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The Sixth International Multi-Conference on Computing in the Global Information Technology ICCGI 2011 June 19-24, 2011 - Luxembourg |
| Tutorials |
T1. Future Internet Trends: Networked Media, Content and Services Orientation
Prof. Dr. Eugen Borcoci, University Politehnica Bucharest, Romania
T2. Algorithm and Experiment Design with HeuristicLab: An Open Source Optimization Environment for Research and Education
Prof. Dr. Stefan Wagner, Upper Austria University of Applied Sciences, Austria
Dr. Gabriel Kronberger, Upper Austria University of Applied Sciences, Austria
DETAILS
T1. Future Internet Trends: Networked Media, Content and Services Orientation
Prof. Dr. Eugen Borcoci, University Politehnica Bucharest, Romania
Recent estimations of Future Internet trends, versus real market and users needs, forecast a strong FI orientation towards services, content and networked media. The service /content/media development of the FI (evolutionary or revolutionary) is expected to be offered based on more flexible business models, in which the classic client-server communication style is possible, but also peer-to-peer, multicast, broadcast and social networks. The FI will support multiple user roles as consumer, content producer, or even manager.
FI and telecommunication convergence is naturally desired; however this raises still many issues like architectural management and deployment ones. Virtualization and overlays on the top of the network infrastructure level, is considered an important tool to make the FI more flexible than today, while complex management tasks should be solved. For networked media, new paradigms are investigated, like content aware networking (CAN) and network aware applications (NAA). Content centric networking (CCN) is also a new approach, complementary and related to CAN approach, estimated to enable FI to better respond to multimedia transport and delivery needs.
This tutorial presents a perspective on some of the above topics, with focus on CAN/NAA, correlated with recent developments proposed by research communities, academia, industry, operators and standardization bodies. Sample examples of a CAN oriented architecture and solutions are given, extracted from an IP FP7 European research project : ALICANTE „MediA Ecosystem Deployment through Ubiquitous Content-Aware Network Environments”, 2010-2013.
T2. Algorithm and Experiment Design with HeuristicLab: An Open Source Optimization Environment for Research and Education
Prof. Dr. Stefan Wagner, Upper Austria University of Applied Sciences, Austria
Dr. Gabriel Kronberger, Upper Austria University of Applied Sciences, Austria
Tutorial Outline
The proposed tutorial demonstrates how to apply and analyze metaheuristic optimization algorithms using the HeuristicLab [1] open source optimization environment. It will be shown how to parameterize and execute evolutionary algorithms to solve combinatorial optimization problems (traveling salesman, vehicle routing) as well as data analysis problems (regression, classification). The attendees will learn how to assemble different algorithms and parameter settings to a large scale optimization experiment and how to execute such experiments on multi-core or cluster systems. Furthermore, the experiment results will be compared using HeuristicLab's interactive charts for visual and statistical analysis to gain knowledge from the executed test runs. To complete the tutorial, it will be sketched briefly how HeuristicLab can be extended with further optimization problems and how custom optimization algorithms can be modeled using the graphical algorithm designer.
Additional Information
HeuristicLab [1] is an open source system for heuristic optimization that features several metaheuristic optimization algorithms (e.g., genetic algorithms, genetic programming, evolution strategies, tabu search, simulated annealing) as well as several optimization problems (e.g., traveling salesman, regression, classification, vehicle routing, knapsack, simulation-based optimization). It is developed by the research group Heuristic and Evolutionary Algorithms Laboratory (HEAL) [2] of the Upper Austria University of Applied Sciences and is based on C# and the Microsoft .NET Framework. HeuristicLab is used as development platform for several research and industry projects (for example the Josef Ressel Centre Heureka! [3]) as well as for teaching metaheuristics in the study programs Software Engineering and Medical- and Bioinformatics. Over the years HeuristicLab has become more and more known within the metaheuristic optimization community and is used by researchers and lecturers at different universities. The application of HeuristicLab in multiple theoretical and practical projects has been documented in several publications (see for example [4] for a comprehensive list).
