Dynamic Restoration in Multi-layer IP/MPLS-over- Flexgrid Networks

Transcription

1 Dynami Restoration in Multi-layer IP/MPLS-over- Flexgri Networks Alberto Castro, Luis Velaso, Jaume Comellas, an Gabriel Junyent Universitat Politènia e Catalunya (UPC), Barelona, Spain aastro@a.up.eu Abstrat-The reent avanes in photoni tehnology will allow eploying flexgri-base optial ore networks in the near future. Although that tehnology favors more effiient spetrum utilization, multilayer IP/MPLS-over-flexgri networks woul still be neee to groom together lient flows, oming from aess an metro networks, into optial onnetions. In this senario, multi-flow transponers (s) will provie aitional flexibility allowing reonfiguration of optial onnetions to be performe. To be operate, a istribute ontrol plane together with a entralize Path Computation Element (PCE) oul be use. In the event of a failure, tens or hunres of lient flows oul beome isonnete an thus, restoration routes nee to be foun by the PCE for these flows. In stanar restoration, path omputation for eah lient flow is performe whih erives into resoure ontention as a result of several onnetions trying to use some ommon resoures, an poor resoure utilization as a result of the reution of grooming levels. In this paper, we eal with these problems an solve the DYNami restoration in Multi-layer IP/MPLS-over-flexgri Optial networks (DYNAMO) problem. Client flows restoration requests are groupe into a single bulk in the PCE. Afterwars, a Global Conurrent Optimization (GCO) moule solves the DYNAMO problem fining routes for all the flows in the bulk. The DYNAMO problem is moele by using mathematial programming. However, as a onsequene of its omplexity an the stringent times within whih the problem has to be solve, a GRASP-base heuristi is use. Exhaustive simulation results performe on two national ore topologies show that a PCE with a GCO moule solving DYNAMO highly improves restorability an reues remarkably the number an apaity of s, at the expense of some inrement in restoration times. 1 Keywors: Flexgri Optial Networks, Multi-layer Networks, Dynami Bulk Restoration. I. INTRODUCTION Reent avanes in optial moulation formats, filtering, an igital signal proessing, among others, are enabling flexgri omponents to be proue; finer granularity an flexibility in the use of the optial spetrum ompare to that of the fixe gri an be ahieve [1]. For flexgri-base optial ore networks to be eploye, banwith-variable optial ross-onnets (BV-OXC), equippe with banwith-variable wavelength seletive swithes, an multi-flow transponers () nee to be 1This work has been partially fune by the European Community's Seventh Framework Programme FP7/ uner grant agreement n IDEALIST projet an from the Spanish Siene Ministry through ELASTIC (TEC ) projet. ommerially available []-[4]. Installe in IP/MPLS routers an onnete to a BV-OXC, s a extra flexibility allowing several optial onnetions (lightpaths) to be terminate in eah of them. As a onsequene of the introution of flexgri networks, grooming oul be partially one at the optial layer reuing the amount of IP/MPLS routers that nee to be installe in a network [5]. However, those routers woul be still neee to fill the gap between the bitrate of IP/MPLS lient flows oming from aess an metro networks (e.g. 1 Gb/s) an the apaity of one slot lightpath (in the orer of 10 Gb/s when 6.5 GHz slot with an the quarature phase shift keying, QPSK, moulation format are use). To operate those multilayer IP/MPLS-over-flexgri networks, a istribute ontrol plane with a entralize Path Computation Element (PCE) [6] an be use. The stanarize PCE omputes routes in response to path omputation requests. It takes avantage of a traffi engineering atabase (TED) that is upate after network resoures are effetively use or release. In multilayer IP/MPLS-over-flexgri, a failure in a single fiber link may isonnet tens or hunres of lient IP/MPLS flows. In that ase, an inepenent restoration path omputation request for eah