Cluster reserves: a mechanism for resource management in cluster-based network servers
Proceedings of the 2000 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Sharc: Managing CPU and Network Bandwidth in Shared Clusters
IEEE Transactions on Parallel and Distributed Systems
QoS Evaluation of JMS: An Empirical Approach
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 9 - Volume 9
Integrated resource management for cluster-based internet services
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Analysis of the Message Waiting Time for the FioranoMQ JMS Server
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Throughput Performance of Java Messaging Services Using WebsphereMQ
ICDCSW '06 Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems
Self-adaptation of service level in distributed systems
Software—Practice & Experience
Self-adapting service level in Java enterprise edition
Middleware'09 Proceedings of the ACM/IFIP/USENIX 10th international conference on Middleware
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Today's entreprise-level applications are often built as an assembly of distributed components that provide the basic services required by the application logic. As the scale of these applications increases, coarse-grained components will need be decoupled and will use message-based communication, often helped by Message-Oriented Middleware or MOMs. In the Java world, a standardized interface exists for MOMs: Java Messaging Service or JMS. And like other middleware, some JMS implementations use clustering techniques to provide some level of performance and fault-tolerance. One such implementation is JORAM, which is open-source and hosted by the ObjectWeb consortium. In this paper, we describe performance modeling of various clustering configurations and validate our model with performance evaluation in a real-life cluster. In doing that, we observed that the resource-efficiency of the clustering methods can be very poor due to local instabilities and/or global load variations. To solve these issues, we provide insight into how to build autonomic capabilities on top of the JORAM middleware. Specifically, we describe a methodology to (i) dynamically adapt the load distribution among the servers (load-balancing aspect) and (ii) dynamically adapt the replication level (provisioning aspect).