Performance Evaluation of Architectures for Distributed Real-Time Communication (DiRC)



Research project financed by

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Funded period: October 2004 - September 2008

DiRC Project Team

Researchers
Partners
Dieter Hemkemeyer (Siemens AG)
Rainer Zimmermann (Siemens AG)


Summary

In this project, we focus architectures for distributed real-time communication. In particular, we study eventing sytems that are implemented, e.g., according to the publish/subscribe paradigm. They may be used as a middleware in distributed applications.

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Both information providers -- so-called publishers -- and information consumers -- so-called subscribers -- register at the server. The publishers send messages to the server that forwards them to all subscribers. Optionally, subscribers can install filters on the server that select only a certain subset of messages for them. Currently we study the following problems.
  • There are no or only insufficient performance evaluation studies about the capacity of JMS servers. We tested several types from different vendors and assessed their capacity in different application scenarios, in particular with and without filters. Our measurement results showed that the capacity of the servers ranges over several orders of magnitude depending on the server type and the application scenario.
  • Eventing systems in large distributed applications must handle an enormous message rate. The applications often have real-time or quasi-real-time requirements, i.e., the delay observed by the messages at the JMS server must be small. As mentioned above, the service time for messages ranges over several orders of magnitude. We considered different application scenarios to assess a reasonable upper bound on the variation of the service time and derived the waiting time distribution for the messages. The results showed that the waiting time is short even at a high server load of 90% if the server capacity is sufficiently large.
  • The capacity of a single server may not suffice to forward the traffic of a large-scale application. Therefore, we look at the performance of server clusters.

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