IN transport communication networks two rather diverse

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1 apacity-efficient Planning of Resilient Networks with p-ycles laus G. Gruber, ominic. Schupke fclaus.gruber, Munich University of Technology Institute of ommunication Networks Munich, Germany bstract The p-ycle concept is an efficient way to protect the network traffic against failures of network elements. We present the methods and issues related to network planning, management and operations of p-ycles and highlighttheadvantages usingthis concept. urthermore we report efficiency results of a case-study of the deployment of p-ycles in Wavelength ivison Multiplex (WM) networks. I. INTROUTION IN transport communication networks two rather diverse restoration concepts were developed and implemented in the last years: Ring-based and mesh-based restoration. Ring concepts such as the bi-directional lineswitched ring (LSR) provide cyclic structures to protect the network against failures of network elements. The switching mechanisms of ring are quite simple. Restoration times of ms can be achieved []. However, the ring concepts need at least 00% capacity redundancy for the primary, non-preemtible traffic. ecause of scalability problems and ring-constrained routing % redundancy can be observed in real networks. In mesh restoration networks the spare capacity is much more accessible and reusable in the network. The required spare to working resources ratio can achieve values of 50-70% []. However, due to the more complex distributed signalling the restoration speeds of ring-concepts can hardly be reached. urthermore in SONT/SH networks mesh restoration is traditionally centralized and needs an interaction with a central management system. This involves slow restoration times and all problems of a centralized system (e.g. the single point of failure issue). In 998 Grover and Stamatelakis introduced the concept of p-ycles that unites the advantages of both schemes - the fast recovery speed of rings with the capacity efficiency of mesh restorable networks [2], [3]. The p-ycle concept can be applied to a great variety of network types such as WM, IP, MPLS, MP S, SH, SONT, SON and STN networks. In this paper we summarize the general concept of p-ycles. We present the methods and issues related to network planning, management and operations of p-ycles and highlight the advantages using the p-ycle concept. urthermore we report efficiency results of a case-study of the deployment of p-ycles in Wavelength ivison Multiplex (WM) networks..g. Gruber is supported by the German ederal Ministry of ducation and Research and Siemens G, project KING... Schupke is supported by the German ederal Ministry of ducation and Research, project TransiNet. The next section gives an overview of the general concept of p-ycles. Section III describes a method of combining different p-ycles to protect a whole network. Section IV gives an overview of the procedures to obtain an optimal set of p-cycles. Section V deals with possible alternatives to reconfigure a network. In Section VI we summarize the pros and cons of the concept. Then we present results of a case study considering the deployment of p-ycles in WM networks and conclude the paper. II. TH p-yl ONPT Similar to LSR the p-ycle concept is based on cyclic structures, but unlike LSR p-ycles have higher coverage. Spare capacity is reserved along a cycle and can be shared to protect several elements of a network (:N ). ctions from two nodes at the endpoints of the failure are sufficient to detour the traffic around the failure using the pre-configured capacities given through the cycle. (c) X ig.. network with one link p-ycle that protects on-cycle links (by one path, ) and straddling links (by two paths, (c) and (d)) (d) X X

2 There are two basic types of p-ycles. Link p-ycles are able to protect the working capacity of a link. Node-encircling p-ycles protect all traversing paths through a single node. igure shows an example of a network with one link p- ycle. The p-ycle is able to protect on-cycle links as shown in igure (Links -, -, -, -, -). dditionally, the same p-ycle is able to protect straddling links. straddling link is a link whose endpoints are on-cycle nodes of one p-ycle but does not belong to the cycle itself (Links -, -). The working capacity of a straddling link can be protected by the two alternative paths provided by the p-ycle (igure (c),(d)). This characteristic is one of the main advantages of the p-ycle concept. The working capacity of a straddling link can be divided into two parts and the required detour-capacity along the cycle can be halved. The p-ycle of igure is able to protect 5 on-cycle and 2 straddling links. The additional of straddling links provides much more working capacity coverage by p-ycle compared to pure ring-based schemes. G ig. 2. network with one node p-ycle protecting node G. The traffic is detoured on two paths aroung the failed node until it reaches the outgoing node igure 2 shows an example of a node p-ycle. fter