Pragati Node Popularity (PNP) Approach to Identify Congestion Hot Spots in MPLS
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.
[1] D. Awduche et al, "Overview and principles of Internet Traffic
Engineering" IETF, Internet draft, draft-ietf-tewg-principles-02.txt,
Jan 2002.
[2] D. Awduche, J.Maledm, J. Agogbua, M.O-Dell and J. McManus,
"Requirements for traffic Engineering over MPLS", IETF RFC 2702,
Sep 1999.
[3] ATM Forums, Private-Network-Network-Interface Specification
Version 1.0 (PNNI 1.0), International Standart-of-pnni-oo55.000, March
1996.
[4] Yufei Wang, Zheng Wang, Leah Zhang, "Internet traffic engineering
without full mesh Overlaying", Infocom 2001.
[5] Raj P.H and SVK Raja, Identifying Congestion hot spots in MPLS using
Bayesian network", Asia J.Inform. Technol., 6 : 854-858, 2007.
[6] F. Jensen, "An Introduction to Bayesian Networks", UCL Press, 1996.
[7] J. Moy, "OSPF version 2", Internet rtc, IETF May 1998, Technical
report RFC 2328.
[8] Yufei Wang and Zheng Wang, Explicit routing algorithm for Internet
Traffic Engineering", Proceeding of ICCCN-99, Sep 1999.
[9] C. Villamizer, "OSPF optimized multipoint", Internet draft draft-ietfospf-
omp-00.txt, IETF, March 1998.
[1] D. Awduche et al, "Overview and principles of Internet Traffic
Engineering" IETF, Internet draft, draft-ietf-tewg-principles-02.txt,
Jan 2002.
[2] D. Awduche, J.Maledm, J. Agogbua, M.O-Dell and J. McManus,
"Requirements for traffic Engineering over MPLS", IETF RFC 2702,
Sep 1999.
[3] ATM Forums, Private-Network-Network-Interface Specification
Version 1.0 (PNNI 1.0), International Standart-of-pnni-oo55.000, March
1996.
[4] Yufei Wang, Zheng Wang, Leah Zhang, "Internet traffic engineering
without full mesh Overlaying", Infocom 2001.
[5] Raj P.H and SVK Raja, Identifying Congestion hot spots in MPLS using
Bayesian network", Asia J.Inform. Technol., 6 : 854-858, 2007.
[6] F. Jensen, "An Introduction to Bayesian Networks", UCL Press, 1996.
[7] J. Moy, "OSPF version 2", Internet rtc, IETF May 1998, Technical
report RFC 2328.
[8] Yufei Wang and Zheng Wang, Explicit routing algorithm for Internet
Traffic Engineering", Proceeding of ICCCN-99, Sep 1999.
[9] C. Villamizer, "OSPF optimized multipoint", Internet draft draft-ietfospf-
omp-00.txt, IETF, March 1998.
@article{"International Journal of Biological, Life and Agricultural Sciences:53269", author = "E. Ramaraj and A. Padmapriya", title = "Pragati Node Popularity (PNP) Approach to Identify Congestion Hot Spots in MPLS", 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.", keywords = "Conditional Probability Distribution, Congestion hotspots, Operational Networks, Traffic Engineering.", volume = "4", number = "8", pages = "546-5", }