alexa Scalable and Efficient Tree based Memory Mapping in Virtual Routers
ISSN: 2319-9873

Research & Reviews: Journal of Engineering and Technology
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Research Article

Scalable and Efficient Tree based Memory Mapping in Virtual Routers

Deepika V1*, Geetha SK1 and Varunkumarr CV2

1Assistant Professor, Department of CSE, RVS College of Engineering and Technology, Anna University, Coimbatore, India

2System Engineer, Infosys Limited, Chennai, India

*Corresponding Author:
Deepika V
Assistant Professor
Department. of CSE
RVS College of Engineering and Technology
Anna University, Coimbatore, India
E-mail: [email protected]

Received: 17 July 2015 Accepted: 24 Sepetember 2015 Published: 30 Sepetember 2015



Many well liked algorithms for fast packet forwarding and filtering rely on the tree facts and figures structure. Examples are the triebased IP lookup and package classification algorithms. With the latest interest in mesh virtualization, the ability to run multiple virtual router examples on a common personal router stage is absolutely vital. A significant climbing issue is the number of virtual router examples that can run on the platform. One limiting factor is the allowance of high-speed recollection and caches available for storing the packet forwarding and filtering data organizations. A perfect goal is to accomplish good climbing while sustaining total isolation amidst the virtual routers. Although total isolation requires sustaining distinct facts and figures structures in high speed memory for each virtual router. In this paper, we study the case where some sharing of the forwarding and filtering facts and figures organizations is permissible and evolve algorithms for blending tries used for IP lookup and packet classification expressly, we evolve a means called trie braiding that allows us to blend tries from the facts and figures organizations of different virtual routers into just one compact trie. Two optimal braiding algorithms and a faster heuristic algorithm are offered, and the effectiveness is demonstrated utilizing the real-world facts and figures sets.


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