isonnete flow is reeive at the PCE. The stanar proeure (name sequential in this paper) onsists in omputing a route for eah request using the state of the network resoures store in the TED. However, better network-wie solutions an be ahieve grouping together a set of path omputation requests an performing bulk path omputation. Reently, efforts to introue enhane omputation apabilities in the PCE have onlue with the stanarization of Global Conurrent Optimization (GCO) [7]. GCO aims at serving a bulk of path requests attaining the optimal solution for the whole network. In view that the sequential approah ahieves poor resoure effiieny an suffers from ontention problems sine path omputations might be performe over a non-upate TED, we propose to take avantage of bulk path omputation in restoration senarios. To this en, we efine the DYNami restoration in Multi-layer IP/MPLS-over-flexgri Optial networks (DYNAMO) an solve it in a GCO moule, whih is alle from a entralize PCE. Few works aresse the appliation of the GCO framework. Authors in [8] present a ynami bulk provisioning framework with the objetive of optimizing the use of network resoures. PCE lients are allowe to bunle 155

2 an simultaneously sen multiple onnetion requests to the PCE where, in turn, several bunles an be onurrently proesse together as a single bulk. To the best of our knowlege, however, the only work in the literature aressing bulk restoration is [9] where the authors esribe an experimental implementation for bulk restoration in a GCO moule alle from the entralize PCE for multilayer fixe gri -base networks. Base on the lessons learne from that experimental implementation, in this paper we fous on the speifi problems that arise in IP/MPLS-over-flexgri networks. The reminer of this paper is organize as follows. Setion II esribes ynami restoration in multilayer IP/MPLS-overflexgri networks an presents two ifferent approahes: sequential an bulk. The DYNAMO problem for bulk restoration is formally state an a mathematial moel to solve it is presente in Setion III. Due to the fat that the moel is omputationally impratial when realisti problem instanes are onsiere, a heuristi algorithm able to obtain near-optimal solutions in pratial times is propose in Setion IV an use in Setion V to ompare the performane of the bulk restoration approah against that of the sequential one. Finally, Setion VI raws the main onlusions. II. DYNAMIC RESTORATION IN MULTILAYER NETWORKS For illustrative purposes, Fig. 1(a) shows a simple physial network onsisting of five BV-OXCs an four IP/MPLS routers. BV-OXCs are onnete by biiretional fiber links. Let us assume that one is installe in eah of the IP/MPLS routers an onnete to the olloate BV-OXC. Finally, two IP/MPLS lient flows are alreay being serve. We assume that the bitrate of eah flow is 1 Gb/s. Two lightpaths were establishe in the physial topology to support an equal number of virtual links in the virtual topology. The route of eah IP/MPLS flow over the virtual topology is given in the ajaent table. At this stage, let us assume that a new IP/MPLS eman between R1 an R3 nee to be serve. After requesting a route to the PCE it omputes R1-R-R3, where the existing virtual link R1-R is reuse an a new virtual link R-R3 must be reate, whih triggers the new lightpath R-R3 to be establishe. Refer to [10] for etails on virtual links set-up. Later, another IP/MPLS eman #4 between R an R3 arrives an it is serve through the route R-R3, using apaity available in virtual link R-R3. Fig. 1(b) esribes the onfiguration of both the physial an the virtual topologies one all four IP/MPLS flows have been route. Next, a failure in fiber link X1-X has triggere eah of the affete flows (flows an ) to request a restoration route to the entralize PCE. In Fig. (a) the restoration route has been ompute sequentially by the PCE an signale afterwars. Sine the TED in the PCE is only upate when the resoures have been effetively alloate in the network, the signaling of the restoration