Self-Healing model for software application

Kumar, Kethavath Prem; Naik, Nenavath Srinivas

doi:10.1109/icraie.2014.6909207

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…To increase the requirement of self-management services, the system achieves a significant improvement in the scalability, elasticity, autonomic efficiency, and issue resolving time, compared to state-of-the-art approaches like IBM [1,4,7,8], IMD [13], the data stream model and SOA [16], and the policy-base model [17]. These approaches implement static self-management services, whereas the proposed approach brings dynamism.…”

Section: Increase/decrease Of Server Loadmentioning

confidence: 99%

“…Some research works [4,8] present conceptual models that match IBM's concept. Peer2peer, aggregator-escalator peer, and chain of configurator and configuration manager are different architectural approaches to adaptive computing [4].…”

Section: Related Workmentioning

confidence: 99%

“…Different architectures [1,3,7,13], models [4,8,16,17] and design patterns [2,21,23] exist for autonomic computing systems. A great deal of research [1,3,7,9,10] supports IBM's reference model for autonomic computing systems.…”

Section: Related Workmentioning

confidence: 99%

“…This produces a better response rate compared to the existing approach. By introducing S*SAAS, the autonomic computing paradigm achieves a significant improvement in security, scalability, elasticity, autonomic efficiency, and issue resolving time, compared to the state-of-the-art approaches (IBM [1,4,7,8], Intelligent Machine Design (IMD) [13], the data stream model and Service Oriented Architecture (SOA) [16], and the policy-based model [17]) that implement static self-management services.…”

mentioning

confidence: 99%

“…The most widely used model for autonomic computing is International Business Machines (IBM)'s reference model [1,[3][4][5][6][7][8][9][10]. The two main components of the reference model are the autonomic manager (AM) and managed resource (MR).…”

mentioning

confidence: 99%

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A Secure, Scalable and Elastic Autonomic Computing Systems Paradigm: Supporting Dynamic Adaptation of Self-* Services from an Autonomic Cloud

2018

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Abstract:Autonomic computing embeds self-management features in software systems using external feedback control loops, i.e., autonomic managers. In existing models of autonomic computing, adaptive behaviors are defined at the design time, autonomic managers are statically configured, and the running system has a fixed set of self-* capabilities. An autonomic computing design should accommodate autonomic capability growth by allowing the dynamic configuration of self-* services, but this causes security and integrity issues. A secure, scalable and elastic autonomic computing system (SSE-ACS) paradigm is proposed to address the runtime inclusion of autonomic managers, ensuring secure communication between autonomic managers and managed resources. Applying the SSE-ACS concept, a layered approach for the dynamic adaptation of self-* services is presented with an online 'Autonomic_Cloud' working as the middleware between Autonomic Managers (offering the self-* services) and Autonomic Computing System (requiring the self-* services). A stock trading and forecasting system is used for simulation purposes. The security impact of the SSE-ACS paradigm is verified by testing possible attack cases over the autonomic computing system with single and multiple autonomic managers running on the same and different machines. The common vulnerability scoring system (CVSS metric) shows a decrease in the vulnerability severity score from high (8.8) for existing ACS to low (3.9) for SSE-ACS. Autonomic managers are introduced into the system at runtime from the Autonomic_Cloud to test the scalability and elasticity. With elastic AMs, the system optimizes the Central Processing Unit (CPU) share resulting in an improved execution time for business logic. For computing systems requiring the continuous support of self-management services, the proposed system achieves a significant improvement in security, scalability, elasticity, autonomic efficiency, and issue resolving time, compared to the state-of-the-art approaches.

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