What is it about?
A neural admission control framework with action termination is proposed to adapt data-intensive systems to more appropriate conditions in enterprise environments with established goals for system's performance and revenue generation. Different control policies are derived and evaluated under various load patterns, service level requirements, and action types with distinct run-time characteristics. This approach appears eminently suitable for systems with harsh execution time Service Level Agreements, or systems running under conditions of hard pressure on power supply or other constraints. Moreover, the proposed framework can be combined with available toolkits to support deployment of autonomous controllers in cloud-based enterprise information systems.
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Why is it important?
A machine-learning driven control mechanism, employing neural networks, is derived and applied within data-intensive systems. The experimental results demonstrate performance characteristics and benefits as well as implications of termination control when applied to different action types with distinct run-time characteristics. This approach appears eminently suitable for systems with harsh execution time Service Level Agreements, or systems running under conditions of hard pressure on power supply or other constraints.
Perspectives
Although most modern cloud-based enterprise systems, or operating systems, do not commonly allow configurable/automatic termination of processes, tasks or actions, it is common practice for systems administrators to manually terminate, or stop, tasks or actions at any level of the system. The paper proposes an approach based on neural networks that can be combined with other available tool-kits to support deployment of autonomous controllers in high-dimensional enterprise information systems.
George Magoulas
University of London
Read the Original
This page is a summary of: Neural adaptive admission control framework: SLA-driven action termination for real-time application service management, Enterprise Information Systems, March 2019, Taylor & Francis,
DOI: 10.1080/17517575.2019.1585578.
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