Achieving self-healing in service delivery software systems by means of case-based reasoning

Montani, Stefania; Anglano, Cosimo

doi:10.1007/s10489-007-0047-1

Cited by 30 publications

(20 citation statements)

References 25 publications

(33 reference statements)

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“…Montani et al described a Case based reasoning (CBR) approach that gave capabilities of self-healing to distributed software systems, by means of real world application experimental results [8]. Park et al proposed a self-healing mechanism that diagnose, heal and monitor its internal error by contextual information and self-awareness [9].…”

Section: Related Workmentioning

confidence: 99%

An Automated Approach for Software Fault Detection and Recovery

Hudaib¹,

Fakhouri²

2016

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Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

An Automated Approach for Software Fault Detection and Recovery

Hudaib¹,

Fakhouri²

2016

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“…This technique picks an appropriate action Self-configuration learning [3] from a finite pool of actions using reinforcement learning Decision tree Various parameters representing the system Self-healing learning [8] state are ranked based on the information gain value and used to determine problem in a distributed environment Conversational CBR A case is prepared by asking various Self-healing approach questions about the system state and [4], [37], [38] simple matching with previous cases is used to diagnose the problem Hybrid A hybrid approach using conversational Self-healing approach CBR and rule-based technique is used to [20] diagnose the problem and find solution Conventional CBR This approach exploits various features of Self-configuration approach CBR like various similarity options, revise and [27]- [29] retain strategies with different measures ciency. These approaches have following common limitations:…”

Section: Literature Review Of Existing Applicationsmentioning

confidence: 99%

“…Self-configuration in autonomic systems is a continuous process and the system has to keep on configuring itself with the passage of time and varying environmental conditions. Various artificial intelligence techniques like rule-based systems [24], control theory [2], [12], [15], [25], [44], AI planning [5], case-based reasoning (CBR) [27], [28], [38] etc. have been used to devise new configuration settings in autonomic systems.…”

Section: Introductionmentioning

confidence: 99%

Improving Efficiency of Self-Configurable Autonomic Systems Using Clustered CBR Approach

Khan¹,

Awais²,

Shamail³

2010

IEICE Trans. Inf. & Syst.

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SUMMARYInspired from natural self-managing behavior of the human body, autonomic systems promise to inject self-managing behavior in software systems. Such behavior enables self-configuration, self-healing, self-optimization and self-protection capabilities in software systems. Selfconfiguration is required in systems where efficiency is the key issue, such as real time execution environments. To solve self-configuration problems in autonomic systems, the use of various problem-solving techniques has been reported in the literature including case-based reasoning. The casebased reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities. The learning process improves as more experience is added in the case-base in the form of cases. This results in a larger case-base. A larger case-base reduces the efficiency in terms of computational cost. To overcome this efficiency problem, this paper suggests to cluster the case-base, subsequent to find the solution of the reported problem. This approach reduces the search complexity by confining a new case to a relevant cluster in the case-base. Clustering the case-base is a one-time process and does not need to be repeated regularly. The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework. The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA). This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case-base in the proposed framework. The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy, Recall and Precision (ARP) and computational efficiency.

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“…Some self-management capabilities of autonomic software systems have been achieved using CBR in literature [15], [3], [11]. With the arrival of new problems, case-base keeps on growing continuously and its size becomes very large.…”

Section: Enabling Self-configuration Using Cbrmentioning

confidence: 99%

“…When a new problem occurs, it needs to be compared with all cases of the case-base and this process becomes quite time consuming when every problems has to be compared with the whole large case-base. Inspired from the approach suggested for self-healing in [15], we suggest to maintain a template case-base and restrict fast growth of the case-base with arrival of new problems. Template case-base contains all possible general cases.…”

Section: Enabling Self-configuration Using Cbrmentioning

confidence: 99%

Enabling Self-Configuration in Autonomic Systems Using Case-Based Reasoning with Improved Efficiency

Khan

Awais

Shamail

2008

Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08)

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Autonomic computing is an emerging philosophy which promises to enable self-management capabilities in software systems. These self-management properties include self-configuration, self-healing, self-protection, self-optimization, self-awareness and self-governance. Enabling any of these properties in software systems is an open challenge. Exhibiting such self-management behavior is a continuous process in the software life cycle. Case-based reasoning is a problem solving methodology which exploits past experience. Past experience is maintained in the form of problemsolution pairs, also called cases. On the arrival of new problem, solution of past similar problems is used after appropriate adaptation. This problem solving technique can be used to achieve some of the properties of autonomic systems based on experience. To find this solution, entire experience space is searched which reduces efficiency. To overcome this efficiency problem, we restrict the fast growth of case repository, so that every time we have to search a very limited number of cases. We applied the proposed approach on a simulation of Autonomic Forest Fire application for self-configuration capability. Our results show that the proposed approach is quite promising in terms of accuracy as well as efficiency.

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Achieving self-healing in service delivery software systems by means of case-based reasoning

Cited by 30 publications

References 25 publications

An Automated Approach for Software Fault Detection and Recovery

An Automated Approach for Software Fault Detection and Recovery

Improving Efficiency of Self-Configurable Autonomic Systems Using Clustered CBR Approach

Enabling Self-Configuration in Autonomic Systems Using Case-Based Reasoning with Improved Efficiency

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