routes of both IP/MPLS flows have triggere two parallel lightpaths to be set-up so as to reate the virtual links neee to route the IP/MPLS flows. In the example, both lightpaths oul be reate beause enough resoures, i.e. frequeny slots in the links an ports in the IP/MPLS routers, were available. Frequently, nonetheless, that is not the ase an resoure ontention may arise. In that regar, note that both restoration routes reuse the virtual link R1-; again resoure ontention oul arise as a onsequene of not enough apaity being available for both IP/MPLS flows in that virtual link. In Fig. (b) the PCE has groupe all restoration requests an performe bulk route omputation. In the example, the restoration route of both IP/MPLS flows has been ompute. R1 R R3 Virtual Topology R1 R R3 R1 R R3 Virtual Topology R1 R R3 a) X1 X X3 X4 X5 Physial Topology IP/MPLS Path Route (R1-R) R1-R # (R1-) R1- a) X1 X X3 X4 X5 Physial Topology IP/MPLS Path Route (R1-R) R1--R # (R1-) R1- (R1-R3) R1--R-R3 #4 (R-R3) R-R3 R1 R R3 Virtual Topology R1 R R3 R1 R R3 Virtual Topology R1 R R3 b) X1 X X3 X4 X5 Physial Topology IP/MPLS Path Route (R1-R) R1-R # (R1-) R1- (R1-R3) R1-R-R3 #4 (R-R3) R-R3 Fig. 1. Example of multilayer network onsisting in five BV-OXCs an four IP/MPLS routers. As a result of set-up two IP/MPLS emans a virtual topology has been reate; eah virtual link is supporte by a lightpath in the physial topology (a). Four IP/MPLS emans have been set-up (b). b) X1 X X3 X4 X5 Physial Topology IP/MPLS Path Route (R1-R) R1--R # (R1-) R1- (R1-R3) R1--R-R3 #4 (R-R3) R-R3 Fig.. After a failure in link X1-X, IP/MPLS emans have been restore an the virtual topology has been reonfigure. Restoration has been one sequentially (a) an bulk (b). 156

3 a) b) # R1 # R1 # # #4 R #4 R flows. In our example, flows an nee virtual link - R to be reate. Then, one of the emans is reroute an the other one must be elaye enough to allow the virtual link -R to be effetively reate. This fat introues a set of epenenes among the emans that must be onsiere so as to minimize reovery times. To solve the bulk ynami restoration approah, the next setion first formally states the DYNAMO problem an then a mathematial programming formulation is presente. ) # R1 #,3 #4 Fig. 3. Routing of IP/MPLS eman in routers R1,, an R3 before a failure in link X1-X (a). Routing after the restoration of the flows performe sequentially (b) an bulk (). The bulk routing algorithm eies to reate virtual link - R using that for both restoration routes, thus reuing the amount of resoures use ompare to the sequential approah. However, for the bulk restoration to work restoration routes nee to be sequene: one of the routes must be signale first, so as to trigger atual virtual link reation; after waiting enough time, virtual link -R is effetively reate an the seon route reusing it an be signale. As a onsequene of the effiieny that bulk restoration reahes by reusing virtual links, the number of s ports an be reue. Fig. 3 is intene for illustrating the above. Fig. 3(a) shows how the IP/MPLS flows have been route before the failure; eah router is equippe with a number of lient ports were the IP/MPLS flows arrive. One allowing for five lightpaths to be ene is onnete to eah router. For instane, in Fig. 3(a) two lightpaths are ene in the, were flows an are groome together into a single lightpath whereas flow # uses a ifferent one. Flow is route through intermeiate Router, so that flow enters in Router by one of the lightpaths terminating in the in that router an leaves it using the same but aggregate together with flow #4 into lightpath R-R3. Fig. 3(b) an Fig. 3() show the routing at eah router after the restoration has been one sequentially an bulk, respetively. The main ifferene an be shown in routers an 4 were more resoures in eah have been use in the ase of sequential restoration as a onsequene of the inrease number of lightpaths reate. Hene, effiieny in the use of the resoures an reue notably the amount of resoures neee