31,731 research outputs found
Spectrum Allocation Policy in Elastic Optical Networks
The considered problem covers routing and spectrum allocation problem (RSA problem) in Elastic Optical Networks while maintaining the spectrum continuity constraints, non-overlapping spectra constraints for adjacent connections on individual links of the network and spectrum contiguity constraints of the connection. In this article the modified version of the First Fit spectrum slot allocation policy for Fixed Alternate Routing in flexible optical networks has been proposed. The Fixed Alternate Routing with proposed spectrum allocation policy rejects fewer requests, provides less bandwidth blocking probability and less spectrum fragmentation than Fixed Alternate Routing with well-known First Fit and Exact Fit spectrum allocation policies. However, the cost of improving these parameters is a higher computational complexity of the proposed allocation policy
Stochastic Online Shortest Path Routing: The Value of Feedback
This paper studies online shortest path routing over multi-hop networks. Link
costs or delays are time-varying and modeled by independent and identically
distributed random processes, whose parameters are initially unknown. The
parameters, and hence the optimal path, can only be estimated by routing
packets through the network and observing the realized delays. Our aim is to
find a routing policy that minimizes the regret (the cumulative difference of
expected delay) between the path chosen by the policy and the unknown optimal
path. We formulate the problem as a combinatorial bandit optimization problem
and consider several scenarios that differ in where routing decisions are made
and in the information available when making the decisions. For each scenario,
we derive a tight asymptotic lower bound on the regret that has to be satisfied
by any online routing policy. These bounds help us to understand the
performance improvements we can expect when (i) taking routing decisions at
each hop rather than at the source only, and (ii) observing per-link delays
rather than end-to-end path delays. In particular, we show that (i) is of no
use while (ii) can have a spectacular impact. Three algorithms, with a
trade-off between computational complexity and performance, are proposed. The
regret upper bounds of these algorithms improve over those of the existing
algorithms, and they significantly outperform state-of-the-art algorithms in
numerical experiments.Comment: 18 page
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
We propose and systematically evaluate three strategies for training
dynamically-routed artificial neural networks: graphs of learned
transformations through which different input signals may take different paths.
Though some approaches have advantages over others, the resulting networks are
often qualitatively similar. We find that, in dynamically-routed networks
trained to classify images, layers and branches become specialized to process
distinct categories of images. Additionally, given a fixed computational
budget, dynamically-routed networks tend to perform better than comparable
statically-routed networks.Comment: ICML 2017. Code at https://github.com/MasonMcGill/multipath-nn Video
abstract at https://youtu.be/NHQsDaycwy
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