Maciej Filocha

Seamless access to the PL-Grid e-infrastructure using UNICORE middleware

This chapter provides a brief overview of the UNICORE grid middleware and its utilization in the large Distributed Computing Infrastructure. UNICORE framework, in its recent version implements key grid standards and specifications. The system architecture and capabilities, such as security, workflow and data management are described. The installation of the UNICORE environment in the PL-Grid is presented. Special attention is given to the integration of the UNICORE middleware with the PL-Grid authentication and authorization framework which allows for uniform infrastructure and user management across different middlewares. The solutions for monitoring and accounting of the UNICORE infrastructure is presented.

Polish computational research space for international scientific collaborations

The Polish Grid Initiative commenced in 2009 as part of the PL-Grid project funded under the framework of the Innovative Economy Operational Programme [1]. The main purpose of this Project is to provide the Polish scientific community with an IT basic platform making use of Grid computer clusters, enabling e-science research in various fields. The Project is establishing a country-wide computing platform, which supports scientific research through integration of experimental data and results of advanced computer simulations carried out by geographically-dispersed teams. The solutions applied in setting up this e-infrastructure will ensure integration with other, similar platforms around the world. In the paper some basic facts concerning the Project history are given, PL-Grid goals are described and several examples of innovative grid services and software as well as support procedures developed to-date are presented.

A hybrid system for forecasting 24-h power load profile for Polish electric grid

Abstract We present a method of forecasting 24-h power load profile in state-wide power system in Poland. The presented method is based on a hybrid artificial intelligence system. It employs actual temperature forecasts prepared by Interdisciplinary Centre for Mathematical and Computational Modelling of Warsaw University. The machine learning part of the system consists of 24 instances of Hierarchical Estimator: a machine learning method that divides the problem into non-exclusive subproblems with the help of fuzzy clustering and combines results of fairly simple neural networks trained on those subproblems into one, possibly more accurate solution. The presented system also includes a part responsible for dealing with days that have distinct power load patterns, such as additional state holidays. That latter part uses 30 (or 33) appropriately arranged linear regressions. The proposed approach was tested on historical load data from Poland and a few other countries. The achieved MAPE varied from 1.08% to 2.26% in dependence on the country. Such errors are among the lowest achieved by the published methods.

Training in the PL-Grid as key component to attract users to grid e-infrastructures

This chapter provides an overview of the training facilities in the PL-Grid project. It is well known that procedures necessary to obtain certificates and then install software or get access to the User Interfaces are complicated and due to security reasons cannot be simplified. For these purposes we have crated extended training facilities which allow to get easy access to the Grid. We have installed dedicated infrastructure including the computational resources, certificate authority, user portal and an on-line training system. The efficiency of the training infrastructure, materials and procedures has been proved in the numerous training sessions.