Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
Using Simulation to Facilitate Effective Workflow Adaptation
SS '02 Proceedings of the 35th Annual Simulation Symposium
Software agents using simulation for decision-making
ACM SIGSOFT Software Engineering Notes
Performance engineering of service compositions
Proceedings of the 2006 international workshop on Service-oriented software engineering
Performance modeling for service oriented architectures
Companion of the 30th international conference on Software engineering
Business transformation to SOA: aspects of the migration and performance and QoS issues
Proceedings of the 2nd international workshop on Systems development in SOA environments
Capacity planning for service-oriented architectures
CASCON '08 Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds
Toward a simulation-generated knowledge base of service performance
Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing
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As service-oriented systems grow larger and more complex, so does the challenge of configuring the underlying hardware infrastructure on which their consitituent services are deployed. With more configuration options (virtualized systems, cloud-based systems, etc.), the challenge grows more difficult. Configuring service-oriented systems involves balancing a competing set of priorities and choosing trade-offs to achieve a satisfactory state. To address this problem, we present a simulation-based methodology for supporting administrators in making these decisions by providing them with relevant information obtained using inexpensive simulation-generated data. Our services-aware simulation framework enables the generation of lengthy simulation traces of the system's behavior, characterized by a variety of performance metrics, under different configuration and load conditions. One can design a variety of experiments, tailored to answer specific system-configuration questions, such as, "what is the optimal distribution of services across multiple servers" for example. We relate a general methodology for assisting administrators in balancing trade-offs using our framework and we present results establishing benchmarks for the cost and performance improvements we can expect from run-time configuration adaptation for this application.