Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Web server performance measurement and modeling techniques
Performance Evaluation - Special issue on tools for performance evaluation
Web traffic modeling and Web server performance analysis
ACM SIGMETRICS Performance Evaluation Review
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
Server Capacity Planning for Web Traffic Workload
IEEE Transactions on Knowledge and Data Engineering
The N-Burst/G/1 Model with Heavy-Tailed Service-Times Distribution
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
The distribution of file transmission duration in the web: Research Articles
International Journal of Communication Systems
Web traffic modeling at finer time scales and performance implications
Performance Evaluation - Long range dependence and heavy tail distributions
A histogram-based stochastic process for finite buffer occupancy analysis
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
Network Performance Analysis based on Histogram Workload Models
MASCOTS '07 Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Towards characterizing cloud backend workloads: insights from Google compute clusters
ACM SIGMETRICS Performance Evaluation Review
A weighted-fair-queuing (WFQ)-based dynamic request scheduling approach in a multi-core system
Future Generation Computer Systems
Utilization analysis of servers in a data centre
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
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Web servers are required to perform millions of transaction requests per day at an acceptable Quality of Service (QoS) level in terms of client response time and server throughput. Consequently, a thorough understanding of the performance capabilities and limitations of web servers is critical. Finding a simple web traffic model described by a reasonable number of parameters that enables powerful analysis methods and provides accurate results has been a challenging problem during the last few decades. This paper proposes a discrete statistical description of web traffic that is based on histograms. In order to reflect the second-order statistics (long-range dependence and self-similarity) of the workload, this basic model has been extended using the Hurst parameter. Then, a system performance model-based on histogram operators (histogram calculus) is introduced. The proposed model has been evaluated using real workload traces using a single-site server model. These evaluations show that the model is accurate and improves the results of classic queueing models. The model provides an excellent basis for a decision support tool to predict the behavior of web servers.