Quantitative system performance: computer system analysis using queueing network models
Quantitative system performance: computer system analysis using queueing network models
Robustness of queuing network formulas
Journal of the ACM (JACM)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Analyzing stability in wide-area network performance
SIGMETRICS '97 Proceedings of the 1997 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Internet Web servers: workload characterization and performance implications
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Capacity planning for Web performance: metrics, models, and methods
Capacity planning for Web performance: metrics, models, and methods
In search of invariants for e-business workloads
Proceedings of the 2nd ACM conference on Electronic commerce
The Operational Analysis of Queueing Network Models
ACM Computing Surveys (CSUR)
Characterizing the scalability of a large web-based shopping system
ACM Transactions on Internet Technology (TOIT)
On the constancy of internet path properties
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Summary of WWW characterizations
World Wide Web
Server Capacity Planning for Web Traffic Workload
IEEE Transactions on Knowledge and Data Engineering
A hierarchical and multiscale approach to analyze E-business workloads
Performance Evaluation
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Injecting realistic burstiness to a traditional client-server benchmark
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
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Web and e-commerce workloads are known to vary significantly from hour to hour, day to day, and week to week. The causes of these fluctuations are changes in the number of users visiting a site and the mix of services they require. Since the workloads are known to vary over time, one should not simply choose an arbitrary time interval and consider it as a reference for performance evaluation. We conclude that times scales are of great importance for operational analysis, particularly for systems with bursty loads. Service level agreements must certainly take into account measurement time scales. Similarly input parameters for predictive models are sensitive to time scale. Ultimately, a time scale should be chosen for service level requirements that best expresses the needs of end-users and the price the owner of a site is willing to pay for QoS.