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Related Experiment Video

Updated: Apr 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
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Risk intelligence: making profit from uncertainty in data processing system.

Si Zheng1, Xiangke Liao1, Xiaodong Liu1

  • 1School of Computer, National University of Defense Technology, China.

Thescientificworldjournal
|June 3, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces RiskI, a novel algorithm for extreme scale data processing systems. RiskI improves job execution response times by predicting task performance and managing execution uncertainty, outperforming traditional methods.

Related Experiment Videos

Last Updated: Apr 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Big Data

Background:

  • Fault tolerance is critical in extreme scale data processing systems.
  • Proactive fault tolerance, like speculative execution, aims to reduce job response times.
  • Accurate prediction of task execution is a long-standing challenge for efficient fault tolerance.

Purpose of the Study:

  • To design and implement a novel proactive fault tolerance scheme that addresses task execution uncertainty.
  • To accelerate task executions in distributed systems by leveraging uncertainty.
  • To improve the efficiency of fault tolerance mechanisms in large-scale data processing.

Main Methods:

  • Developed RiskI, a profile-based prediction algorithm.
  • Integrated RiskI with a risk-aware task assignment algorithm.
  • Implemented the proposed algorithms within the Hadoop 0.21.0 framework.

Main Results:

  • Experimental results demonstrate significant improvements in response time.
  • Achieved a 46% reduction in response time compared to the traditional LATE algorithm.
  • Maintained system throughput while improving response times.

Conclusions:

  • The proposed RiskI algorithm effectively handles task execution uncertainty in extreme scale systems.
  • Leveraging uncertainty in task execution can lead to performance benefits.
  • RiskI offers a more efficient approach to proactive fault tolerance than existing methods.