Traffic protection in MPLS networks using an
off-line flow optimization model
A.E. Krzesinski and K.E. M¨uller
Department of Computer Science
University of Stellenbosch, 7600 Stellenbosch, South Africa
Phone: +27 21 8084232 Fax: +27 21 8084416 Email:
{aek1,kmuller}@cs.sun.ac.za
P.G. Taylor
Department of Mathematics and Statistics
University of Melbourne, Melbourne, Australia
Email: p.taylor@ms.unimelb.edu.au
Abstract—
MPLS-based recovery is intended to effect rapid and com-plete restoration of traffic affected by a fault in an MPLS work. We present a model of protection switching in MPLS net-works. 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. Protection switching is im-plemented by reserving a set of pre-established recovery LSPs. A simulation model is used to evaluate the MPLS recovery cy-cle in terms of the time needed to restore the traffic after a uni-directional link failure. The model is applied to evaluate the ef-fectiveness of protection switching in a 20-node network.
Keywords—
Label switched paths, multi-protocol label switching, traffic engineered routes, traffic recovery, protection switching.
I. I
NTRODUCTIONThe Internet is becoming the ideal platform to sup-port all forms of modern communications including voice, data and multimedia transmissions. However, the standard IP routing protocols were developed on the basis of a connectionless model where routing de-cisions are based on simple metrics such as delay or hop count which leads to the selection of shortest path routes. Despite its ability to scale to very large net-works, this approach provides support for only rudi-mentary Quality of Service (QoS) capabilities which cannot be used to provide scalable service level agree-ments for bandwidth intensive applications in modern networks.
Multi-protocol label switching (MPLS) [1] extends the IP destination-based routing protocols to provide new and scalable routing capabilities. MPLS rout-ing/switching is achieved by forwarding IP packets along virtual connections called label switched paths (LSPs). LSPs are set up by a label distribution pro-tocol which uses the information contained in layer 3 routing tables. The LSPs form a logical network that is layered on top of the physical network to provide con-nection oriented processing above the concon-nectionless
This work is supported by grants from the Australian Research Council, the South African National Research Foundation, Siemens Telecommunications and Telkom SA Limited.
IP network.
In a previous paper [2] we developed a method for finding an optimal set of LSPs. We formulated the problem of finding an optimal set of LSPs and optimally allocating bandwidths to these LSPs as a constrained non-linear programming problem (NLP) which minimizes an objective function that affords an appropriate representation of the quality of the net-work. We adapted the flow deviation method [3], [4], [5] to solve the NLP.
In this paper we investigate some aspects of MPLS-based recovery [6] which is intended to effect rapid and complete restoration of traffic affected by a fault in an MPLS network. Two recovery models have been proposed for MPLS networks: IP re-routing which es-tablishes recovery paths on demand, and protection
switching which works with pre-established recovery
paths.
This paper presents a model of protection switching in MPLS networks. A more detailed study of MPLS recovery including analytic and simulation models can be found in [7]. The NLP solver is used to design and capacitate an optimal set of LSPs which defines the working (active, primary) LSPs: the traffic is routed over the working LSPs. We next present a method for finding one or more alternate (recovery, back-up, pro-tection) paths for each working path: the recovery path is used when its working counterpart fails. Global re-pair is implemented by reserving a set of LSPs for use as pre-established recovery paths.
The rest of the paper is organized as follows. Sect. II presents an overview of MPLS recovery. The ob-jectives of MPLS recovery are presented and various recovery models are compared. Sect. III presents a model of an MPLS network, definitions of feasible and optimal LSP bandwidth assignments and a description of the LSP design problem whose solution yields an optimal set of LSPs and optimal LSP bandwidth as-signments. Sect. IV presents an efficient method for computing an optimal set of recovery paths. Sect. V presents a simulation model which is used to evaluate
the dynamics of protection recovery in a models of 20-and 50-node networks. Our conclusions are presented in Sect. VII.
