Slingshot: Time-CriticalMulticast for Clustered Applications
NCA '05 Proceedings of the Fourth IEEE International Symposium on Network Computing and Applications
A middleware for autonomic QoS management based on learning
SEM '05 Proceedings of the 5th international workshop on Software engineering and middleware
Analysis of the Message Waiting Time for the FioranoMQ JMS Server
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
A distributed architecture meta-model for self-managed middleware
Proceedings of the 5th workshop on Adaptive and reflective middleware (ARM '06)
Grid-based large-scale Web3D collaborative virtual environment
Proceedings of the twelfth international conference on 3D web technology
ISORC '08 Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing
Deep middleware for the divergent Grid
Proceedings of the ACM/IFIP/USENIX 2005 International Conference on Middleware
Future Generation Computer Systems
A Reinforcement Learning Approach to Online Web Systems Auto-configuration
ICDCS '09 Proceedings of the 2009 29th IEEE International Conference on Distributed Computing Systems
Evaluating Transport Protocols for Real-Time Event Stream Processing Middleware and Applications
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part I
Q-clouds: managing performance interference effects for QoS-aware clouds
Proceedings of the 5th European conference on Computer systems
Adapting and evaluating distributed real-time and embedded systems in dynamic environments
Proceedings of the First International Workshop on Data Dissemination for Large Scale Complex Critical Infrastructures
Ricochet: lateral error correction for time-critical multicast
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
An aspect-oriented approach for developing self-adaptive fractal components
SC'06 Proceedings of the 5th international conference on Software Composition
Towards service awareness and autonomic features in a SIP-Enabled network
WAC'05 Proceedings of the Second international IFIP conference on Autonomic Communication
Proceedings of the 5th ACM international conference on Distributed event-based system
Autonomic computing driven by feature models and architecture in FamiWare
ECSA'11 Proceedings of the 5th European conference on Software architecture
A highly efficient cloud-based architecture for large-scale STB event processing: industry article
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Proceedings of the 9th Middleware Doctoral Symposium of the 13th ACM/IFIP/USENIX International Middleware Conference
Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments
International Journal of Adaptive, Resilient and Autonomic Systems
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Enterprise distributed real-time and embedded (DRE) publish/subscribe (pub/sub) systems manage resources and data that are vital to users. Cloud computing---where computing resources are provisioned elastically and leased as a service---is an increasingly popular deployment paradigm. Enterprise DRE pub/sub systems can leverage cloud computing provisioning services to execute needed functionality when on-site computing resources are not available. Although cloud computing provides flexible on-demand computing and networking resources, enterprise DRE pub/sub systems often cannot accurately characterize their behavior a priori for the variety of resource configurations cloud computing supplies (e.g., CPU and network bandwidth), which makes it hard for DRE systems to leverage conventional cloud computing platforms. This paper provides two contributions to the study of how autonomic configuration of DRE pub/sub middleware can provision and use on-demand cloud resources effectively. We first describe how supervised machine learning can configure DRE pub/sub middleware services and transport protocols autonomically to support end-to-end quality-of-service (QoS) requirements based on cloud computing resources. We then present results that empirically validate how computing and networking resources affect enterprise DRE pub/sub system QoS. These results show how supervised machine learning can configure DRE pub/sub middleware adaptively in μsec with bounded time complexity to support key QoS reliability and latency requirements.