Self-Healing by Means of Runtime Execution Profiling

TL;DR

This paper introduces a runtime profiling technique that enables self-healing applications to autonomously recover from faults by matching current failure scenarios to known fault models, reducing manual troubleshooting.

Contribution

It proposes a novel method of fault detection and recovery using execution profiling and model matching, advancing autonomous fault handling in software systems.

Findings

Effective fault matching with minimal overhead

Improved system stability after failures

Reduced need for user intervention

Abstract

A self-healing application brings itself into a stable state after a failure put the software into an unstable state. For such self-healing software application, finding fix for a previously unseen fault is a grand challenge. Asking the user to provide fixes for every fault is bad for productivity, especially when the users are non-savvy in technical aspect of computing. If failure scenarios come into existence, the user wants the runtime environment to handle those situations autonomically. This paper presents a new technique of finding self-healing actions by matching a fault scenario to already established fault models. By profiling and capturing runtime parameters and execution pathWays, stable execution models are established and later are used to match with an unstable execution scenario. Experimentation and results are presented that showed that even with additional overheads; this technique can prove beneficial for autonomically healing faults and reliving system administrators from mundane troubleshooting situations.