Bandwidth Learning in Distributed Networking Environments for Global Information Dissemination

- This work investigates bandwidth learning algorithms in a version of a distributed heterogeneous data dissemination system called the Agile Information Control Environment (AICE). In this environment, the probability of setup or rejection of communication requests has to be derived without any updated knowledge of the actual topologies of the underlying networks. Recently, a learning algorithm for AICE was introduced by the authors in [6]. In this paper, an enhanced learning algorithm is introduced that is able to predict the setup/rejection of a communication request with an accuracy greater than 70% with less available feedback information than in [6]. The learning algorithm uses elements of exponential adjustment coupled with a binary search. This enables the learner to quickly learn about changes in the network topology and traffic load. It is shown that this learner outperforms another learning algorithm (NWS) recently proposed in the literature. An increase of accuracy of up to...

• Type: Conference
• Year: 2001
• Where: ISCAPDCS
• Authors: Craig Sullivan, Michael Jurczyk
• Keywords: Bandwidth Learning Algorithms, ISCAPDCS 2001, ISCAPDCS 2007, Learner, Learning Algorithm