Program Behavior Prediction Using a Statistical Metric Model

Copyright © (2010) by Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distrubuted for profit or commericial advantage. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee.

Adaptive computing systems rely on accurate projections of program behavior to understand and respond to the dynamically-varying application characteristics. In this study, we propose a Statistical Metric Model (SMM) that is system- and metric-independent for predicting program phases. SMM is a probability distribution over workload patterns and it attempts to model how frequently a specific behavior occurs. Maximum Likelihood Estimation (MLE) criterion is used to estimate the parameters of the SMM. The model parameters are further refined with a smoothing method to improve the robustness of the proposed program model. Finite sequences of execution metrics, obtained from running workloads are used to train the SMM. The SMM learns the application patterns during runtime, and at the same time predicts the upcoming program phases based on what it has learned so far. The finite prediction horizon can be adjusted based on the available data, which is used to estimate the model parameters. An extensive and rigorous series of prediction experiments demonstrate the superior performance of the SMM predictor over existing predictors on a wide range of benchmarks. For some benchmarks the improvements in prediction error are as much as 10-fold and 3-fold respectively, compared to the existing last-value and table-based prediction approaches.

By: Ruhi Sarikaya; Canturk Isci; Alper Buyuktosunoglu

Published in: ACM Performance Evaluation Review, volume 38, (no 1), pages 371-2 in 2010


This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.


Questions about this service can be mailed to .