In this paper we investigate the problem of scalable admission control for real-time traffic in sink-tree networks employing per-aggregate resource management policies, like MPLS or DiffServ. Every traffic flow entering the network at an ingress node, and flowing towards a given egress node, specifies its leaky-bucket parameters and a required delay bound for traversing the network. We propose an algorithm that admits a new flow if a guarantee can be given that the required delay bound, besides those of other already established flows, are not exceeded. We identify properties of sink-tree networks based on which we considerably reduce the complexity of the proposed algorithm, and we show that the latter approaches the theoretical lower bound on the worst case complexity of any algorithm working under the same hypotheses. Finally, we show that the algorithm lends itself to a distributed implementation, thus allowing for better scalability.

A Novel Approach to Scalable CAC for Real-time Traffic in Sink-Tree Networks with Aggregate Scheduling

LENZINI, LUCIANO;MARTORINI, LINDA;MINGOZZI, ENZO;STEA, GIOVANNI
2006-01-01

Abstract

In this paper we investigate the problem of scalable admission control for real-time traffic in sink-tree networks employing per-aggregate resource management policies, like MPLS or DiffServ. Every traffic flow entering the network at an ingress node, and flowing towards a given egress node, specifies its leaky-bucket parameters and a required delay bound for traversing the network. We propose an algorithm that admits a new flow if a guarantee can be given that the required delay bound, besides those of other already established flows, are not exceeded. We identify properties of sink-tree networks based on which we considerably reduce the complexity of the proposed algorithm, and we show that the latter approaches the theoretical lower bound on the worst case complexity of any algorithm working under the same hypotheses. Finally, we show that the algorithm lends itself to a distributed implementation, thus allowing for better scalability.
2006
1595935045
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/181025
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