for the same grae of servie, thus reuing remarkably CAPEX osts when ealing with expensive resoures suh as s. As antiipate above, for the bulk restoration to work properly, ompute routes must be sequene for signaling so as to allow that new virtual links are firstly reate, an their lightpaths establishe, by one IP/MPLS eman; after that their apaity is available to be reuse by other IP/MPLS R III. THE DYNAMO PROBLEM A. Problem Statement The DYNAMO problem an be formally state as follows: Given: a network topology represente by a graph G o (N, L), being N the set of BV-OXC noes an L the set of biiretional fiber links onneting two BV-OXC noes, exluing faile ones; eah link onsists of two uniiretional optial fibers. a set S of available slots of a given spetral with for eah link in L, the virtual network represente by a graph G v (V, E), being V the subset of N where IP/MPLS routers are plae, an E the set of virtual links efining the onnetivity among the IP/MPLS noes, a set D of IP/MPLS emans to be reovere. Eah eman is efine by the tuple {s, t, b }, where s an t represent eman s soure an estination IP/MPLS routers an b its bitrate. Output: the routing in the virtual topology of every reovere eman, the routing of the new lightpaths use to serve new virtual links to be reate. Objetive: maximize the total amount of bitrate reovere whilst minimizing the amount of resoures use (i.e., slots an s) an the total reovery time. We have moele the DYNAMO problem by means of a mathematial programming formulation base on preomputing hannels to ensure spetrum ontiguity as esribe in [11]. The next subsetion presents the formulation propose. B. Mathematial Moel The mathematial programming moel for the DYNAMO problem performs routing in both the optial an the IP/MPLS layers using noe-link formulations for eah network layer [1]. A set of virtual links is pre-ompute beforehan; eah virtual link onnets two loations with IP/MPLS noes provie that a feasible optial route an be foun. A set of lightpaths is available for eah virtual link, although its atual route on the optial topology is etermine uring the resolution of the problem. 157

4 The following sets an parameters have been efine: Optial Topology: N Set of BV-OXC noes, inex n. L Set of fiber links, inex l. L(n) Subset of fiber links inients to BV-OXC noe n. len(l) Length of fiber link l (km). R Set of bitrate-reah pairs (Gb/s, km), inex r. len(r) Reah of a path using bitrate-reah pair r in km. b(r) Maximum bitrate of a path using bitrate-reah pair r. Optial Spetrum: S Set of frequeny slots, inex s. C Set of hannels, inex. Eah hannel ontains a subset of ontiguous slots. a ls Equal to 1 if slot s in fiber link l is being use. u s Equal to 1 if hannel inlues slot s. b Capaity of hannel (Gb/s). Virtual Topology: V Set of IP/MPLS routers (V N), inex v (v=n provie that BV-OXC noe with inex n is physially onnete to the IPMPLS router with inex v). E Set of virtual links, inex e. P(v) Set of s in IP/MPLS router v, inex p. K( Set of routes to support virtual link e, inex k. K 1 ( Subset of K( alreay eploye in the optial topology. K ( Subset of K( not urrently eploye in the optial topology. E(v) Subset of virtual links inient to IP/MPLS router v. N( Set of en BV-OXC noes (noes onnete to the orresponent IP/MPLS router) of virtual link e. b Available apaity in virtual link e using lightpath k (Gb/s). b pv Available apaity in p in IP/MPLS router v (Gb/s). f pv Number of lightpaths that an be assigne to MF- TP p in IP/MPLS router v. g pv Equal to 1 if virtual link e using lightpath k ens in p in IP/MPLS router v Demans to be reovere: D Set of IP/MPLS emans to be reovere, inex. SD() Set of {s, t } IP/MPLS routers of eman. b Bitrate of eman (Gb/s). The eision variables are: ω Binary. Equal to 1 if eman is route through virtual link e using lightpath k, 0 otherwise. δ Binary. Equal to 1 if lightpath k of virtual link e uses hannel, 0 otherwise. λ l Binary. Equal to 1 if lightpath k of virtual link e uses hannel in fiber link