Information-centric Multilayer Networking: improving performance through an ICN/WDM architecture

TL;DR

This paper proposes an innovative ICN/WDM network architecture that leverages content popularity and publish/subscribe mechanisms to enhance network utilization, reduce power consumption, and improve routing efficiency.

Contribution

It introduces an ICN-based publish/subscribe approach to wavelength grouping and develops a power-aware MaxDeg algorithm for optimized routing and power efficiency.

Findings

ICN architecture outperforms traditional IP networks in utilization and power efficiency.

MaxDeg algorithm significantly improves network performance over conventional RWA methods.

Content aggregation based on popularity enhances routing flexibility and optimization.

Abstract

Information-centric networking (ICN) facilitates content identification in networks and offers parametric representation of content semantics. This work, proposes an ICN/WDM network architecture that uses these features to offer superior network utilization, in terms of performance and power consumption. The architecture introduces an ICN publish/subscribe communication approach to the wavelength layer, whereby, content is aggregated according to its popularity rank into wavelength-size groups that can be published and "subscribed to" by multiple nodes. Consequently, routing and wavelength assignment (RWA) algorithms benefit from anycast to identify multiple sources of aggregate content and allow optimization of the source selection of light-paths. A power-aware algorithm, Maximum Degree of connectivity (MaxDeg), has been developed with the objective of exploiting this flexibility to address the trade-off between power consumption and network performance. The algorithm is also applicable to IP architectures, albeit with less flexibility. Evaluation results indicate the superiority of the proposed ICN architecture, even when utilizing conventional routing methods, compared to its IP counterpart. The results further highlight the performance improvement achieved by the proposed algorithm, compared to conventional RWA methods such as Shortest-path First Fit (SFF).