Using machine learning to maintain pub/sub system QoS in dynamic environments
Proceedings of the 8th International Workshop on Adaptive and Reflective MIddleware
Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments
International Journal of Adaptive, Resilient and Autonomic Systems
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In spite of significant work on real-time content distribution, providing consistent service quality across the Internet remains a challenge. Multicast applications further exacerbate that problem as the number of participants - clients, providers and administrative domains - and the level of heterogeneity in a single session is increased. Recently the increasing complexity of managing and operating the Internet has sparked off interest in autonomous networking. In this paper, we introduce vital enhancements to the elementary Single Source Multicast service that facilitates incremental deployment, self-configuration, self-optimization and self-healing. We further introduce an autonomic multicast architecture where a self-monitoring network layer multicast automatically adapts the network layer forwarding service to meet the service quality requirements requested by application level services.