pacheco-Sanchez2853.98
Giuliano Casale3121390.40