Ágnes Bogárdi-Mészöly

Investigating factors influencing the response time in ASP.NET web applications

Distributed systems and network applications play an important role in computer science nowadays. The most common consideration is performance, because these systems have to provide cost-effective and high availability services in the long term, thus they have to be scaled to meet the expected load. The performance of a web application is affected by several factors. The goal of our work is to analyze how some of them affect the response time. The paper presents the result of performance measurements of an ASP.NET web application. We measured the average response time of a test web application while changing the parameters of the application server. The results are analyzed using statistical methods: (i) independence tests to investigate which factors influence principally the performance, (ii) in addition certain plots and hypothesis tests to determine the distribution of the response time.

Performance Factors in ASP.NET Web Applications with Limited Queue Models

Distributed systems and Web applications play an important role in computer science nowadays. The most common consideration is performance, because these systems must provide services with low response time, high availability, and certain throughput level. The performance of a Web application is affected by several factors. The goal of our work is to analyze how some of them affect the response time. In this paper, the effects of two configurable settings of the ASP.NET application server are discussed: the limit of the global queue and the limit of the application queue. The response time of a test Web application are measured, while changing these settings. The results are analyzed in a qualitative manner which is followed by using statistical methods: independence tests to investigate which factors influence principally the performance. Our experiments have shown that the global queue limit and the application queue limit are performance factors. Finally, optimal settings according to the performance-related requirements are determined as a function of client workload and the settings of the thread pool attributes

A novel algorithm for performance prediction of web-based software systems

Performance metrics can be predicted with appropriate performance models and evaluation algorithms. The goal of our work is to adapt the Mean-Value Analysis evaluation algorithm to model the behavior of the thread pool. The computation time and the computational complexity of the proposed algorithm have been provided. The limit of the response time and the throughput sequences computed by the novel algorithm has been determined. It has been shown that the proposed algorithm can be applied to performance prediction of web-based software systems in ASP.NET environment. The proposed algorithm has been validated and the correctness of performance prediction with the novel algorithm has been verified with performance measurements. Error analysis has been performed to verify the correctness of performance prediction.

Extending the Mean-Value Analysis Algorithm According to the Thread Pool Investigation

Web-based information systems play an important role in computer science. The most common consideration is performance. With the help of a proper performance model and evaluation algorithm, the performance metrics can be determined at the early stages of the development process. In our work, the response time and the throughput of multi-tier Web applications have been predicted based on a queueing model. The goal of our work is to extend the mean-value analysis (MVA) algorithm according to the investigation of the thread pool. The MVA and the proposed extended MVA evaluation algorithms have been implemented with the help of MATLAB. The input parameters have been estimated based on one measurement, and the model has been evaluated to predict performance metrics. Moreover, a Web application has been tested with concurrent user sessions in order to validate the proposed algorithm in ASP.NET environment.

Improved performance models of web-based software systems

Web-based software systems access some resources while executing the requests of the clients, typically several requests arrive at the same time, thus, competitive situation is established for the resources. In case of modeling such situation queueing model-based approaches are widely recognized. In our work, novel models and algorithms have been proposed to model the queue limit performance factor. In addition, the computational time and complexity of the proposed algorithms have been provided. Moreover, it has been shown that the proposed models and algorithms can be applied to performance prediction of webbased software systems in ASP.NET environment. The goal of our work is to validate the proposed models and algorithms and to verify the correctness of performance prediction with performance measurements in ASP.NET environment. The results have shown that the proposed models and algorithms predict the performance metrics much more accurate than the original model and algorithm.

Thread Pool-Based Improvement of the Mean-Value Analysis Algorithm

The performance of information systems is an important consideration. With the help of proper performance models and evaluation algorithms, performance metrics can be predicted accurately. The goal of our work is to improve the Mean-Value Analysis (MVA) evaluation algorithm based on the investigation of thread pools. In our work, the performance metrics of multi-tier information systems are predicted with the help of a queueing model and the improved MVA. The proposed evaluation algorithm has been implemented, the inputs have been estimated based on one measurement, and the model has been evaluated to predict performance metrics. In addition, ASP.NET web applications have been tested with concurrent clients in order to validate the proposed algorithm in different versions of ASP.NET environments.

Detect Scenic Leaves and Blossoms Viewing Places from Flickr Based on Social and Image Features

Photo sharing websites have become extremely popular with GPS-enabled digital cameras and camera phones. Seeing autumn leaves, cherry blossoms, etc. is a traditional seasonal activity. Tremendous photos about leaves and blossoms have been uploaded. The goal of our paper is to detect and rank the most scenic leaves and blossoms viewing places based on social and image features. Methods for social interestingness, color percentage, edge rate, sharpness, ranking score have been provided. The proposed methods have been validated and verified by experimental results for maple leaves in Kyoto.

Performance Prediction of Web-Based Software Systems

This paper addresses the issues to establish performance models and evaluation methodologies applying the identified queue limit performance factors. The computational complexity of the novel algorithms is provided. It is demonstrated that the proposed models and algorithms can be used for performance prediction of web-based software systems in ASP.NET environment. The validity of the novel models and algorithms as well as the correctness of the performance prediction is proven with performance measurements. It is shown that the error of the suggested models and algorithms is less than the error of the original model and algorithm. These methods facilitate more efficient performance prediction of web-based software systems.

Improved Evaluation Algorithm for Performance Prediction with Error Analysis

With the help of a proper performance model and evaluation algorithm, the performance metrics of information systems can be determined at the early stages of the development process. In our work, the response time and the throughput performance metrics of multi-tier Web applications have been predicted based on a queueing model. The goal of our work is to extend the mean-value analysis (MVA) algorithm according to the investigation of the thread pool. Web applications have been tested with concurrent user sessions in order to validate the proposed algorithm in different versions of ASP.NET environment. Moreover, error analysis has been performed to demonstrate the accuracy of the proposed algorithm.

EBSCAN: An Entanglement-based Algorithm for Discovering Dense Regions in Large Geo-social Data Streams with Noise

The remarkable growth of social networking services on global positioning system (GPS)-enabled handheld devices has produced enormous amounts of georeferenced big data. Given a large spatial dataset, the challenge is to effectively discover dense regions from the dataset. Dense regions might be the most attractive area in a city or the most dangerous zone of a town. A solution to this problem can be useful in many applications, including marketing, tourism, and social research. Density-based clustering methods, such as DBSCAN, are often used for this purpose. Nevertheless, current spatial clustering methods emphasize density while neglecting human behavior derived from geographical features. In this paper, we propose EBSCAN, which is based on the novel idea of an entanglement-based approach. Our method considers not only spatial information but also human behavior derived from geographical features. Another problem is that competing methods such as DBSCAN have two input parameters. Thus, it is difficult to determine optimal values. EBSCAN requires only a single intuitive parameter, tooFar, to discover dense regions. Finally, we evaluate the effectiveness of the proposed method using both toy examples and real datasets. Our experimentally obtained results reveal the properties of EBSCAN and show that it is >10 times faster than the competitor.