Prediction of Queue Waiting Times for Metascheduling on Parallel Batch Systems

  • Rajath Kumar
  • Sathish VadhiyarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8828)


Prediction of queue waiting times of jobs submitted to production parallel batch systems is important to provide overall estimates to users and can also help meta-schedulers make scheduling decisions. In this work, we have developed a framework for predicting ranges of queue waiting times for jobs by employing multi-class classification of similar jobs in history. Our hierarchical prediction strategy first predicts the point wait time of a job using dynamic k-Nearest Neighbor (kNN) method. It then performs a multi-class classification using Support Vector Machines (SVMs) among all the classes of the jobs. The probabilities given by the SVM for the class predicted using k-NN and its neighboring classes are used to provide a set of ranges of predicted wait times with probabilities. We have used these predictions and probabilities in a meta-scheduling strategy that distributes jobs to different queues/sites in a multi-queue/grid environment for minimizing wait times of the jobs. Experiments with different production supercomputer job traces show that our prediction strategies can give correct predictions for about 77–87 % of the jobs, and also result in about 12 % improved accuracy when compared to the next best existing method. Experiments with our meta-scheduling strategy using different production and synthetic job traces for various system sizes, partitioning schemes and different workloads, show that the meta-scheduling strategy gives much improved performance when compared to existing scheduling policies by reducing the overall average queue waiting times of the jobs by about 47 %.


Support Vector Machine Wait Time Average Wait Time Request Size Instance Base Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by Department of Science and Technology (DST), India via the grant SR/S3/EECE/0095/2012.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Supercomputer Education and Research CenterIndian Institute of ScienceBangaloreIndia

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