A multi-agent system for managing the quality of service in telecommunications networks

2005 ◽  
Vol 14 (2) ◽  
pp. 129-158 ◽  
Author(s):  
Pasquale De Meo ◽  
Antonio Iera ◽  
Giorgio Terracina ◽  
Domenico Ursino
2015 ◽  
Vol 13 (4) ◽  
pp. 1048-1054 ◽  
Author(s):  
Joel Sanchez Dominguez ◽  
Adailton J. Cerqueira Junior ◽  
Dany Sanchez Dominguez ◽  
Diego Frias ◽  
Susana Marrero Iglesias

2020 ◽  
Vol 17 (5) ◽  
pp. 2035-2038
Author(s):  
E. Ajith Jubilson ◽  
Ravi Sankar Sangam

Metrics are the essential building blocks for any evaluation process. They establish specific goals for improvement. Multi agent system (MAS) is complex in nature, due to the increase in complexity of developing a multi agent system, the existing metrics are less sufficient for evaluating the quality of an MAS. This is due to the fact that agent react in an unpredictable manner. Existing metrics for measuring MAS quality fails to addresses potential communication, initiative behaviour and learn-ability. In this work we have proposed additional metrics for measuring the software agent. A software agent for online shopping system is developed and the metrics values are obtained from it and the quality of the multi agent system is analysed.


2002 ◽  
Vol 17 (4) ◽  
pp. 317-329 ◽  
Author(s):  
PAUL DAVIDSSON ◽  
FREDRIK WERNSTEDT

A multi-agent system architecture for coordination of just-in-time production and distribution is presented. The problem to solve is twofold: first the right amount of resources at the right time should be produced, then these resources should be distributed to the right consumers. In order to solve the first problem, which is hard when the production and/or distribution time is relatively long, each consumer is equipped with an agent that makes predictions of future needs that it sends to a production agent. The second part of the problem is approached by forming clusters of consumers within which it is possible to redistribute resources fast and at a low cost in order to cope with discrepancies between predicted and actual consumption. Redistribution agents are introduced (one for each cluster) to manage the redistribution of resources. The suggested architecture is evaluated in a case study concerning management of district heating systems. Results from a simulation study show that the suggested approach makes it possible to control the trade-off between quality of service and degree of surplus production. We also compare the suggested approach to a reference control scheme (approximately corresponding to the current approach to district heating management), and conclude that it is possible to reduce the amount of resources produced while maintaining the quality of service. Finally, we describe a simulation experiment where the relation between the size of the clusters and the quality of service was studied.


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