Emergent Mind

Abstract

A new architectural paradigm, named, optical-computing-enabled network, is proposed as a potential evolution of the currently used optical-bypass framework. The main idea is to leverage the optical computing capabilities performed on transitional lightpaths at intermediate nodes and such proposal reverses the conventional wisdom in optical-bypass network, that is, separating in-transit lightpaths in avoidance of unwanted interference. In optical-computing-enabled network, the optical nodes are therefore upgraded from conventional functions of add-drop and cross-connect to include optical computing / processing capabilities. This is enabled by exploiting the superposition of in-transit lightpaths for computing purposes to achieve greater capacity efficiency. While traditional network design and planning algorithms have been well-developed for optical-bypass framework in which the routing and resource allocation is dedicated to each optical channel (lightpath), more complicated problems arise in optical-computing-enabled architecture as a consequence of intricate interaction between optical channels and hence resulting into the establishment of the so-called integrated / computed lightpaths. This necessitates for a different framework of network design and planning to maximize the impact of optical computing opportunities. In highlighting this critical point, a detailed case study exploiting the optical aggregation operation to re-design the optical core network is investigated in this paper. Numerical results obtained from extensive simulations on the COST239 network are presented to quantify the efficacy of optical-computing-enabled approach versus the conventional optical-bypass-enabled one.

We're not able to analyze this paper right now due to high demand.

Please check back later (sorry!).

Generate a summary of this paper on our Pro plan:

We ran into a problem analyzing this paper.

Newsletter

Get summaries of trending comp sci papers delivered straight to your inbox:

Unsubscribe anytime.