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High-Availability Computing Platform with Sensor Fault Resilience.

Yen-Lin Lee1, Shinta Nuraisya Arizky1, Yu-Ren Chen1,2

  • 1Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan.

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Summary
This summary is machine-generated.

This study introduces a high availability (HA) mechanism that reconstructs fault models dynamically. It ensures continuous HA for cloud systems even when sensors fail, reducing reliance on human intervention.

Keywords:
failoverfault detection and recoveryhigh availabilityliveness detectionsensor fault

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Area of Science:

  • Computer Science
  • Cloud Computing
  • System Reliability

Background:

  • Modern computing platforms rely on multiple sensors for system monitoring and high availability (HA).
  • Sensor failures can compromise HA, necessitating human intervention to adjust fault models or repair sensors.
  • Existing HA mechanisms may be vulnerable to sensor malfunctions, impacting cloud service continuity.

Purpose of the Study:

  • To propose and evaluate a novel HA mechanism for cloud systems.
  • To enable continuous HA through dynamic fault model reconstruction.
  • To address the challenge of sensor failures impacting system fault detection.

Main Methods:

  • Implementation of a dynamic fault model reconstruction HA mechanism.
  • Deployment on a four-layer OpenStack cloud system.
  • Systematic testing with injected faults and various sensor fault scenarios.

Main Results:

  • The proposed HA mechanism accurately detects and recovers system faults despite disabled sensors.
  • Increased sensor faults lead to a rise in system fault detection time.
  • The worst-case scenario occurs when the HA mechanism degrades to a single-system-fault model.

Conclusions:

  • Dynamic fault model reconstruction effectively maintains HA in cloud systems with sensor failures.
  • The proposed mechanism enhances system resilience by adapting to sensor malfunctions.
  • Performance is dependent on the number of sensor faults, highlighting the importance of sensor reliability.