II. A
NO
VERVIEW OFMPLS
BASEDRECOVERY
A. The Objectives of MPLS Based Recovery
MPLS based recovery mechanisms and techniques should: (1) be subject to the traffic engineering (TE) goal of optimal use of resources, (2) facilitate restora-tion times that are sufficiently fast for the end-user ap-plications, (3) maximize network reliability and avail-ability and minimize the number of single points of failure in the MPLS protected domain, (4) enhance the reliability of the protected traffic while minimally or predictably degrading the traffic carried by the di-verted resources, (5) protect the traffic at various gran-ularities 1 (6) be applicable to an entire end-to-end path or to segments of an end-to-end path, (7) take into consideration the recovery actions of the lower layers and should not trigger lower layer protection switching, (8) minimize the loss of data and packet re-ordering during recovery operations, (9) minimize the state overhead incurred for each recovery path main-tained and, (10) preserve the constraints on traffic af-ter switchover, if desired, so that the recovery path meets the resource requirements of the working path and achieves the same performance characteristics as the working path.
Some of the above goals are in conflict with each other and real deployment will involve compromises based on a variety of factors such as cost, end-user application requirements, network efficiency, and rev-enue considerations.
B. Recovery Models for MPLS Networks
Two recovery models have been proposed for MPLS networks: 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 pro-tection switching which is intended to offer high reli-ability to premium services where fault recovery takes place at the 100 ms time scale. This paper does not address IP re-routing.
Protection switching works with pestablished re-covery paths. A rere-covery path may support the same traffic contract as the working path, or it may not. An
equivalent recovery path can replace a working path
without degrading service. A limited recovery path lacks the resources (or the resource reservations) to re-place the working path without degrading service.
1Three levels of traffic granularity are proposed: part of the
recov-ery traffic can be allocated to an individual path, all of the recovrecov-ery traffic can be allocated to an individual path, or all of the recovery traffic can be allocated to a group of paths.
There are two options for the initiation of resource allocation: pre-reserved allocation which only applies to protection switching and reserved-on-demand allo-cation which may apply either to IP re-routing or to protection switching. A preserved recovery path re-serves required resources on all hops along its route during its establishment before any failure has oc-curred. A reserved-on-demand recovery path reserves required resources after a failure on the working paths has been detected and before the traffic on the working path is switched over to the recovery path(s).
C. Comparison Criteria
Several criteria have been suggested for comparing various MPLS-based recovery schemes. The recovery
time is the time between a failure of a node or link and
the time before a recovery path is installed and the traf-fic starts flowing on it. The full restoration time is the time required for traffic to be routed onto links which are capable of (or have been engineered to) handle traf-fic in recovery scenarios. The setup vulnerability time is the time that a working path or a set of working paths is left unprotected during such tasks as recovery path computation.
Recovery schemes may require differing amounts of back-up capacity in the event of a fault. This ca-pacity will depend on the traffic characteristics of the network. However, it may also depend on the protec-tion plan selecprotec-tion algorithms as well as the signaling and re-routing methods. Recovery schemes may intro-duce additive latency to traffic. For example, a recov-ery path may take many more hops than the working path. This may be dependent on the recovery path se-lection algorithms. The quality of protection: recov-ery schemes can offer a spectrum of packet surviv-ability options which may range from relative to abso-lute. Relative survivability may mean that the packet is on an equal footing with other traffic for the sur-viving network resources. Absolute survivability may mean that the survivability of the protected traffic has explicit guarantees. Recovery schemes may introduce
re-ordering of packets. Also the action of putting
traffic back on preferred paths might cause packet re-ordering. As the number of recovery paths in a protec-tion plan grows, the state overhead required to main-tain them also grows. Recovery schemes may require differing numbers of paths to maintain certain levels of coverage. The state overhead may depend on the recovery scheme. In many cases the state overhead will be in proportion to the number of recovery paths. Recovery schemes may introduce a certain amount of
packet loss during switchover to a recovery path. In
the case of link or node failure a certain packet loss is inevitable.