detecting a failure of node G neighbor node detours the traffic that originally would traverse the failed node G to the preconfigurednode cycle. The detoured traffic visits the neighbor nodes of the failed node until it reaches its outgoing node [4]. The p-ycles are embedded in a mesh topology. p-ycles can be installed as a fixed cycle infrastructure in the network (similar to SH/Sonet rings) or as virtual cycles, which we consider in the following. p-ycles are formed in the spare capacity of the network. reconfiguration of the cycles can be done without affecting the working traffic. ecause of this, the cycles can be assigned freely and flexibly in the network. epending on the network type, different assignments for p- ycles are necessary. or instance, in WM networks capacity, i.e. wavelengths, must be reserved. or the switching, a local action of, e.g., a pair of micro-mirrorswitches in the neighbor node are sufficient to detour the traffic to its p-ycle. In IP-networks the capacity of IP p-ycles need not to be reserved explicitly. In MPLS, for example, additional GX labels can be used to define a p-ycle that are used as a route around a failure. III. TH OMINTION O p-yls To protect working traffic at 00%, the reserved capacity on the cycle links must equal the maximum of the highest on-cycle capacity and the highest portion (e.g. /2) of the straddling capacity. n example is given in igure 3. When using only one p-ycle to protect the demand pattern of igure 3, a cycle must be found that is able to detour the highest traffic demand, i.e. protecting a failure of link - (igure 3). reduction of reserved resources can be achieved by using two different p-ycles each protecting a fraction of the traffic demand of the links (igure 3(c)). If the link - fails, its entire traffic can be detoured around the failure using two different cycles. dditionally, due to the straddling, link - is protected by 2*6+4 = 6 capacity units. 0 6 (c) 6 4 ig. 3. Protection of a network with demand with one p-ycle (reserved capacity = 4*0 = 40) and with a combination of two p-ycles (c) (reserved capacity = 4*6 + 3*4 = 36) It is obvious, that the possible number of straddling links of a p-ycle will increase by its length. In fact, longer p-ycles are accessible to more links than shorter ones [5]. However, they do require a reservation of the highest capacity amount on each of its links. or that reason, a combination of several smaller cycles each protecting a part of the network or even capacity fractions of protected links may reduce the total sum of reserved capacity. or efficient planning, an optimal set of p- ycles can be computed. IV. OTINING N OPTIML ST O p-yls setofp-ycles must be found that minimizes the used reservation capacity costs and protects all considered network elements. igures 4 to 6 outline the general high-level process 6 0

3 of this optimization that is part of network planning and operations. To find an optimal set of cycles all potential cycles of the network must be known. possible cycle search process is shown in igure 4. The cycles of a network (or graph) can easily be found by applying well studied algorithms like readth irst Search (S) or Johnson s algorithm [6] to a given topology. The number of cycles of a graph can be rather large [7] and the computation times of these algorithms can be long. However, this calculation needs to be performed only once for a given network topology. dditional constraints for the cycle search like the maximum allowed physical length or hop-count can decrease the number of found cycles and reduce the size of the optimization, which can yield sub-optimal results. However, these constraints are also required when considering the longer path lengths during the detouring along a cycle, in order to restrict delay and signal degradation. ycle Reduction links/nodes removed ig. 4. ycle Search ycle atabase ycle onstraints (e.g. length) ycle determination process links/nodes added If links or nodes are removed from the topology (e.g. deinstallation), cycles using these links or nodes have to be taken out of the database. fter this, a p-ycle reconfiguration can be necessary. n addition of links or nodes can help to improve the capacity consumption and thus the p-ycles (to be selected) can be recalculated. lthough a combined routing and p-ycle optimization may increase the efficiency of the network, the two processes can be separated from each other. This allows the network operator to use a flexible routing and the algorithm of his or her choice that meets his or her requirements. igure 5 shows that the demand routing of the process is based on the network topology, the capacity on the network links, the demand matrix and additional routing constraints of the network operator. The algorithm must be processed at the installation of a new network, if a rerouting of the network traffic is desired and if new demand(s) occur. In particular for new networks a global routing can be performed, whereas for new demands paths can be added without changing the present paths ( incremental ). The results of the routing process, i.e. the paths and the capacity of the working traffic, are assigned to the network. ased on this information the optimal set of cycles can be found using the process shown in igure 6. emands new demand emand Routing (global or incremental) Ressource llocation (working capacity) ig. 5. apacity on links Routing of the demands onstraints The cycles are assigned (virtually emedded) in the remaining spare capacity of the network. This spare capacity results via reducing the total network capacity by the already assigned working capacity of the demand traffic. set of cycles is chosen from all possible cycles of the network (obtained by the cycle search algorithm, igure 4) that is based on the known topology. The topology and the capacity along these cycles must be adjusted to be able to protect the working traffic in case of an element failure. The optimization problem can be formulated as an Integer Linear Problem (ILP) as shown in [], [8] and solved in a centralized way. lternatively it can be solved using distributed techniques as shown in [2]. fter this optimization process, the optimized set of cycles can be allocated and associated to their appropriate working paths. ach working capacity is protected by at least one p-ycle. V. RONIGURTION O TH NTWORK p-ycles are able to protect the network against one failure within a cycle domain only [9]. of two simultaneously failed elements by a single p-ycle is not possible. If there is no additional unused p-ycle that is able to protect the second failing element, the network will be vulnerable until the initial failure is repaired. igure 6 shows a possible reconfiguration process (dasheddotted lines). If an element of the configured network fails, the neighbor nodes switch the affected working traffic to the p- ycles. The traffic is detoured around the failure. fter repair of the failure the working traffic can be reverted back to the original paths. uring the failure, however, the elements that were originally protected by the cycles in use are not protected. fast readjustment of the p-ycles can temporarily protect these paths. To readjust the set of p-ycles, either a global or an incremental optimization can be performed. In the case of a global optimization the whole network topology is readjusted. The failing element itself as well as cycles that include the failing element must be erased from the topology and the cycle-database (dashed-dotted in igure 4). l-

4 dapted Spare apacity total - (working + ) deassignment (unprotected elements) ycle atabase dapted ycle atabase of the unprotected elements Rerouting of the affected demands Optimization Optimized Set of ycles Spare apacity (total - working) onstraints of all demands of the new demand dapted Spare apacity (total - working) dapted Spare apacity total - (working + ) no dapted global incremental Reconfiguration? Reconfiguration Protection Switching in the network (automatic) ig. 6. failure in the network Ressource allocation () working to assignment yes new incremental routed demand Process to find an optimal set of p-ycles global optimisation no Present p-ycles reconfiguration? lready protectable? fix present p-ycles though a rerouting of a demand is not necessary, the detoured traffic-paths along the cycles may be rather long compared to a direct routing and may be rerouted. Given the updated information, a new optimal set of p-ycles can be found using the optimation methods described in Section IV. The optimization of the whole network may take long. However, almost all elements are still protected by the remaining p- ycles that are not used for. The only elements that are no longer protected are the elements that were previously protected by the now used cycles. dditionally, network elements along the used cycles carry more demand capacity compared to the optimized failure free network. t the beginning of the incremental optimization process the traffic on the elements that are no longer protected as well as the unprotected traffic along the used p-ycles must be identified and deallocated from the cycles. Only this traffic must be protected by additional cycles. Since the remaining traffic needs not to be reoptimized, the spare capacity, i.e. the free capacity of the network, can now be recalculated as the total network capacity diminished by the remaining fixed working and capacity. The network topology, reduced by the failing element as well as the cycle database, reduced by all cycles that traverse the failing element, can be used as input for the following optimization. This process yields additional cycles that protect the traffic on previously unprotected network elements. The reduced number of elements allows faster recalculation times for the optimization process but the results may not be as capacity efficient as the results from the global reconfiguration process. Similar to the failure scenario, a change of the demand matrix and a reconfiguration of the network can either be done globally or incrementally (right part of igure 6). Using global rerouting, all demands are rerouted according to igure 5. With the updated spare capacity information a new optimal set of cycles following Section IV can be obtained. When using incremental rerouting, only the new demand is routed in the network. The other demand routes remain fixed. If the resulting working traffic on the paths is not already protectable by assigned cycles, new p-ycles must be found by the

