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Hierarchical virtual partitioning: algorithms for virtual private networking

Authors: Debasis Mitra 0001; Ilze Ziedins 0001;

Hierarchical virtual partitioning: algorithms for virtual private networking

Abstract

Hierarchical virtual partitioning facilitates sharing of a resource, such as a link, by multiple customers, each with multiple service classes. Calls of each service class have distinctive bandwidth requirements, arrival rates and mean holding times. The quality of service is in terms of call blocking probabilities. The algorithms allow the desired mix of fairness, robustness and multiplexing efficiency at each of two levels, namely, customers and the various services of each customer. The scheme relies on nominal allocations of capacities to each customer and service, and priorities in the call admission process, which are implemented by dynamic trunk reservations. An approximate method of analysis based on fixed point equations is given. A reward/penalty paradigm is devised to reflect the issues in virtual private networking and the performance of our algorithm compared to the optimal solution. The results show that the scheme is fair, efficient and robust.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Average
Top 10%
Top 10%
Related to Research communities
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