Recovery schemes may offer various types of failure
coverage. A recovery scheme may account for only
certain types of faults such as link faults or both node and link faults. The recovery scheme may also respond
to service degradation. For example, a scheme may require more recovery paths to take node faults into account. A recovery scheme may be able to handle
concurrent faults : depending on the layout of the
re-covery paths in the protection plan, multiple-fault sce-narios may be able to be restored. A recovery scheme can offer multiple recovery paths : for a given fault, there may be one or more recovery paths. A recov-ery scheme may offer a varying degree of coverage : depending on the recovery scheme and its implemen-tation, a certain percentage of link and node faults may be covered. Finally, a recovery scheme has a reaction
time: the number of protected paths may effect how
fast the total set of paths affected by a fault can be re-covered.
III. T
HEM
ODELConsider a communications network with N nodes and L links. The nodes represent the routers in the MPLS-capable core of a network. Some nodes are con-nected by a link. The links are directed: each link has a starting node and an ending node.
Let bidenote the capacity (bandwidth) of link i. Let
d(m,n)denote the offered traffic load that wants to
en-ter the MPLS network at node m and exit at node n. We consider only a single class of service.
A. Feasibility and Optimality
A path P is a sequence of links L1, L2, . . . , LHP
where HP ≥ 1 is the hop count of the path P . In
our terminology a route and a path and an LSP (label switched path) are synonymous. No path traverses the same link or the same node more than once. The flow deviation algorithm ensures that no paths contain cy-cles. LetP denote the set of all such non-cycling paths. LetP(i)denote the set of paths that utilize link i. Let
P(m,n)denote the set of paths from node m to node n
with m6= n.
Each path P will be assigned a bandwidth BP ≥ 0.
The goal is to select these bandwidths in an optimal way. Let B = (BP)P∈Pdenote a set of bandwidths.
B is said to be feasible if the following two constraints hold:
1. For each pair of nodes (m, n): P
P∈P(m,n)BP =
d(m,n)so that all of the offered traffic is carried.
2. For each link i: P
P∈P(i)BP ≤ bi so that no link
has an offered load greater than its capacity.
We next choose a definition of optimality. Let fi =
P
P∈P(i)BP denote the flow on link i. Let fi/bi
de-note the utilization of link i and let si= bi− fidenote
the slack on link i.
Let Fi(fi) denote a penalty function for link i when
the link carries a flow fi. The LSP design problem
is specified in terms of the following constrained non-linear optimization problem: Find a set of feasible
bandwidths Boptthat minimizes the objective function F (B) =X
i
Fi(fi) (1)
subject to the two constraints above where the sum in equation (1) is over links i with bi > 0. Boptis said
to provide an optimal solution to Eq. (1). Note that the optimal link flows fi are almost certainly unique
although the optimal bandwidths B are usually not [2].
B. The Penalty Function
Previous studies of the flow deviation algorithm [3], [4], [5] modelled each link as an M/M/1 queue with
Fi(x) = m
x bi− x
(2) where m is the average length of a packet and biis the
bandwidth of link i. The link penalty function (2) is the product of the link flow x and the average link de-lay (waiting and service both included, but propagation delay excluded).
However, in the modern Internet with TCP, and RED and all its variations, it is possible to have very highly utilized links (utilization practically one) and still low delay and low loss in the buffer: all delay is moved to the edge of the network. Equation (2) is probably no longer a suitable penalty function. Given these con-cerns, we use a link penalty function [2] with proper-ties which make it suitable for use in an objective func-tion whose minimizafunc-tion will yield routes and band-widths that correspond closely to the optimal operation of a modern internet. Our choice of penalty function is
Fi(x) = cix + ησi σ i bi− x ν (3) where link i has a bandwidth bi ≥ 0, a weight factor
σi> 0 with η > 0, ν > 1 and ci= τi− ην (σi/bi) ν+1
where τi≥ 0 denotes the propagation delay on link i.
