Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning: An Essential Tool for Managing Web Services
IT Professional
The behavior of Ethernet-like computer communications networks
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions
Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions
Stability of Feature Selection Algorithms
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Customized Benchmark Generation Using MDA
WICSA '05 Proceedings of the 5th Working IEEE/IFIP Conference on Software Architecture
Model driven benchmark generation for web services
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Performance modeling for service oriented architectures
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Proceedings of the 2nd international workshop on Systems development in SOA environments
Memory performance prediction of web server applications based on grey system theory
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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The transformation and integration of government services, enabled by the use of new technologies such as application servers and Web services, is fundamental to reduce the cost of government and improving service outcomes to citizens. Many core Government information systems comprise applications running on legacy mainframes, databases and transaction processing monitors. As Governments worldwide provide direct access over the Internet to these legacy applications from the general public, they may be exposed to workloads well above the origin design parameters of these back-end systems. This creates a significant risk of high profile failures for Government agencies whose newly integrated systems become overloaded. In this paper we describe how we conducted a performance assessment of a business-critical, Internet-facing Web services that integrated new and legacy systems from two Australian Government agencies. We leveraged prototype tools from our own research along with known techniques in performance modeling. We were able to clearly demonstrate that the existing hardware and software would be adequate to handle the predicted workload for the next financial year. We were also able to do 'what-if' analysis and predict how the system can perform with alternative strategies to scale the system. We conclude by summarizing the lessons learnt, including the importance of architecture visibility, benchmarking data quality, and measurement feasibility due to issues of outsourcing, privacy legislation and cross-agency involvement.