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LCR '98 Selected Papers from the 4th International Workshop on Languages, Compilers, and Run-Time Systems for Scalable Computers
AQuA: An Adaptive Architecture that Provides Dependable Distributed Objects
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An Object-level Gateway Supporting Integrated-Property Quality of Service
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The Architecture of the Remos System
HPDC '01 Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
Packaging Quality of Service Control Behaviors for Reuse
ISORC '02 Proceedings of the Fifth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
PASS - A service for Efficient Large Scale Dissemination of Time Varying Data Using CORBA
ICDCS '99 Proceedings of the 19th IEEE International Conference on Distributed Computing Systems
QuO's runtime support for quality of service in distributed objects
Middleware '98 Proceedings of the IFIP International Conference on Distributed Systems Platforms and Open Distributed Processing
Monitoring and Characterization of Component-Based Systems with Global Causality Capture
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Adaptive Cyberdefense for Survival and Intrusion Tolerance
IEEE Internet Computing
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Computer Networks: The International Journal of Computer and Telecommunications Networking
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OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part II
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Distributed applications that can adapt at runtime to changing quality of service (QoS) require a model of the expected QoS and of the possible application adaptations. QoS models in turn require runtime measurements, both in-band and out-of-band, from across the application's components. As the comprehensiveness of the model increases, so does the quality of adaptation. But eventually the increasing comprehensiveness becomes too complex for the QoS Designer to deal with effectively. In addition, performance models of any complexity are expensive to create and maintain at runtime. The QoS Designer therefore needs a set of distributed-QoS tools to assist in the construction of models, the handling of quality vs. complexity tradeoffs, and the efficient maintenance of models at runtime. This paper describes the Quality Objects (QuO) middleware support that provides for developing a performance model; collecting and organizing run-time measurements of a system, both in-band and out-of-band; and maintaining the model at runtime in an efficient way.