We choose η positive but small so that if a feasible solution exists for which all flows fiare small and all
link utilizations fi/bi are low – in which case the
sys-tem is said to be uniformly lightly loaded – then the penalty function (3) will yield routes that are in agree-ment with OSPF routing where the propagation delays are the OSPF metrics of the links.
If the system is not uniformly lightly loaded then the penalty function (3) enforces a distance from the bar-rier bi. We showed [2] that a suitable choice of
param-eter values enables the penalty function (3) to perform a “lexicographic maximization” of all weighted slacks: first it maximizes the smallest weighted slack, then the next smallest, and so on.
C. The Flow Deviation Algorithm
The operation of the flow deviation algorithm [3], [4], [5] is simple. The algorithm executes in a loop where each iteration of the loop implements one step
of the algorithm. During each step the algorithm com-putes the current set of shortest (least cost) paths from all sources to all destinations. An optimal amount of flow is diverted from the current set of LSPs to the shortest paths. Those shortest paths that are not al-ready in the LSP set are added to the LSP set, the link costs are updated (the link costs have changed because the link flows have changed) and the next step of the algorithm is executed. The loop continues until flow re-distribution achieves no further reduction in the ob-jective function.
IV. A M
ODEL OFP
ROTECTIONR
ECOVERYOur protection model is subject to the TE goal of optimal use of resources (see goal (1) of section II) in the sense that when the link loads are low, the flow de-viation solution yields the OSPF routes, but when the link loads are high, the flow deviation solution selects relatively short routes so that the slack on each link is maximized. This means that goal (3) is also satisfied, since the network reliability and availability are max-imized by not overloading any one specific link. Our protection recovery model aims to facilitate restoration times that are sufficiently fast (2) and may be applica-ble for an entire end-to-end path or for segments of an end-to-end path (6). The protection model does not trigger lower layer protection switching (7) and aims to minimize the loss of data (8). Our protection recovery model works with pre-established reserved-on-demand recovery paths. Some of these paths are limited recov-ery paths only when the network is not well designed and capacitated, otherwise all of the recovery paths are equivalent recovery paths. If the network is sufficiently capacitated then the recovery paths can meet the re-source requirements of, and achieve the same perfor-mance characteristics as the working paths (10).
Our recovery model also makes use of split path
pro-tection where multiple recovery paths are allowed to
carry the traffic of a working counterpart. This is use-ful and sometimes necessary when no single recovery path can be found that can carry the entire traffic of the working path in case of a fault although this may be detrimental to the performance of the restored traffic.
A. Computing a Set of Recovery Paths
The following method was used to compute a set of traffic engineered recovery paths. LetF denote a set of network failure scenarios. The flow deviation method was first used to find a setA of optimal LSPs for the network in the absence of failures. For each failure scenario i ∈ F the flow deviation method was then used to find a setAiof optimal LSPs for the network
with failure i. The setBi =Ai\ A defines the set of
optimal recovery paths for failure i, and the setB = ∪i∈FBi defines the optimal set of recovery paths for
all the failure scenarios.
V. T
HES
IMULATIONM
ODEL This section presents a simulation model which rec-ognizes both connection-level and packet-level events. Unlike for example the NS simulator [10] which presents a faithful simulation of the network protocols, our simulator accounts for only those protocol features which influence MPLS based recovery.A. Connection-Level Arrivals and Departures
LetL denote the set of O-D pairs in the network. Let Pidenote pathset i ∈ L which is the set of LSPs that
connect the O-D pair i. LetP = ∪i∈LPi denote the
set of LSPs in the network.
Connections arrive to pathset i ∈ L individually at the instants of a Poisson stream with rate νi. The
connection holding times are exponentially distributed with parameter 1/τi. This is not essential – the
simu-lation can be modified to relax this assumption. There are two connection-level network state de-scriptors: n = (n1, . . . , nL) where ni denotes
the number of connections on pathset i and m = (m1, . . . , mP) where mi denotes the number of
con-nections on LSP i. Clearly|m| = |n|. Since no con-fusion can arise we use the notation niand miin the
place of ni(t) and mi(t) where t is the current
simula-tion time.