l, 0 otherwise. σ Binary. Equal to 1 if eman is reovere, 0 otherwise. γ pv Binary. Equal 1 if p in IP/MPLS noe v is alloate. ν r χ t φ t C t C max Binary. Equal 1 if lightpath k of virtual link e uses bitrate-reah pair r. Binary. Equal 1 if eman is establishe in time interval t. Binary. Equal 1 if lightpath k of virtual link e is ative in time interval t. Positive integer. Completion time of lightpath k of virtual link e. Positive integer with the total reovery time. Finally, the mathematial programming formulation for the DYNAMO problem is as follows: maximize A1 b σ A γ pv A3 C (1) max v V p P subjet to: ω = σ D, v SD( ) ( () ω D, v SD( ) ( (3) ωe' k ω D, v SD( ), e E( v) e' E ( e' ) e' e b b ω ω l L( n) l L( n) l' L( n) l' l l L b ( b l λ = δ e E, k K 1( e ) δ e E, k K ( e E, k K (, n N( l λ e E, k K (, n N( l' l λ λ e E, k K (, n N(, l L( n) (9) l λ L δ e E, k K(, C (10) δ 1 e E, k K ( e ) (11) ( ( (4) (5) (6) (7) (8) l s ls λ u + a 1 l L, s S (1) δ g f γ v V, p P( v) (13) ( pv pv pv ω b g b v V, p P( v) (14) pv pv r b ω b( r) ν e E, k K ( (15) l L r ν r R ω t' T t' t χ r R l r len( l) λ len( r) ν e E, k K ( (16) r R 1 e E, k K( χ t' t χ ϕ 1+ D ϕ ' e E, k K (, t T t = ω t' t T ( t' T t' < t t e E, k K (, t T D (17) (18) (19) (0) 158

5 C ( 1 ) T ϕ 1 e E, k K ( e t δ ) = t T (1) C, ( max C e E k K () The objetive funtion (1) maximizes the total bitrate reovere, whilst minimizing the use of s an the total restoration time. Constraints ()-(4) ompute the route an perform aggregation of emans through the virtual topology. Constraints (5)-(6) allow the emans to restore for using existing virtual links or new ones, in whih ase new lightpaths nee to be reate. Constraints (7)-(1) ompute both route an hannel assignment over the optial topology for those lightpaths supporting new virtual links. Constraints (13)-(14) ensure that s apaity is not exeee. Constraints (15)-(17) take are of bitrate-reah pair seletion. Finally, onstraints (18)-() perform eman sequening assigning eman establishing, an so new virtual links, to time intervals. Note that onstraint (18) entails multiplying two binary variables thus onverting the mathematial moel into a nonlinear one. Notwithstaning, variable multipliation an be easily solve as the expense of introuing aitional binary variables an onstraints. Even though, the DYNAMO problem an be onsiere NP-har sine simpler multilayer network problems have been prove to be NP-har (e.g. [13]); hene its exat solving beomes impratial for the stringent times require for restoration an, as a result, an heuristi algorithm is neee so as to provie goo near optimal solutions in the time perios require for reovering. Table I esribes the propose greey ranomize onstrutive algorithm. Equation (3) is use to quantify the quality of reovering a given eman, in line with the objetive funtion for the mathematial moel. A restrite aniate list (RCL) ontaining those emans with the best quality is use. Parameter α in the real interval [0,1] etermines the size of RCL. q = A bw A. newresoures A. (3) ( ) epen 1. 3 During the loal searh, the route of the emans is hange so as to try to avoi new virtual links to be reate. The heuristi was valiate, for really small instanes, against the mathematial formulation; in all the instanes heke, the heuristi provie the optimal solution, i.e. the same solution than the one obtaine from solving the mathematial moel with CPLEX [14]. In light of this, the heuristi was use to obtain the results presente next. V. ILLUSTRATIVE NUMERICAL RESULTS The performane of the onsiere restoration approahes was ompare on two national network topologies: the 1- noe Spanish Telefónia (TEL) an the 1-noe Deutshe Telom (DT) topologies (Fig. 4) where eah loation ontaine one IP/MPLS router an one BV-OXC. Evaluation of the restoration approahes was performe by using the simulation algorithm presente in Table II. To loa the network (line ), we evelope an a-ho event-riven simulator in OMNET++ [15]; a ynami network environment was simulate where inoming IP/MPLS IV. HEURISTIC ALGORITHM In this setion we propose a GRASP-base heuristi to solve the DYNAMO problem. In general, the GRASP metaheuristi onsists of two main phases: in the onstrutive phase, a greey ranomize onstrution proeure is use to buil a feasible solution; in the loal searh phase the solution built in the first phase is improve until a loal optimum is foun [13]. 