Anna Kamińska-Chuchmała

Distributed Web Systems Performance Forecasting Using Turning Bands Method

With the increasing development of distributed computer systems (DCSs) in networked industrial and manufacturing applications on the World Wide Web (WWW) platform, including service-oriented architecture and Web of Things QoS-aware systems, it has become important to predict the Web performance. In this paper, we present Web performance prediction in time and in space by making a forecast of a Web resource downloading using the Turning Bands (TB) geostatistical simulation method. Real-life data for the research were obtained in an active experiment conducted by our multi-agent measurement system MWING performing monitoring of a group of Web servers worldwide from agents localized in different geographical localizations in Poland. The results show good quality of Web performance prediction made by means of the TB method, especially in the case when European Web servers were monitored by an MWING agent localized in Gliwice, Poland.

CLIENT-PERCEIVED WEB PERFORMANCE KNOWLEDGE DISCOVERY THROUGH TURNING BANDS METHOD

Client-perceived web performance prediction is a challenging for the knowledge engineering community. This article presents the application of the turning bands (TB) method to discover knowledge about the client-perceived spatiotemporal web performance. The TB method is used to study spatial or spatiotemporal phenomena. We conducted an experimental study to apply this technique. The MWING system was used to collect performance measurement for four clients, each monitoring the same group of web servers during several weeks of observations. In this article we present TB-based web performance prediction for an agent localized in Wrocław, Poland, and monitoring European web servers. The results of the spatial prediction of the total download web page times within a forecast of a two-week period showed that the TB simulation is efficient.

Experiments of Trust Prediction in Social Networks by Artificial Neural Networks

Social network online services are growing at an exponential pace, both in quantity of users and diversity of services; thus, the evaluation of trust in the interaction among users and toward the system is a central issue from the user point of view. Trust can be grounded in past direct experience or in the indirect information provided by trusted third-party users shaping the trustee reputation. When there is no previous history of interactions, the truster must resort to some form of prediction in order to establish Trust or Distrust on a potential trustee. In this study, we deal with the prediction of trust relationships on the basis of reputation information. Trust can be positive or negative (Distrust), hence, we have a two-class problem. Feature vectors for the classification have binary-valued components. Artificial neural network and statistical classifiers provide state-of-the-art results with these features on a benchmarking trust database. In this article, we propose the application of a sample generation method for the minority class in order to reduce some of the effect of class imbalance among Trust and Distrust classes. Specifically, the approach shows high resiliency to system growth.

Spatio-temporal Web Performance Forecasting with Sequential Gaussian Simulation Method

In this article, we describe a novel proposal for the spatio-temporal Web performance forecasting using one of the geostatistical methods – Sequential Gaussian Simulation (SGS). Necessary data were obtained from Multiagent Internet Measurement System – MWING, which monitored web transactions issued by MWING’s agent located in Las Vegas, US and targeting web servers in Europe. Data contains the measurements, which were taken every day at the same time: at 06:00 a.m., 12:00 a.m. and 6:00 p.m. during the period of May, 2009. First, the preliminary analysis of measurement data was conducted. Next, the structural analysis, which includes directional variogram approximated with the theoretical model, was performed. Subsequently, the spatial forecast (from one week in advance) of total time of downloading data from Web servers was calculated. The analysis of server activity on a particular weekday for a period of a few weeks in selected time intervals and considered forecasted errors was performed. Finally, the results of forecast were analyzed in detail, followed by the determination of subsequent research directions to improve Web performance forecasts.

Web Performance Forecasting with Kriging Method

Due to the substantial growth of communication network in last years, the access to the Internet network is crucial for the society. Therefore, there is a necessity of research on Web systems forecasting. This work presents a proposal of the application of the geostatistical estimation - the Kriging method, which give spatio-temporal information about forecast of network throughput. The database was created on the basis of Multiagent Internet Measurement System MWING. In the research the connections between an agent in Gdansk and European serverswere considered. The preliminary structural analysis of the data, which are necessary to use the Kriging method was conducted. Next a spatial forecast of the total time of downloading data from Web servers with a four days time advance was calculated. The results were analyzed and comparedwith other simulationmethods results from the same database.