Connection arrivals and departures are generated as follows. Let t denote the current simulation time. Gen-erate an exponentially distributed random time Tconn
with parameter
Γ =X
i∈L
(νi+ niτi) .
A single connection-level event with due time T = t + Tconnis inserted into the event scheduler which is used
at the packet level (see below).
When the simulation gets to time T a random vari-able U is chosen uniformly distributed on [0, 1]. If there exists an I such that
I−1 X i=1 νi< ΓU < I X i=1 νi
then a connection arrives to pathset I. The arrival is modelled as follows.
• Set nI := nI + 1 so that the number of connections
on pathset I is increased by one. Any type of connec-tion admission control can be employed at this stage.
• Let J ∈ PI be the LSP in pathset I with the least
delay. Then mJ := mJ + 1 so that the number of
connections on LSP J is increased by one. Clearly P
j∈PImj= nI.
Alternatively, if there exists an I such that
X i∈L νi+ I−1 X i=1 niτi< ΓU < X i∈L νi+ I X i=1 niτi
then a connection completes on pathset I. The com-pletion is modelled as follows.
• Set nI := nI− 1 so that the number of connections
on pathset I is decreased by one.
• Assume that all connections on pathset I have the
same average holding time so that, with an abuse of notation, τi= τI for all i∈ PI.
Let Ω = P
i∈PImiτi. Choose a random variable U
uniformly distributed on [0, 1]. If there exists a J such that J−1 X i=1 miτi< ΩU < J X i=1 miτi
then a connection completes on LSP J .
Set mJ := mJ− 1 so that the number of connections
on LSP J is decreased by one. ClearlyP
j∈PImj =
nI.
B. Packet-Level Arrivals
Packets for any connection on LSP i arrive at rate λi = L/τiwhere L is the average number of packets
that each connection offers to the network.
Consider a time instant t when a packet arrival has just occurred, or when a connection arrival or departure has occurred. The next packet arrival is generated as follows. Generate an exponentially distributed random time Tpackwith parameter
Λ =X
i∈P
miλi.
When the simulation gets to time t a random variable U is chosen uniformly distributed on [0, 1]. If there exists an I such that I−1 X i=1 miλi< ΛU < I X i=1 miλi
then a packet is generated on LSP I and assigned a label j. LetJ denote the set of packet labels in the network at time t.
The arrival time aj for packet j is aj = t + Tpack
and packet j is placed in the queue at the first link kj
of LSP I. If packet j is the only packet in this queue then a completion time for packet j is generated as fol-lows. Generate an exponentially distributed random time Tserv with parameter bkj where bkj is the
band-width (packets/sec) of link kj. Packet j is scheduled to
complete at time aj+ Tservand the packet is inserted
into the scheduler.
C. Progression of Packets
At any time t there is a schedule of times with at most L + 1 entries: one entry for the next connection event and one entry for each packet completion event. The schedule is organized in chronological order. The simulator also knows (at least) the order of packets in the queues at each of the links, the delay on each of the
LSPs, the initial arrival time ajand the current link kj
of every packet j∈ J .
Suppose the first event in the schedule is a packet completion event with due time Tprog. Thus at time
Tprog say packet j at link kj completes: the departure
time for the next packet (if any) in the queue kjis
gen-erated and this packet is inserted into the scheduler. If the packet j was at the final link of its route, the end-to-end delay Tprog− ajis recorded together with other
relevant statistics and the label j is released. If packet j was not at the final link of its route, the packet selects the next link of its route (either deterministically or ac-cording to some routing probabilities) and packet j is inserted at the end of the queue at the next link. If packet j is the only packet at this queue, a new depar-ture time is generated and packet j is inserted into the scheduler.