1 TEL DT TABLE I GREEDY RANDOMIZED CONSTRUCTIVE ALGORITHM INPUT G o (N, L), G V (V, E), D, α OUTPUT Sol 1: Sol Ø; Q D : while Q Ø o 3: for eah Q o 4:.route = shortestpath(g V, ) 5: if.route = Ø then q() = -INF 6: else evaluate the quality q() using eq. (3) 7: q min min{q() : Q} ; q max max{q() : Q} 8: if q max = -INF then break 9: RCL { Q : q() q max -α(q max - q min )} 10: Selet an element from RCL at ranom 11: for eah e.route o 1: if not e.isimplemente then implement(g o, 13: implement(g V, ) 14: Q Q \ {} ; Sol Sol U {} 15: return Sol Fig. 4. Sample network topologies use in this paper: 1-noe Spanish Telefónia (left) an 1-noe Deutshe Telom (right). 1: : 3: 4: 5: 6: 7: 8: 9: 10: 11: 1: TABLE II SIMULATION ALGORITHM for i=1..10 o Loa the network to the esire level store the state of the network for eah l L o ut l perform sequential restoration repair l restore network state ut l perform bulk restoration repair l restore network state

6 TABLE III BITRATE-REACH PAIRS Bitrate (Gb/s) Max reah (km) ,000 40,000 10,500 onnetion requests arrive to the system following a Poisson proess an are sequentially serve without prior knowlege of future inoming onnetion requests. To ompute the routing an spetrum alloation of the lightpaths, we use the algorithm esribe in [16]. The holing time of the onnetion requests is exponentially istribute with a mean value equal to hours. Soure/estination pairs are ranomly hosen with equal probability (uniform istribution) among all IP/MPLS noes. Different values of the offere network loa are reate by hanging the inter-arrival rate while keeping the mean holing time onstant. Finally, note that eah point in the results is the average of 10* L runs an that sequential an bulk restoration approahes are exeute using iential input ata. In our experiments, the bitrate of eah IP/MPLS flow was set to 1 Gb/s, the QPSK moulation format was use for the optial signals, the optial spetrum with was set to 1 THz, the slot with was fixe to 6.5GHz, an eah IP/MPLS router was equippe with one, whose apaity for terminating lightpaths range from to 5. Regaring bitratereah pairs, we use the values reproue in Table III. 60% 50% TEL Sequential To fin the appropriate loas, we first run the simulator without utting links an store the resulting bloking probabilities. Five traffi loas unleashing bloking probabilities ranging from 0.1% to 5% for eah of the networks an apaities onsiere were foun. Fig. 5 presents the perentage of un-restorability of IP/MPLS flows as a funtion of the amount of flows to restore for the TEL an DT networks. Five plots for both sequential an bulk restoration approahes are presente, one for eah of the foun traffi loas, where eah point orrespons to one apaity value. As antiipate, the sequential approah proues un-restorability values as high as 7% to above 50% as a funtion of the traffi loa. In ontrast, the bulk approah ahieves un-restorability values of almost 0%, i.e. virtually all IP/MPLS flows are restore for even the most stringent traffi loa. The behavior is the same for the TEL network as for the DT, as shown in Fig. 5. Table IV gives insight on the results for the TEL network using with apaity for 5 lightpaths. There, the amount of IP/MPLS flows to be restore ranges, on average, from 37 to 46 as a funtion of the loa offere to the network. Un-restorability values are given for both, the sequential an the bulk approah. Two main auses behin unrestore flows are etaile: i) no route oul be foun uring path omputation; an ii) resoure ontention, i.e. resoures where alreay in use uring the signaling phase. The latter gets together