Joint scheduling and admission control for ATS-based switching nodes
SIGCOMM '92 Conference proceedings on Communications architectures & protocols
Mean-Value Analysis of Closed Multichain Queuing Networks
Journal of the ACM (JACM)
Preserving QoS of e-commerce sites through self-tuning: a performance model approach
Proceedings of the 3rd ACM conference on Electronic Commerce
High Performance Cluster Computing: Architectures and Systems
High Performance Cluster Computing: Architectures and Systems
Integrating Service Level Agreements: Optimizing Your OSS for SLA Delivery
Integrating Service Level Agreements: Optimizing Your OSS for SLA Delivery
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
Adaptive Load Control in Transaction Processing Systems
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
A method for transparent admission control and request scheduling in e-commerce web sites
Proceedings of the 13th international conference on World Wide Web
Adaptive middleware for data replication
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
Controlling Quality of Service in Multi-Tier Web Applications
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Provisioning servers in the application tier for e-commerce systems
ACM Transactions on Internet Technology (TOIT)
Analytic modeling of multitier Internet applications
ACM Transactions on the Web (TWEB)
Performance comparison of middleware architectures for generating dynamic web content
Proceedings of the ACM/IFIP/USENIX 2003 International Conference on Middleware
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Finding an efficient configuration for cluster-based multi-tier Internet services is often a difficult task. Moreover, even a good configuration could become obsolete, depending on workload evolution. In this paper, we address both problems by dynamically calculating an optimal configuration for multi-tier Internet services and applying this configuration to the managed application. Our approach is based on two main components. A model of the underlying application, and a controller using this model to find the optimal configuration according current environment and performance objectives. We evaluate the model accuracy and the controller efficiency. Experiments show that our solution improves resource consumption, and may lead to significant energy savings, besides matching the performance objectives even with a dynamic workload.