D. Link Failure
The failure of a uni-directional link i is modelled by disabling all LSPs that use the failed link and by activating the recovery LSPs. Currently the simulator does not represent the delay in communicating the fault indication signal to the path-switch and path-merge LSRs. Packets which complete service on the failed link, and packets which are forwarded to the failed link are destroyed and all packets queued for service at the failed link are destroyed.
For each j∈ L let
Pj(i) ={P ∈ Pj | i ∈ P } (4)
denote set of paths in pathset j which use the failed link i. The number of connections on pathset j is de-creased
nj := nj−
X
P∈Pj(i)
mP.
The number of connections on each failed path is set to zero so that mP = 0 for all paths P ∈ Pj(i) and all
j∈ L.
VI. E
XPERIMENTAL RESULTSThis section presents the results of MPLS recovery in a model of a 20-node network. The results were pro-duced using an extended version [7] of the simulator presented in Sect. V. The computation was performed on a 1.8GHz AMD6 processor.
The set of recovery paths was designed to provide protection against all uni-directional single link fail-ures. We examine single link failure scenarios which are the most probable failure events since (see [8] and the references therein) a complete fibre cut is the most common and frequently reported failure event.
The 20-node network [9] has 102 bi-directional links and carries 1 traffic class. The links are capaci-tated with 5,289,780 units of bandwidth. A total of 1,344,705 units of flow are offered to the 380 origin-destination pairs. The flow deviation method finds an
0 500000 1e+06 1.5e+06 2e+06
0 1e+07 2e+07 3e+07
0 2000 4000 6000 8000 10000 packet throughput packets sent network failed pathset affected pathsets 0 10 20 30
0 1e+07 2e+07 3e+07
0 1000 2000 3000 delay packets sent failed LSP failed pathset network
Fig. 1. The 20-node network: (a) packet throughput (b) packet delay.
optimal LSP set containing 540 paths. The worst-case uni-directional single link failure will disable 40 LSPs which carry 8% of the network flow: 144 recovery paths are deployed.
The simulator requires some 10 minutes to process 100 million events which include 32 million packet transfers. An average of 45,750 connections are simul-taneously active and 47,620 packets are on average in the network, either in transmission or queued in link queues awaiting transmission. The link failure caused 3,643 connections to fail and 6,496 packets were lost.
From equation (4) pathset j is affected by the failure of link i ifPj(i)6= ∅ in which case pathset j contains
an LSP that used the failed link i. Fig. 1(a) shows the packet throughput in packets per second for the entire network (top curve, left hand scale), for the failed path-set (middle curve, right hand scale) and for the affected pathsets (bottom curve, left hand scale). The through-put is a moving average comthrough-puted over the most recent 400 connection completions. A bezier curve is fitted to the throughput of the failed pathset (middle curve) to smooth the fluctuations. The impact of the link fail-ure on the throughputs can clearly be seen. The re-covery LSPs quickly restore the throughputs to their pre-failure values.
The failed link connects an O-D pair that prior to the link failure was served by a single LSP – a direct path. After the link failed, the O-D pair is served by 4 recov-ery LSPs each of which is 4 links long. Fig. 1(b) shows the average end-to-end packet delay on the failed LSP (top curve, left hand scale), the average end-to-end packet delay of the failed pathset (middle curve, right hand scale) and the average end-to-end packet delay for the entire network (bottom curve, left hand scale). The moving averages are computed over the most re-cent 400 completing packets. The link failure has a negligible impact on the network average delay. How-ever the failure gives rise to a 25-fold increase in the average packet delay on the four recovery LSPs. In this case the recovery paths do not have the same delay
performance characteristics as the working path.
VII. C
ONCLUSIONThis paper considers the problem of protection switching in MPLS networks. A variant of the flow de-viation method was used to find and capacitate a set of optimal working LSPs. Global repair is implemented by reserving a set of pre-established recovery LSPs. A simulation model was used to evaluate the dynamics of the MPLS recovery cycle in terms of the time needed to effect the restoration of the traffic affected by a fault. The model was applied to evaluate the effectiveness of protection switching in a 20-node network.
R
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