Abstract: LSP routing is among the prominent issues in MPLS
networks traffic engineering. The objective of this routing is to
increase number of the accepted requests while guaranteeing the
quality of service (QoS). Requested bandwidth is the most important
QoS criterion that is considered in literatures, and a various number
of heuristic algorithms have been presented with that regards. Many
of these algorithms prevent flows through bottlenecks of the network
in order to perform load balancing, which impedes optimum
operation of the network. Here, a modern routing algorithm is
proposed as MIRAD: having a little information of the network
topology, links residual bandwidth, and any knowledge of the
prospective requests it provides every request with a maximum
bandwidth as well as minimum end-to-end delay via uniform load
distribution across the network. Simulation results of the proposed
algorithm show a better efficiency in comparison with similar
algorithms.
Abstract: The overlay approach has been widely used by many service providers for Traffic Engineering (TE) in large Internet backbones. In the overlay approach, logical connections are set up between edge nodes to form a full mesh virtual network on top of the physical topology. IP routing is then run over the virtual network. Traffic engineering objectives are achieved through carefully routing logical connections over the physical links. Although the overlay approach has been implemented in many operational networks, it has a number of well-known scaling issues. This paper proposes a new approach to achieve traffic engineering without full-mesh overlaying with the help of integrated approach and equal subset split method. Traffic engineering needs to determine the optimal routing of traffic over the existing network infrastructure by efficiently allocating resource in order to optimize traffic performance on an IP network. Even though constraint-based routing [1] of Multi-Protocol Label Switching (MPLS) is developed to address this need, since it is not widely tested or debugged, Internet Service Providers (ISPs) resort to TE methods under Open Shortest Path First (OSPF), which is the most commonly used intra-domain routing protocol. Determining OSPF link weights for optimal network performance is an NP-hard problem. As it is not possible to solve this problem, we present a subset split method to improve the efficiency and performance by minimizing the maximum link utilization in the network via a small number of link weight modifications. The results of this method are compared against results of MPLS architecture [9] and other heuristic methods.
Abstract: Traffic Engineering (TE) is the process of controlling
how traffic flows through a network in order to facilitate efficient and
reliable network operations while simultaneously optimizing network
resource utilization and traffic performance. TE improves the
management of data traffic within a network and provides the better
utilization of network resources. Many research works considers intra
and inter Traffic Engineering separately. But in reality one influences
the other. Hence the effective network performances of both inter and
intra Autonomous Systems (AS) are not optimized properly. To
achieve a better Joint Optimization of both Intra and Inter AS TE, we
propose a joint Optimization technique by considering intra-AS
features during inter – AS TE and vice versa. This work considers the
important criterion say latency within an AS and between ASes. and
proposes a Bi-Criteria Latency optimization model. Hence an overall
network performance can be improved by considering this jointoptimization
technique in terms of Latency.
Abstract: In large Internet backbones, Service Providers
typically have to explicitly manage the traffic flows in order to
optimize the use of network resources. This process is often referred
to as Traffic Engineering (TE). Common objectives of traffic
engineering include balance traffic distribution across the network
and avoiding congestion hot spots. Raj P H and SVK Raja designed
the Bayesian network approach to identify congestion hors pots in
MPLS. In this approach for every node in the network the
Conditional Probability Distribution (CPD) is specified. Based on
the CPD the congestion hot spots are identified. Then the traffic can
be distributed so that no link in the network is either over utilized or
under utilized. Although the Bayesian network approach has been
implemented in operational networks, it has a number of well known
scaling issues.
This paper proposes a new approach, which we call the Pragati
(means Progress) Node Popularity (PNP) approach to identify the
congestion hot spots with the network topology alone. In the new
Pragati Node Popularity approach, IP routing runs natively over the
physical topology rather than depending on the CPD of each node as
in Bayesian network. We first illustrate our approach with a simple
network, then present a formal analysis of the Pragati Node
Popularity approach. Our PNP approach shows that for any given
network of Bayesian approach, it exactly identifies the same result
with minimum efforts. We further extend the result to a more
generic one: for any network topology and even though the network
is loopy. A theoretical insight of our result is that the optimal routing
is always shortest path routing with respect to some considerations of
hot spots in the networks.