Web workload analysis and session characterization using clustering

Web servers have a significant presence in today\u27s Internet. Corporations want to achieve high availability, scalability, and consistent performance for respective Web systems, maintaining high customer service standards. Web Workload characterization and the analysis of Web log files are the basis on which Web server modeling for efficiency, scalability and availability can be planned. This thesis analyzes the Web access logs of six public Web sites: Department of Computer Science and Electrical Engineering at West Virginia University, West Virginia University, three NASA IVV servers, and Clarknet server. In addition, three private NASA IVV servers are also analyzed.;We characterize sessions using several attributes such as number of request per session, session length in time units, number of bytes transferred per session, and number of erroneous requests per session. We use clustering, as unsupervised learning methods, to classify Web server sessions. Unlike most other studies which were focused on building user profiles based on their navigational patterns, we use session attributes as basis for clustering. We also study the effectiveness of the Principal Component Analysis on session classification based on clustering

Simulating complex systems with a low-detail model

In this paper we show how modeling and simulating a complex system such as a web-server can help to evaluate di erent metrics and proposals to improve the performance without necessity of using a real system. Many times the system is unavailable or requires spending time and resources to generate results. With simulation and concretely with a coarse-grain simulation as we propose can solve with great success this problem. In this article we have been able to simulate the metric and behaviour of a web server with SSL security, the ellapsed time required by the simulation on a desktop machine is only 1/10 of real time. We have also been able to measure, for example,the performance enhancements with 8 CPUs without having an available machine of similar features.Postprint (published version

CPHmodels-3.0--remote homology modeling using structure-guided sequence profiles

CPHmodels-3.0 is a web server predicting protein 3D structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.0 profile-profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models for 94% of the targets (117 out of 128), 74% were predicted as high reliability models (87 out of 117). These achieved an average RMSD of 4.6 A when superimposed to the 3D structure. The remaining 26% low reliably models (30 out of 117) could superimpose to the true 3D structure with an average RMSD of 9.3 A. These performance values place the CPHmodels-3.0 method in the group of high performing 3D prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server i

Computing Web-scale Topic Models using an Asynchronous Parameter Server

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135 times more data and 10 times more topics without sacrificing model quality.Comment: To appear in SIGIR 201

SVRMHC prediction server for MHC-binding peptides

BACKGROUND: The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. RESULTS: Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. CONCLUSION: SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers