AN IMPROVED GMPLS SURVIVABILITY MECHANISM USING

LINK DELAY-CONSTRAINED ALGORITHM

Anastasios Bikos

, Christos Bouras

1,2

and Kostas Stamos

1,2,3

Research Academic Computer Technology Institute, Patras, Greece

Computer Engineering and Informatics Department, University of Patras, Patras, Greece

Technological Educational Institute of Patra, Patra, Greece

Keywords: GMPLS, Link failure probability, Link delay-constrained Algorithm, LSP routing, Path protection.

Abstract: It is widely accepted that GMPLS (Generalized MPLS) will be a key technology in the evolution of the next

generation of reliable Internet Protocol (IP) backbone networks. Conventional GMPLS-based optical-

switching network fault recovery only provides resiliency in terms of path segment selection instead of

constraint-based calculation. This can create severe impact on the protocol’s transport plane when a fault

occurs to a link or path with many optical connections attached to it. This paper proposes the

implementation of an improved GMPLS recovery algorithm based on the metric of optical link delay which

is achieved through the pre or post selection of a safer and more stable protection path with fewer

connections attached to it, and therefore with a lesser link delay metric compared to other possible paths.

The improved recovery algorithm is evaluated using the network simulator ns-2 and more particularly a

specialized simulator add-on for GMPLS, called ASONS (Automatically Switched Optical Network

Simulator). The results indicate improved resiliency, increased fault avoidance, and reduced packet loss.

1 INTRODUCTION

As IP traffic becomes more and more massive, the

optical switching network emerges as the most

promising solution for meeting the modern

backbone network needs. This has caused the

introduction of GMPLS (Generalized Multi-Protocol

Label Switching), as a reliable and stable optical

framework, in order to meet those new standards and

developments of telecom services (Dutta, 2008;

Stern, 2009; Farrel, 2006).

While optical fiber medium using wavelength

division multiplexing (WDM) offers tremendous

transmission bandwidth to deliver high-traffic

services cost effectively, faults such as the

unavailability of optical links are still an important

issue to resolve. Because the most massive amount

of traffic is transmitted over the optical backbone

network, a fault in the backbone may result in very

important service degradation. This forces Internet

Service Providers (ISPs) to include network

reliability parameters in their Service Level

Agreements and to design new protection strategies

guaranteeing fast failure recovery times and high

levels of reliability.

In this paper, we describe the design and

performance analysis of a QoS-Constrained GMPLS

Recovery algorithm, based on the ideas that have

been presented in (Ortega, 2004) for MPLS, which

is based on the dynamically changing optical link

Delay Constraint, distributed by the IGP Routing

Protocol. This makes the GMPLS fault recovery

procedure able to adapt to the current network state

and based on that condition it then becomes possible

to post compute and configure the backup path.

Finally, we evaluate the implementation of the

currently existing GMPLS Protection Mechanisms,

as well as an improved Restoration Mechanism,

based on this new algorithm, under the network

simulator ns-2 environment.

2 NETWORK SURVIVABILITY

ISSUES AND RECOVERY

SCHEMES

Next-generation optical communication technologies

(DWDM/OADM/PXC) are expected to exceed

aggregate capacities of hundreds of terabits per

Bikos A., Bouras C. and Stamos K..

AN IMPROVED GMPLS SURVIVABILITY MECHANISM USING LINK DELAY-CONSTRAINED ALGORITHM.

DOI: 10.5220/0003433900450050

In Proceedings of the International Conference on Data Communication Networking and Optical Communication System (DCNET-2011), pages 45-50

ISBN: 978-989-8425-69-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

second. As wavelength routing and all-optical

switching paves the way for network throughput of

such scales, network survivability assumes critical

importance. A single loss of or damage to a fiber is a

common means of a greater loss. A short network

outage can lead to huge data loss, particularly in the

backbone core. Thus, a connection being carried in

the network also needs high protection and

resilience. Survivability refers to the ability of the

network to reconfigure and re-establish

communication upon node and/or link failures.

Such network survivability can be classified into

two general categories: pre planned protection and

dynamic restoration. In pre-planned protection-based

techniques resources are already planned, typically

at the time of establishing a lightpath connection, to

recover from network failures and hence recovery is

faster.

During the normal operation phase these

reserved resources remain idle. Upon the occurrence

of failure, reserved resources are used to recover

from the failure according to protection protocols. In

contrast, in dynamic restoration, the resources used

for recovery from failure are not reserved at the time

of connection establishment, but are discovered

dynamically using link state algorithms when a

failure occurs. As it is obvious, dynamic restoration

uses resources efficiently, but the restoration time is

usually longer, because it requires the establishment

of a new functional backup path. Moreover, 100%

service recovery cannot be guaranteed as it is not

guaranteed that the spare capacity is available at the

time of failure (Dutta, 2008).

2.1 Problems with Conventional

GMPLS Restoration Mechanisms

One of the most common problems of the existing

fault recovery schemes in GMPLS networks is that

they do not consider the already existing link load of

a backup path when it has to be configured. A

typical bad case scenario is when selecting an

optical link which is a critical segment, or cut-edge,

for many connections. It has been shown that a

failure on this link has more overall impact on the

network traffic (Changwoo, 2007). Figure 1 shows a

related network situation where more connections

cross through a particular optical link, which

therefore acts as a bridge, than other links. As the

number of connections increases in a particular link,

so does the overall impact of a potential failure of

the link.

It is well known that some links have higher

failure probabilities, and this can be attributed to

their physical situation and conditions. This Link

Failure Probability Factor (LFP) is based on the type

of physical link, the node characteristics and

geographical distribution of the network segments.

Since these parameters are outside of our control, we

consider the LFP values for the network topology as

given, and our purpose is to route backup paths so

that traffic distribution across the network becomes

as even as possible. Thus we can try to decrease the

impact of new potential network faults in terms of

affected connections.

Figure 1: Link Failure in a Path with many connections

(Changwoo, 2007).

3 LINK DELAY-CONSTRAINED

ALGORITHM

IMPLEMENTATION

Due to the previous problems that occur from

traditional unconstrained Restoration Schemes in

GMPLS networks, our proposed algorithm

configures a backup path by searching for the

optimal path through the link state algorithm, based

on the delay parameter. The key concept of this

improved algorithm is that the path selection

procedure typically prefers links that carry fewer

connections, and thus, given the Link Failure

Probability Factors, distributes the impact of links

failures on LSP more evenly.

3.1 Constraint-based Algorithm

The implementation of our mechanism, from now on

called LDC (Link Delay-Constrained), is separated

in two phases: The Link Searching for Delay

Constraint Procedure and the Modified Dijkstra

Algorithm which takes into account the filtered link

information, with newer cost values, from the

previous phase according to the least delay

constraint. The delay metric can be calculated using

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on the fly measurements.

3.1.1 Link Searching for Delay Constraint

We present the following pseudo code that searches

for the optical link that satisfies the least link delay

constraint among all its neighbouring ones. In

particular, when it indeed finds this optimal link, it

sets all its nearby links, except itself, with higher arc

weight or cost (w(n, mi) >= 2), so that the Modified

Dijkstra’s Algorithm in the second phase will be

able to calculate the optimal path based on these new

link cost metrics, and thus re-update the whole

routing table. The optimal link selected from this

phase will continue to preserve the default cost value

(c=1), thus it will remain first selection priority for

the Dijkstra’s Algorithm, in order to create the

Protection Path.

L = All links except primary path links(l)

Function Link_Delay_Constraint_Search(L)

FOR each node n

Find link with min delay {lmin}

FOR each link l of n

IF l ≠ lmin

Set weight value of link l = 2

ELSE

lmin = 1 {default link cost}

Return L

Figure 2: Link Searching for Delay Constraint.

3.1.2 Modified Dijkstra’s Algorithm

The algorithm (Changwoo, 2007) receives the L

value from the previous phase and then searches and

selects a link to the destination t with the smallest

number of connections within pool L. Afterwards,

the optimal backup path is being configured. In any

case, even if an optimal path segment to a specific

node destination might not logically exist, due to the

previous link delay cost update, the Dijkstra’s

Algorithm (Changwoo, 2007) will compromise to a

path selection even with worse arc weight (w ≥ 2).

4 REDUCING THE IMPACT OF

THE LFP FACTOR WITH LDC

As discussed in the introductory section, in GMPLS-

based networks the usual method of recovering a

failure is the utilization of an alternative and disjoint

path to the main working path. The general time

process for the failure recovery procedure which

applies to both pre planned protection and dynamic

restoration is discussed in (Ortega, 2004).

Since the failure event point occurs, there is a

time period when data packets are inevitably being

lost due to the uncompleted switchover process, and

until the normalization procedure finishes. If the

survivability mechanism (either pre planned or

dynamic) does not consider the load of traffic

carried by each link of the potential protection LSP,

the restoration can become prone to more recurrent

faults and their associated costs. One such example,

where the impact of Link Failure Probability greatly

affects the future failure impact of the protection

LSP is shown and described in Figure 3.

Figure 3 (a): Link Failure Probability in the Working Path

with Conventional Resiliency.

Figure 3 (b): Link Failure Probability in the Working Path

with LDC Improved Resiliency.

Let as assume that the centralized optical links 3-

6 and 6-9 act as bridges for the overall network

traffic, thus contain a large number of optical

connections, as most traffic passes through them. In

Figure 3 (a) the working path (formed by the LSRs

1-3-6-9-11) contains the two links with high Link

Failure Probability (3-6 and 6-9).Unfortunately this

conventional method for path selection causes a high

risk of increased service impact, should a possible

link failure occur in the future. Indeed, when a

failure event occurs between the nodes 3 and 6 (one

of the two links mentioned earlier with high LFP

Factor), the GMPLS conventional survivability

AN IMPROVED GMPLS SURVIVABILITY MECHANISM USING LINK DELAY-CONSTRAINED ALGORITHM

procedure configures a segmented backup Path that

only avoids this faulty connection and does not

consider any other constraint condition for some

potential future failures on itself.

On the other hand, the LDC Survivability

mechanism not only avoids the fault, but it actually

configures a disjoint backup path that is both

optimal and safe. Thus, it diminishes the impact of a

further link failure (between the nodes 6 and 9), by

choosing the optical link 7-9, which is

conventionally not selected to be the minimum path

segment by the Routing Algorithm.

This has great effect on the network’s Resilience

level both for dynamic or pre planned protection

methods. In the first case, after the fault, the LDC

link state algorithm adapts to the current network

conditions and state (concerning the number of link

connections) in order to select the optimal backup

path. In the second case, again based on the same

algorithm, we pre-select this optimal path with the

advantage now of gaining in packet loss and

switchover normalization time, compared to the post

planned protection method which needs to establish

a new lightpath. The results can be a more evenly

distributed network topology and traffic as we can

clearly see in Figure 4 (Compared to Figure 1).

Figure 4: An evenly distributed network traffic

(Changwoo, 2007).

4.1 Improved Restoration Mechanism

using the LDC Algorithm

In this section we examine the issue of restoration

and calculation of a backup path after the failure has

occurred. Figure 5 illustrates the unconstrained

selection of a backup path (dashed line) in case of

failure. The particular path is in fact prone to more

potential future failures compared to the LDC

algorithm which takes into consideration the current

load for each link in the topology graph to form the

optimal path (bold line). The only difference with

the previous schemes is that this procedure is being

implemented a posterior, after the link failure event.

The results can be increased fault avoidance and

resiliency, and also a more evenly distributed

network traffic across the network, without

congested lines and nodes prone to new failures.

Figure 5: Post selecting an optimal backup path based on

the LFP factor.

5 EXPERIMENTAL RESULTS ON

NS-2

For evaluating the LDC algorithm we utilize the

network simulator ns-2 environment

(www.isi.edu/nsnam/) along with the ASONS

simulator (An Automatically Switched Optical

Network Simulator, www.telecom.ntua.gr/asons/).

For our experiments we use an example network

topology consisting of 14 nodes (figure 5). It

consists of 14 nodes and 21 STM-64 (10Gbps) SDH

Full-Duplex FiberLinks. We simulate real physical

mile distances by using equivalent link delays. Each

experiment lasts for 10 seconds, and the (sequential)

failure events occur approximately at 5.0, 5.5, 6.0,

6.5 and 7.0 sec. The failure points are between

nodes: 2-4, 4-5, 5-7, 7-8, and 8-11 respectively.

Table 1 illustrates the parameters used.

Table 1: Network parameters for the experiments.

Network Parameters

Traffic Parameters

(Exponential VBR)

Link BW : 10 Gbps

Link Delay : 5×10

-4

sec

Fiber Delay : 1×10

-3

sec

Network Load : Exponential

Traffic Rate : 100Mb

Packet Size : 100 Bytes

DCNET 2011 - International Conference on Data Communication Networking

Table 2: The impact of Failure Notification Distance

(relevant to the LFP Factor) and receiving Bandwidth Rate

to Recovery Time (T

REC

) and total Packet Loss (PLS).

roug

(Mbps)

Failure Noti

ication Distance

D(i,a) = 1 D(i,a) = 2 D(i,a) = 4 D(i,a) = 0

TREC PLS TREC PLS TREC PLS TREC PLS

23,5 1989 30,7 1992 42,1 3511 0,0 291

26,2 2695 33,3 4045 43,2 4112 0,0 300

26,6 2962 33,4 4858 45,6 4327 0,0 304

10 25,6 6948 36,1 9432 44,7 9878 0,0 334

In Table 2, the influence of the Failure Notification

Distance for different receiving traffic rates is being

shown. It is illustrated in this experiment that these

LSPs with higher Failure Notification Distance from

the ingress node (the node responsible for the

Failure Notification Procedure), are more likely to

experience long recovery times and packet loss.

