Capacity planning and performance modeling: from mainframes to client-server systems
Capacity planning and performance modeling: from mainframes to client-server systems
Web server workload characterization: the search for invariants
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Correlating resource demand information with ARM data for application services
Proceedings of the 1st international workshop on Software and performance
Characterizing the scalability of a large web-based shopping system
ACM Transactions on Internet Technology (TOIT)
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Characterizing Reference Locality in the WWW
Characterizing Reference Locality in the WWW
An analytical model for multi-tier internet services and its applications
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A capacity management service for resource pools
Proceedings of the 5th international workshop on Software and performance
Dynamic Provisioning of Multi-tier Internet Applications
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Sizing the streaming media cluster solution for a given workload
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Detecting performance anomalies in global applications
WORLDS'05 Proceedings of the 2nd conference on Real, Large Distributed Systems - Volume 2
A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Modellus: Automated modeling of complex internet data center applications
ACM Transactions on the Web (TWEB)
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As the complexity of IT systems increases, performance management and capacity planning become the largest and most difficult expenses to control. New methodologies and modeling techniques that explain large-system behavior and help predict their future performance are now needed to effectively tackle the emerging performance issues. With the multi-tier architecture paradigm becoming an industry standard for developing scalable client-server applications, it is important to design effective and accurate performance prediction models of multi-tier applications under an enterprise production environment and a real workload mix. To accurately answer performance questions for an existing production system with a real workload mix, we design and implement a new capacity planning and anomaly detection tool, called R-Capriccio, that is based on the following three components: i) a Workload Profiler that exploits locality in existing enterprise web workloads and extracts a small set of most popular, core client transactions responsible for the majority of client requests in the system; ii) a Regression-based Solver that is used for deriving the CPU demand of each core transaction on a given hardware; and iii) an Analytical Model that is based on a network of queues that models a multi-tier system. To validate R-Capriccio, we conduct a detailed case study using the access logs from two heterogeneous production servers that represent customized client accesses to a popular and actively used HP Open View Service Desk application.