Link addition framework for optical CDNs robust to targeted link cut attacks

Content Delivery Networks (CDNs) are a key en-abler for the distribution of large amounts of data with high capacity and low latency. For instance, content streaming companies extensively use geographical distribution and replication to meet the ever-growing demand for media. Optical networks are the only future-proof technology available that meets the reach and capacity requirements of CDNs. However, the robustness of optical networks becomes a point of concern, as they can be a target of deliberate link cuts that can severely degrade network connectivity and cause large-scale service disruption. To mitigate the vulnerabilities, actions can be taken in the optical and/or cloud infrastructures. The replication of content across geographically diverse data centers results in an intrinsic increase of content accessibility. At the network infrastructure level, robustness to attacks can be enhanced by increasing the topology connectivity through link addition. This work focuses on the latter approach and its effectiveness in increasing content accessibility in the presence of deliberate link cuts. The paper proposes a framework for evaluation and enhancement of content accessibility in CDNs by sparse link addition. First, a content accessibility measure called μ-ACA is introduced to gauge the content accessibility of a given network topology under a set of link cut attack scenarios. Based on this measure, a new link addition strategy is defined aimed at maximizing the content accessibility for a given number of extra links. Simulation results on real-world reference topologies show that the proposed strategy can significantly improve content accessibility by adding a very limited number of optical fiber links.

[1]  Chen Hong,et al.  Improving the network robustness against cascading failures by adding links , 2013 .

[2]  Antoine Dutot,et al.  GraphStream: A Tool for bridging the gap between Complex Systems and Dynamic Graphs , 2008, ArXiv.

[3]  Petter Holme,et al.  Onion structure and network robustness , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Lena Wosinska,et al.  Content accessibility in optical cloud networks under targeted link cuts , 2017, 2017 International Conference on Optical Network Design and Modeling (ONDM).

[5]  Hans J. Herrmann,et al.  Onion-like network topology enhances robustness against malicious attacks , 2011 .

[6]  Sofie Verbrugge,et al.  RECODIS: Resilient Communication Services Protecting End-user Applications from Disaster-based Failures , 2016, 2016 18th International Conference on Transparent Optical Networks (ICTON).

[7]  Samee Ullah Khan,et al.  On the Connectivity of Data Center Networks , 2013, IEEE Communications Letters.

[8]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[9]  Caterina Scoglio,et al.  Characterizing the Robustness of Complex Networks , 2008, 0811.3272.

[10]  An Zeng,et al.  Enhancing network robustness for malicious attacks , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  J. M. Simmons,et al.  Optical Network Design and Planning , 2008 .

[12]  Hans J. Herrmann,et al.  Mitigation of malicious attacks on networks , 2011, Proceedings of the National Academy of Sciences.

[13]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[14]  R. Linsker,et al.  Improving network robustness by edge modification , 2005 .

[15]  José-Luis Marzo,et al.  Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements , 2016, Journal of Network and Systems Management.

[16]  Michal Pioro,et al.  SNDlib 1.0—Survivable Network Design Library , 2010 .

[17]  Andrea Passarella,et al.  A survey on content-centric technologies for the current Internet: CDN and P2P solutions , 2012, Comput. Commun..