More specifically, it is shown that T

REC

is directly

proportional to physical distance between the failure

point and the ingress node. As D(i,a), or the number

of successive hops between i (the ingress node) and

a (the hop where failure occurs), increases relatively

to the Traffic Throughput Rate, so does the

propagation link delay (T

REC

) in conjuction to the

total amount of packet loss (PLS).The final case

(D(i,a) = 0) is the most optimal since a local backup

protection method is being selected.

5.1 Evaluating the GMPLS Protection

Mechanisms using the LDC

Algorithm

For implementing the improved Protection

Mechanisms (using the LDC algorithm) we deploy

100 LSPs, in total, from nodes 1,2,3,6,9,10,12,13,14

to node 15 (egress destination node), as main

working paths. After each disjoint link failure

occurs, we compare the Default Protection state in

the asons environment, which picks up the backup

paths in an unconstrained manner, to the improved

LDC based Mechanism, which preselects the

optimal backup path(s) based on the least link-delay

constraint., We utilize the algorithm to select a pre

planned 1+1 backup path for each working path, as

well as M≥1 protection paths for the M:N scheme.

The two other Protection Mechanisms (1:1 and 1+N)

are equivalent to the previous ones, thus there is no

need to further examine them.

0 20406080

No of Sequential Link

Failures

No of Affected (Faulty) LSPs

Default

Protection

Unconstrained

Improved

Protection

LDC

Figure 6: Comparing the LDC Protection Mechanism to

the Default Unconstrained on the number of Damaged

LSPs (1+1 case).

As we can clearly see in Figure 6, for the 1+1 case,

the greater the number of sequential faults occurs the

more is the amount of LSPs being affected from the

source nodes to egress destination node. Yet, while

this true for both implementations (the

unconstrained and the metric-based), in the LDC

case we manage to obtain more lightpaths unaffected

by the network failures, thus retain traffic normality

and increased resiliency. What is most important is

that the total network traffic distribution is more

evenly applied across the topology, after the

utilization of the LDC algorithm. The results can be

increased network fault avoidance as well as reduced

packet loss, due to the unaffected and better

protected LSPs. Figure 7 also illustrates the same

effects for the M:N case.

0 10203040506070

No of Sequential Link Failures

No of Affected (Faulty) LSPs

Default

Protection

Unconstrained

Improved

Protection LDC

Figure 7: Comparing the LDC Protection Mechanism to

the Default Unconstrained on the number of Faulty LSP’s

(M:N case).

AN IMPROVED GMPLS SURVIVABILITY MECHANISM USING LINK DELAY-CONSTRAINED ALGORITHM

0.1

0.2

0.3

0.4

0.5

0.6

0.7

12345

Trial Number (# of Failures)

LSP Failure Ratio (%)

Improved Protection LDC Default Protection Unconstrained

Figure 8: LSP Rejection Rate Analysis for the 1+1 case

during adjacent link failures. LDC mechanism versus the

default unconstrained.

Figure 8 demonstrates the percentage of LSPs being

rejected as faulty for both Protection Mechanisms

(LDC and Unconstrained) during successive link

failures. It is important to notice that the LDC

algorithm itself increases the total ratio of functional

paths as more disjoint faults occur, contributing to

better protected and fairly distributed network

traffic. This happens because congested lines are

being avoided, the total number of traffic cut-edges

is further minimized, and finally a more even

network topology in terms of traffic flow is

achieved. Results in Figure 9 show similar

behaviour.As the Trial Number increases and more

failure events congest and aggravate the whole

network flow, the LDC solution protects more paths

than the conventional method offering higher

resilience rate and traffic smoothness.

0 500 1000 1500 2000 2500 3000

Improved

ProtectionLDC

Default

Protection

Unconstrained

#ofLSPs

TrialNumber(#ofFailures)

Figure 9: Number of Protected LSPs for the M:N case

during adjacent link failures. Comparing the LDC

mechanism versus the default unconstrained.

6 CONCLUSIONS – FUTURE

WORK

This paper proposes the implementation of improved

GMPLS Survivability mechanisms (both Protection

and Restoration) through an efficient Constrained-

based link-state Algorithm (LDC). This algorithm

considers the delay metric on each link of the

topology, a parameter which is directly related to the

total number of connections the link has. By pre or

post selecting a path with having less connections as

the backup path, it achieves a higher safety level for

the restoration case as well as faster recovery times

and more delivered packets for the pre planned

method. For further research prospects, there will

need to be an investigation of a multi-constrained

algorithm which takes for granted other metrics and

QoS measurements, something that could provide

even more stable and robust protection across the

GMPLS networks.

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DCNET 2011 - International Conference on Data Communication Networking