KEYWORDS: Switching, Networks, Reliability, Optimization (mathematics), Internet, Network architectures, Process modeling, Data communications, Control systems, Data modeling
MPLS-based recovery is intended to effect rapid and complete restoration
of traffic affected by a fault in an MPLS network. Two MPLS-based recovery
models have been proposed: IP re-routing which establishes recovery paths
on demand, and protection switching which works with pre-established
recovery paths. IP re-routing is robust and frugal since no resources are
pre-committed but is inherently slower than protection switching which
is intended to offer high reliability to premium services where fault
recovery takes place at the 100 ms time scale. We present a model of
protection switching in MPLS networks. A variant of the flow deviation
method is used to find and capacitate a set of optimal label switched
paths. The traffic is routed over a set of working LSPs. Global repair
is implemented by reserving a set of pre-established recovery LSPs.
An analytic model is used to evaluate the MPLS-based recovery mechanisms
in response to bi-directional link failures. A simulation model is
used to evaluate the MPLS recovery cycle in terms of the time needed to
restore the traffic after a uni-directional link failure. The models
are applied to evaluate the effectiveness of protection switching in
networks consisting of between 20 and 100 nodes.
Multi-protocol Label Switching extends the IPv4 destination-based routing protocols to provide new and scalable routing capabilities in connectionless networks using relatively simple packet forwarding mechanisms. MPLS networks carry traffic on virtual connections called label switched paths. This paper considers path selection and bandwidth allocation in MPLS networks in order to optimize the network quality of service. The optimization is based upon the minimization of a non-linear objective function which under light load simplifies to OSPF routing with link metrics equal to the link propagation delays. The behavior under heavy load depends on the choice of certain parameters: It can essentially be made to minimize maximal expected utilization, or to maximize minimal expected weighted slacks (both over all links). Under certain circumstances it can be made to minimize the probability that a link has an instantaneous offered load larger than its transmission capacity. We present a model of an MPLS network and an algorithm to find and capacitate optimal LSPs. The algorithm is an improvement of the well-known flow deviation non-linear programming method. The algorithm is applied to compute optimal LSPs for several test networks carrying a single traffic class.
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