Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms
Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms
A model for web services discovery with QoS
ACM SIGecom Exchanges
The Design of QoS Broker Algorithms for QoS-Capable Web Services
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
Self-Tuning Planned Actions Time to Make Real-Time SOAP Real
ISORC '05 Proceedings of the Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing
Managing QoS through prioritization in web services
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
A concept for QoS integration in web services
WISEW'03 Proceedings of the Fourth international conference on Web information systems engineering workshops
SOAP request scheduling for differentiated quality of service
WISE'05 Proceedings of the 2005 international conference on Web Information Systems Engineering
The design of the TAO real-time object request broker
Computer Communications
Building web services middleware with predictable service execution
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Real-time service process admission control with schedule reorganization
Service Oriented Computing and Applications
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This paper proposes a model and an admission control algorithm for achieving predictability in web services by means of service differentiation. We use real-time scheduling principles typically used offline, adapt them to web services to work online. The proposed model and algorithm is empirically evaluated by implementing it Apache Axis2. The implementation is benchmarked against the unmodified version of Axis2 for various types of workloads and arrival rates, given different deadlines. We meet 100% of the deadlines keeping a healthy request acceptance rate of 42-100% depending on the task size variation. Our solution outperforms Axis2, specially at instances with high task size variance, by a factor of 10 - 1000.