Adaptive run-time prediction in heterogeneous environments

ABSTRACT

In this article we describe an approach for the prediction of the run-time of jobs in heterogeneous environments that applies a meta-prediction algorithm working in multiple phases. For an efficient utilization of hardware resources, it is necessary to support schedulers with detailed information about the jobs that are going to be dispatched. One technique is to provide accurate forecasts of application run-times. A large number of current approaches focus on limited sets of prediction techniques (often linear ones) whereas most times only one is deployed on a dataset, ignoring the different characteristics that are "denoted" inherently by the data and therefore are not obvious. For that reason we are proposing an adaptive system for run-time prediction that offers a large set of different and differently parameterized predictors respectively, whereas only the appropriate prediction techniques for specifically filtered clusters of jobs are executed.

REFERENCES

• 1: Christian Glasner , Jens Volkert, An Architecture for an Adaptive Run-time Prediction System, Proceedings of the 2008 International Symposium on Parallel and Distributed Computing, p.275-282, July 01-05, 2008 [doi>10.1109/ISPDC.2008.34]
• 2: Warren Smith, Improving resource selection and scheduling using predictions, Grid resource management: state of the art and future trends, Kluwer Academic Publishers, Norwell, MA, 2004
• 3: Rich Wolski, Experiences with predicting resource performance on-line in computational grid settings, ACM SIGMETRICS Performance Evaluation Review, v.30 n.4, March 2003 [doi>10.1145/773056.773064]

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS
      C.2.4 Distributed Systems

Additional Classification:
  D. Software
  D.4 OPERATING SYSTEMS
      D.4.8 Performance
          Subjects: Modeling and prediction

General Terms:
Algorithms, Design

Keywords:
adaptive prediction, forecasting, grid computing, heterogeneous environments