Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction

TL;DR

This paper presents a machine learning-based profiling system for bioinformatics cluster tasks to predict failures, aiming to improve resource scheduling by understanding IO-bound task behaviors.

Contribution

It introduces a novel machine learning approach for profiling and failure prediction of IO-intensive tasks in bioinformatics clusters, enhancing scheduling efficiency.

Findings

Analyzed 6.7K CPU hours of cluster data over two years.

Developed a machine learning agent for failure prediction.

Improved understanding of IO-bound task failure conditions.

Abstract

Motivation: Traditional computational cluster schedulers are based on user inputs and run time needs request for memory and CPU, not IO. Heavily IO bound task run times, like ones seen in many big data and bioinformatics problems, are dependent on the IO subsystems scheduling and are problematic for cluster resource scheduling. The problematic rescheduling of IO intensive and errant tasks is a lost resource. Understanding the conditions in both successful and failed tasks and differentiating them could provide knowledge to enhancing cluster scheduling and intelligent resource optimization. Results: We analyze a production computational cluster contributing 6.7 thousand CPU hours to research over two years. Through this analysis we develop a machine learning task profiling agent for clusters that attempts to predict failures between identically provision requested tasks.