Short-Term spatio-temporal forecasts of web performance by means of turning bands method

This work presents Turning Bands simulation method (TB) as a geostatistical approach for making spatio-temporal forecasts of Web performance. The most significant advantage of this method is requirement for the minimum amount of input data to make accurate and detailed forecasts. For this paper, necessary data were obtained with the Multiagent Internet Measuring System (MWING); however, only those measurements of European servers that were collected by the MWINGs agent in Gdansk were used. The aforementioned agent performed measurements (i.e. download times of the same given resource from the evaluated servers) three times every day, between 07.02.2009 and 28.02.2009, at 06:00 am, 12:00 pm and 06.00 pm. First, the preliminary and structural analyses of the measurement data were performed. Then short-term spatio-temporal forecasts of total downloading times for a four days ahead were made. And finally, thorough analysis of the obtained results was carried out and further research directions were proposed.

Knowledge discovery about web performance with geostatistical turning bands method

The work presents a proposal of the application of the geostatistical simulation - the Turning Bands method, to acquire knowledge about network throughput. The data base was created on the basis of Multiagent Internet Measurement System MWING. In the research the connections between an agent in Wroclaw and European servers were considered. The preliminary structural analysis of the data which are necessary to use the Turning Bands method was conducted. Next a spatial forecast of the total time of downloading data from Web servers with a two-week time advance was calculated. The analysis of server activity on a particular week day was conducted for a period of a few weeks in selected time intervals.

3D Web Performance Forecasting Using Turning Bands Method

An attempt was made to evaluate Web performance by making a 3D forecast of Web site performance while resource downloading using a geostatistic simulation method Turning Bands for the first time. Data for the research were obtained in an active experiment conducted by a multi-agent measurement system MWING performing monitoring of a common group of Web sites from different agent locations. In this study we use a measurement database collected by an agent from Wroclaw. A preliminary analysis of measurement data was conducted. Next a structural analysis of data was conducted and a spatial forecast of the total time of downloading data from web servers with a one-week time advance was calculated. Forecast results were analyzed in detail and then directions for further research were suggested.The article introduces the first innovative use of the Turning Bands method in the prediction of Internet performance. First results permit to find that the mentioned predictive method can be effective.

An experiment of subconscious intelligent social computing on household appliances

Subconscious Social Intelligence refers to the design of social services oriented towards user problem solving, providing an underlying innovation layer is able to generate new solutions to yet unknown problems. The innovation layer is achieved by Computational Intelligence techniques, encompassing machine learning to build models of user satisfaction over solution quality, and stochastic search as the means for innovation generation. The SandS project provides an instance of such paradigm, where household appliances are the subject of the social service. This paper proposes a specific architecture, reporting results on a synthetic database build according to SandS project current designs. Database synthesis for system tuning and validation is a critical issue, hence the paper details the considerations guiding its design and generation, as well as the validation procedure ensuring the ecological validity of the innovation process simulation. The architecture is composed of a Support Vector Regression (SVR) module for user satisfaction modeling, and an Evolution Strategy (ES) achieving recipe innovation. The paper reports some computational experiments that may guide the real life implementation. The reported results are methodologically sound as far as they are independent of the generation process.

Distributed Web Server’s Data Performance Processing with Application of Spatial Econometrics Models

Predicting the download time of Web distributed resources is an important but challenging problem in the worldwide public Internet. In our recent work we focus on spatial-based methods. We propose to use spatial econometrics methods to predict Web server’s performance. Three spatial regression models have been studied: Classical Regression Model (CRM), Spatial Lag Model (SLM) and Spatial Error Model (SEM). We used the real-life dataset obtained in active experiments performed by our Virtual Multiagent Internet Measurement System (VMWING), which monitored web transactions issued by VMWING’s agent located in Wroclaw, Poland and targeting Web servers in Europe. We also compared studied econometrics methods with geostatistical methods which were analyzed in our previous papers.