frequeny slots an existing virtual links that were 60% 50% DT Sequential Un-restorability (%) 40% 30% 0% lightpaths 3 lightpaths 4 lightpaths 5 lightpaths Un-restorability (%) 40% 30% 0% lightpaths 3 lightpaths 5 lightpaths 4 lightpaths 10% 10% Bulk Bulk 0% IP/MPLS flows to restore (#) 0% IP/MPLS flows to restore (#) Fig. 5. Unrestore IP/MPLS flows against the total amount of flows to restore for TEL (left) an DT (right) networks. Offere Loa TABLE IV RESTORATION RESULTS FOR THE TEL NETWORK USING 5-LIGHTPATH S # flows to restore Total Un-restorability Sequential Path Computation Resoure ontention Total Un-restorability Bulk Path Computation Resoure ontention % 0.00% 47.7% 0.0% 0.0% 0.00% % 0.00% 50.10% 0.13% 0.13% 0.00% % 0.00% 49.11% 0.13% 0.13% 0.00% % 0.00% 51.95% 0.1% 0.1% 0.00% % 0.00% 53.1% 0.56% 0.56% 0.00% 160

7 available in the TED when the route was ompute, or MF- TPs resoures that are atually alloate uring lightpaths set-up. As etaile, the reason for the high un-restorability of the sequential approah is resoure ontention; restoration routes are ompute using the state of the resoures in the TED, however, as a result of the number of path omputation requests arriving at the PCE, the TED beomes immeiately outate an thus both, the same resoures oul be assigne to several routes, an ports availability ereases notably so no new lightpaths oul be establishe. The bulk restoration approah, in ontrast, reahes negligible un-restorability values sine network resoures are globally optimize. One, restoration routes are ompute for a bulk of requests, resoure ontention isappears ompletely. Fig. 6 explores the auses of un-restorability for the sequential restoration approah when the TEL network was loae with the intensity unleashing 1% bloking probability. As shown, when the apaity of the s is low, the perentage of resoure ontention as a onsequene of the lak of the resoures in s is the ominant ause of unrestorability. However, as soon as higher apaity s are use, the main ause rapily hanges to ontention in the Un-restorability ause (%) Frequeny Slot Existing Virtual Link resoures s apaity (lighpaths) Fig. 6. Un-restorability as a onsequene of resoure ontention for the sequential approah in the TEL network. use of existing virtual links apaity. Although not inlue in the paper as a result of lak of spae, the same results an onlusions are vali for any other loa on both TEL an DT networks. The avantages of bulk restoration ome at the ost of inreasing restoration times when ompare to that of the sequential approah. That is partiularly notieable for high traffi loas where large number of IP/MPLS flows nee to be restore as illustrate in Fig. 7. Plots for maximum bulk restoration omputation times for eah network an apaity of the installe s are presente; an almost linear tren with the amount of flows to restore an be observe. Those omputation times translate into restoration times that inlue both, sequening restoration route signaling to allow new virtual links to be reate prior being reuse, an atual signaling. Thus, the time to restore an IP/MPLS flow epens on the epth of the epenenes list for that flow an the length of the route to be signale. As esribe in the DYNAMO mathematial moel above, epenene epth nees to be minimize so as to minimize restoration times in bulk restoration, an as suh, it was inlue in the heuristi algorithm. Table V presents the epenene epth values (max, min an averag as a funtion of the offere loa. As shown, the maximum value is TABLE V BULK COMPUTATION FOR THE TEL NETWORK USING 5- LIGHTPATH S Offere Loa Depenenes Avg Max Min Max. Computation Time (ms) % 1.00 Bulk omputation Time (ms) lightpaths 5 lightpaths 4 lightpaths 3 lightpaths TEL DT IP/MPLS flows (%) 60% 50% 40% 30% 0% 10% lightpaths 5 lightpaths 1 ( lightpaths) 1 (5 lightpaths) Cumulative istribution IP/MPLS flows to restore (#) Fig. 7. Maximum bulk restoration omputation times against the number of IP/MPLS flows to restore. Lines plot results using s with the same apaity. 0% Restoration Time (ms) Fig. 8. Restoration times istribution when IP/MPLS routers are equippe with s with apaity for ening an 5 lightpaths. Cumulative istributions are also plotte

