A Survey on Self-healing Software System
- URL: http://arxiv.org/abs/2403.00455v1
- Date: Fri, 1 Mar 2024 11:23:41 GMT
- Title: A Survey on Self-healing Software System
- Authors: Zahra Yazdanparast
- Abstract summary: The main purpose of self-healing is to have an automatic system that can heal itself without human intervention.
In this study, different self-healing methods are categorized and a summary of them is presented.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With the increasing complexity of software systems, it becomes very difficult
to install, configure, adjust, and maintain them. As systems become more
interconnected and diverse, system architects are less able to predict and
design the interaction between components, deferring the handling of these
issues to runtime. One of the important problems that occur during execution is
system failures, which increase the need for self-healing systems. The main
purpose of self-healing is to have an automatic system that can heal itself
without human intervention. This system has predefined actions and procedures
that are suitable for recovering the system from different failure modes. In
this study, different self-healing methods are categorized and a summary of
them is presented.
Related papers
- A Graphical Modeling Language for Artificial Intelligence Applications
in Automation Systems [69.50862982117127]
An interdisciplinary graphical modeling language that enables the modeling of an AI application as an overall system comprehensible to all disciplines does not yet exist.
This paper presents a graphical modeling language that enables consistent and understandable modeling of AI applications in automation systems at system level.
arXiv Detail & Related papers (2023-06-20T12:06:41Z) - Incremental procedural and sensorimotor learning in cognitive humanoid
robots [52.77024349608834]
This work presents a cognitive agent that can learn procedures incrementally.
We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent.
Results show that this approach is capable of solving complex tasks incrementally.
arXiv Detail & Related papers (2023-04-30T22:51:31Z) - Interactive System-wise Anomaly Detection [66.3766756452743]
Anomaly detection plays a fundamental role in various applications.
It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data.
We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
arXiv Detail & Related papers (2023-04-21T02:20:24Z) - CHESS: A Framework for Evaluation of Self-adaptive Systems based on
Chaos Engineering [0.6875312133832078]
There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems.
There is a lack of systematic evaluation methods for self-adaptive and self-healing systems.
We propose CHESS to address this gap by evaluating self-adaptive and self-healing systems through fault injection based on chaos engineering.
arXiv Detail & Related papers (2023-03-13T17:00:55Z) - On Evaluating Self-Adaptive and Self-Healing Systems using Chaos
Engineering [0.6117371161379209]
We propose CHESS, an approach for the systematic evaluation of self-adaptive and self-healing systems.
Chaos engineering is a methodology for subjecting a system to unexpected conditions and scenarios.
We investigate the viability of this approach through an exploratory study on a self-healing smart office environment.
arXiv Detail & Related papers (2022-08-28T14:38:57Z) - Parallelization of Software Systems Test Case Selection Algorithm Based on Singular Value Decomposition [0.0]
This test seeks to re-measure affected sections to prevent these abnormalities.
We try to cluster the changes of our software system based on the system functions by singular value decomposition.
In order to increase speedup, our calculations were performed in parallel on shared memory systems.
arXiv Detail & Related papers (2022-06-11T10:33:34Z) - Lifelong Learning Metrics [63.8376359764052]
The DARPA Lifelong Learning Machines (L2M) program seeks to yield advances in artificial intelligence (AI) systems.
This document outlines a formalism for constructing and characterizing the performance of agents performing lifelong learning scenarios.
arXiv Detail & Related papers (2022-01-20T16:29:14Z) - Controlling nonlinear dynamical systems into arbitrary states using
machine learning [77.34726150561087]
We propose a novel and fully data driven control scheme which relies on machine learning (ML)
Exploiting recently developed ML-based prediction capabilities of complex systems, we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states coming from any initial state.
Having this highly flexible control scheme with little demands on the amount of required data on hand, we briefly discuss possible applications that range from engineering to medicine.
arXiv Detail & Related papers (2021-02-23T16:58:26Z) - Adaptive Immunity for Software: Towards Autonomous Self-healing Systems [0.6117371161379209]
Self-healing software systems can detect, diagnose, and contain unanticipated problems at runtime.
Recent advances in machine learning may be learned by observing the system.
Artificial immune systems are particularly well-suited for building self-healing systems.
arXiv Detail & Related papers (2021-01-07T13:22:55Z) - Learning to Control PDEs with Differentiable Physics [102.36050646250871]
We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames.
We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs.
arXiv Detail & Related papers (2020-01-21T11:58:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.