How to Improve the Independent Ability of ForCES Routers

As network requirement of a new generation emerges, such as the endless stream of multimedia services and data center network, network management is heavy, extremely difficult and prone to error. How to achieve the self-management of the network, reduce manual input and improve stability and high efficiency of network is a hot topic in the field of network technology. As we known, network element needs to support the capability of self-management. But in the ForCES architecture, the self-management of network nodes is clearly insufficient. Based on the cognition with the characteristics of artificial intelligence, we explore the ForCES architecture introducing cognizance. So it has the capable of self-learning and self-management. This paper focuses on introducing the basic features, architecture and key technologies of cognition-based ForCES by means of the traditional definition and features of ForCES. DOI:  http://dx.doi.org/10.11591/telkomnika.v11i3.2179

[1]  Hamid Sarbazi-Azad,et al.  Application Specific Router Architectures for NoCs: An Efficiency and Power Consumption Analysis , 2010, 2010 First Workshop on Hardware and Software Implementation and Control of Distributed MEMS.

[2]  Fang Hao,et al.  Building Scalable Virtual Routers with Trie Braiding , 2010, 2010 Proceedings IEEE INFOCOM.

[3]  Yimin Liu,et al.  Overview of Virtual Reality Apply to Sports , 2011 .

[4]  Hoshang Kolivand,et al.  An Overview on Base Real-Time Shadow Techniques in Virtual Environments , 2012 .

[5]  M.-G. Di Benedetto,et al.  Cognitive routing models in UWB networks , 2008, 2008 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).

[6]  Ram Dantu,et al.  Forwarding and Control Element Separation (ForCES) Framework , 2004, RFC.

[7]  Todd A. Anderson,et al.  Requirements for Separation of IP Control and Forwarding , 2003, RFC.

[8]  Allen B. MacKenzie,et al.  Cognitive networks: adaptation and learning to achieve end-to-end performance objectives , 2006, IEEE Communications Magazine.

[9]  E. Álvarez-León,et al.  Intrinsically Contaminated Alcohol-Free Mouthwash Implicated in a Nosocomial Outbreak of Burkholderia cepacia Colonization and Infection , 2006, Infection Control & Hospital Epidemiology.

[10]  C. Demetzos,et al.  Self‐preserving cosmetics , 2009, International journal of cosmetic science.

[11]  Fabrizio Granelli,et al.  Architectures and Cross-Layer Design for Cognitive Networks , 2010 .

[12]  Mohd Shahrizal Sunar,et al.  An Overview on Base Real-Time Hard Shadow Techniques in Virtual Environments , 2012 .

[13]  P. Coll,et al.  Moisturizing body milk as a reservoir of Burkholderia cepacia: outbreak of nosocomial infection in a multidisciplinary intensive care unit , 2008, Critical care.

[14]  J. Huang,et al.  Self-adaptive Decomposition Level De-noising Method Based on Wavelet Transform , 2012 .

[15]  Yukyong Kim QoS-Aware Web Services Discovery with Trust Management , 2008, J. Convergence Inf. Technol..

[16]  Ryan W. Thomas,et al.  Cognitive networks , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..