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STDADS: An Efficient Slow Task Detection Algorithm for Deadline Schedulers.

Utsav Upadhyay¹, Geeta Sikka¹

¹National Institute of Technology, Jalandhar, India.

|January 30, 2020

Summary

This summary is machine-generated.

This study introduces an improved speculative task detection algorithm for Apache Hadoop Deadline Schedulers. It enhances job completion rates and task detection accuracy for time-sensitive big data processing.

Keywords:

Deadline Scheduler Hadoop MapReduce speculative execution

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Area of Science:

Computer Science
Distributed Systems
Big Data Analytics

Background:

Apache Hadoop, an open-source implementation of Google's MapReduce, processes large datasets.
Hadoop MapReduce and HDFS manage data and processing efficiently.
Speculative task execution improves Quality of Service (QoS) by re-executing tasks on other nodes.

Purpose of the Study:

To address the lack of integration between speculative task execution mechanisms and Deadline Schedulers in Hadoop.
To present an improved speculative task detection algorithm tailored for Deadline Schedulers.

Main Methods:

Developed a novel speculative task detection algorithm specifically for Hadoop Deadline Schedulers.
Integrated node performance tracking for more efficient speculative task re-execution.
Evaluated the algorithm's impact on QoS metrics for jobs with deadlines.

Main Results:

Successfully improved QoS for Hadoop clusters handling deadline-driven jobs.
Demonstrated enhancements in the percentage of successfully completed jobs.
Reduced speculative task detection time and improved detection accuracy.
Decreased the percentage of incorrectly flagged speculative tasks.

Conclusions:

Regular tracking of node performance is crucial for efficient speculative task execution.
The proposed algorithm significantly enhances Hadoop cluster performance for deadline-based workloads.
The improved algorithm offers better QoS by optimizing speculative task handling in deadline-aware scheduling.