Web server workload characterization: the search for invariants
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Internet Web servers: workload characterization and performance implications
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Open, Closed, and Mixed Networks of Queues with Different Classes of Customers
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Mean-Value Analysis of Closed Multichain Queuing Networks
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A methodology for workload characterization of E-commerce sites
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In search of invariants for e-business workloads
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The Operational Analysis of Queueing Network Models
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Characterizing Web user sessions
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On the nonstationarity of Internet traffic
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Characterizing the scalability of a large web-based shopping system
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Flash crowds and denial of service attacks: characterization and implications for CDNs and web sites
Proceedings of the 11th international conference on World Wide Web
Capacity Planning for Internet Services
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HP-UX 11i Tuning and Performance (2nd Edition)
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Characterizing customer groups for an e-commerce website
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Workload characterization for trend analysis
ACM SIGMETRICS Performance Evaluation Review
Capacity planning for MVS computer systems
ACM SIGMETRICS Performance Evaluation Review
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
Resource overbooking and application profiling in shared hosting platforms
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An analytical model for multi-tier internet services and its applications
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Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
Modeling 3-Tiered Web Applications
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Stardust: tracking activity in a distributed storage system
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
A Synthetic Workload Generation Technique for Stress Testing Session-Based Systems
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Performance modeling and system management for multi-component online services
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Using magpie for request extraction and workload modelling
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Detecting performance anomalies in global applications
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Open versus closed: a cautionary tale
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Analytic modeling of multitier Internet applications
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Cataclysm: Scalable overload policing for internet applications
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A dollar from 15 cents: cross-platform management for internet services
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Automatic request categorization in internet services
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Modeling and exploiting query interactions in database systems
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Configuration-space performance anomaly depiction
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Reference-driven performance anomaly identification
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Towards workflow-driven database system workload modeling
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Query interactions in database workloads
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Automated anomaly detection and performance modeling of enterprise applications
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Self-adapting service level in Java enterprise edition
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A systematic and practical approach to generating policies from service level objectives
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CloudXplor: a tool for configuration planning in clouds based on empirical data
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Autonomic mix-aware provisioning for non-stationary data center workloads
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Monalytics: online monitoring and analytics for managing large scale data centers
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Self-adapting service level in Java enterprise edition
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Short-term performance management by priority-based queueing
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Dynamic Replication in Service-Oriented Systems
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Performance Evaluation
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Real production applications ranging from enterprise applications to large e-commerce sites share a crucial but seldom-noted characteristic: The relative frequencies of transaction types in their workloads are nonstationary, i.e., the transaction mix changes over time. Accurately predicting application-level performance in business-critical production applications is an increasingly important problem. However, transaction mix nonstationarity casts doubt on the practical usefulness of prediction methods that ignore this phenomenon. This paper demonstrates that transaction mix nonstationarity enables a new approach to predicting application-level performance as a function of transaction mix. We exploit nonstationarity to circumvent the need for invasive instrumentation and controlled benchmarking during model calibration; our approach relies solely on lightweight passive measurements that are routinely collected in today's production environments. We evaluate predictive accuracy on two real business-critical production applications. The accuracy of our response time predictions ranges from 10% to 16% on these applications, and our models generalize well to workloads very different from those used for calibration. We apply our technique to the challenging problem of predicting the impact of application consolidation on transaction response times. We calibrate models of two testbed applications running on dedicated machines, then use the models to predict their performance when they run together on a shared machine and serve very different workloads. Our predictions are accurate to within 4% to 14%. Existing approaches to consolidation decision support predict post-consolidation resource utilizations. Our method allows application-level performance to guide consolidation decisions.