8 only 5, whih in turn introues a onsierable elay for the last set of restoration routes to be signale. The histograms in Fig. 8 represent the restoration times istribution when the TEL network was loae with the meium intensity. Cumulative istributions are also plotte. Signaling times omputation were performe using equations an experimental times esribe in [17]. The main onlusion is that restoration times lower than 1s an be ahieve even when the number of IP/MPLS flows to be restore is as high as 50; more than 50% of them being restore in less than 500ms. VI. CONCLUSIONS This work takle the problem of restoration in multilayer IP/MPLS-over-flexgri network. The stanarize sequential restoration approah omputes restoration paths iniviually for eah of the restoration path requests. As a onsequene, poor restorability beause of low new virtual link grooming levels an really high resoure ontention are attaine. In view of the above an taking avantage of the reently stanarize Global Conurrent Optimization (GCO) allowing for bulk path omputation, the bulk restoration approah in multilayer IP/MPLS-over-flexgri networks was propose in this paper. To this en, the DYNami restoration in Multi-layer IP/MPLS-over-flexgri Optial networks (DYNAMO) problem was state an a mathematial moel was evelope. However, for the stringent times neee in on-line restoration senarios, a heuristi algorithm that provies near-optimal solutions with omputation times lower than one seon was propose. The fous of that heuristi was in obtaining the highest effetiveness in terms of the objetive funtion (maximize restorability, minimizing the amount of resoures use an epenene epth). In that regar, it is worth mentioning that more effiient algorithms in terms of omputation times an be evise. The performane of both approahes was extensively assesse on two national network topologies, using an a-ho network simulator. The results obtaine showe that the sequential restoration approah provies poor restorability even for low traffi loas. In ontrast, the bulk restoration approah is able to restore almost all the isonnete IP/MPLS flows. The main auses of the high un-restorability of the sequential approah were stuie resulting in ontention in the use of apaity in existing virtual links an resoure availability in s. The inrease restoration times were ientifie as the main isavantage of the bulk restoration. Two main auses for those longer times were: i) longer omputation times, an ii) epenene epth. The first ause an be notably improve using heuristi frameworks suh as those use in [16] an omputation times remarkably shorter an be ahieve. However, the seon ause is the really limiting fator for restoration times, sine they involve long waiting times. There, several approahes an be evise suh as using restoration lasses to give priority to some flows. Notwithstaning, sub-seon restoration times were ahieve even for high traffi loas, where as many as 50 restoration paths were ompute an signale. REFERENCES [1] M. Jinno, H. Takara, B. Koziki, Y. Tsukishima, Y. Sone, S. Matsuoka, Spetrum-effiient an salable elasti optial path network: arhiteture, benefits, an enabling tehnologies, IEEE Comm. Mag. vol. 47, pp , 009. [] O. Gerstel, M. Jinno, A. Lor, S. Ben Yoo, Elasti Optial Networking: A New Dawn for the Optial Layer? IEEE Commun Mag., vol. 50, pp. s1-s0, 01. [3] O. Perola, A. Castro, L. Velaso, M. Ruiz, J. P. Fernánez-Palaios, D. Careglio, CAPEX stuy for Multilayer IP/MPLS over Flexgri Optial Network, IEEE/OSA J. Opt. Comm. an Netw. (JOCN), vol. 4, pp , 01. [4] M. Jinno, H. Takara, Y. Sone, K. Yonenaga, A. Hirano, Multiflow Optial Transponer for Effiient Multilayer Optial Networking, IEEE Commun Mag., vol. 50, pp , 01. [5] L. Velaso, P. Wright, A. Lor, an G. 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