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.
Highlights
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
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
The detailed working and relevant results of each autonomic manager are provided in sections (a)
Summary
It aims at the provision of self-*. Capabilities to computing systems to make them behave like the human autonomous nervous system. The idea is to shift the human task of software controlling to policy and rules definition [1,2,3]. Self-healing, self-configuring, self-protecting, self-optimizing, self-awareness, context-awareness, openness and anticipation are basic self-* characteristics of autonomic computing systems [1,2,3]. The introduction of self-* capabilities as a service (S*SAAS) for software self-management is motivated by taking advantage of the powerful processing and storage abilities of cloud computing. With the Symmetry 2018, 10, 141; doi:10.3390/sym10050141 www.mdpi.com/journal/symmetry
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