Bandwidth tracking in distributed heterogeneous networking environments

This work investigates bandwidth tracking 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. In this paper, a network learning algorithm is therefore introduced that is able to predict the setup/rejection of a communication request with a high accuracy of around 80% by tracking the average spare bandwidth of end-to-end communication channels. The learning algorithm uses elements of exponential growth 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 the learner is able to predict request setup/rejection with a high accuracy without any information about underlying network topologies. To maximize the learner performance, an initial full mesh aggregation of the underlying network topology at system startup should be used