5 optimization process. If a global reoptimization of the used p-ycles is desired, a new set of cycles must be found that protects all demands. Should the present p-ycles remain fixed, the only unprotected traffic results from the new demand. dditional cycles must be found protecting the new routed traffic. or that reason the spare capacity is set to the total capacity of the network reduced by the remaining protected working and capacity. The optimization process configured with this spare capacity yields additional cycles that protect the new demand. In general, due to the decreased complexity and amount of traffic that should be protected, an incremental optimization is faster but may yield lower capacity efficiencies compared to a global optimization process. WP WM-network consists of nodes that are not able to perform any wavelength conversion. Thus the entire path as well as the p-ycle must have the same wavelength. If a lightpath becomes too long, it can be degraded too much or does not meet the delay requirements anymore. Thus, in this simulation we survey the capacity efficiency and calculation times of the p-ycle concept regarding a restriction of the allowed p-ycle lengths. In the study we optimize the pan-european OST-239 network with nodes and 26 links (igure 7) []. Throughout the simulation we use two bi-directional fiber-pairs per link. The number of wavelengths in a fiber is 28. VI. PROS N ONS O TH p-yl ONPT The p-ycle concept is a very fast and capacity efficient way to protect a network against element failures. The capacity efficiencies of mesh-restorable networks can be reached. urthermore local actions of neighbor nodes of the failing element are sufficient to detour the traffic around the failure and no complex signaling is needed in case of a failure. ue to the simple mechanisms low-cost nodal elements are possible and the fast restoration times of ring concepts can be reached []. The cycles are virtually embedded in a mesh network. There is no fixed association of the capacities with the network demand traffic like it is in known ringconcepts. If necessary, an easy reconfiguration of the cycles can be done using a network management system. lthough the capacity efficiency can further be increased by a combined routing and process, it is possible to separate them from each other. The flexible and adaptive method of the p-ycle process is then independent of the used routing methods giving the full freedom to choose the desired routing method. urthermore the failure-affected traffic is detoured along predictable and well-defined paths. differentiation of quality of service and recovery classes [0] can be done using different cycles or even different recovery concepts for each class. s with ring and mesh concepts the path-length must be considered. ue to the cyclic structure, the route around the failure can become long in comparison to the original failure-free route. The p-ycle concept may be better in more connected networks where the number of candidate cycles is higher. ig. 7. of the node core pan-european test network OST 239. istances in km The demand is based on [] where the traffic matrix is adjusted to a usable amount of lightpath demands (yielding a load of 348 lightpath demands). emands are routed on the shortest path, in the case of routing with equal link metrics, and with routing2 we use metrics reciprocal to the number of free wavelengths of the link that are recalculated each time a demand has been routed. link with no remaining available capacity will be removed for the routing process. The p-ycle optimization was formulated in MPL [2] and solved by PLX [3]. The optimization process minimizes the number of required spare wavelengths. Obviously, the best p-ycle candidates can be chosen, if there are no restrictions to the set of possible cycles. VII. PLOYMNT O p-yls IN WM NTWORKS In this section we present solutions from a case study considering the deployment of p-ycles in WM-networks [8]. We consider virtual wavelength path (VWP) and wavelength path (WP) networks. VWP WM-network consists of nodes that are able to perform full wavelength conversion, i.e. a route can disregard the wavelength.. apacity fficiency igure 8 shows the wavelength efficiency ratio capacity to working capacity over the maximum allowed cycle length for the scaled demand pattern that is routed according to the two routing alternatives. or cycle lengths greater than 3000 km feasible solutions can be obtained for the unscaled demand. ven efficiency ratios of

6 spare capacity to working capacity ratio demand_, routing_ 0 * demand_, routing_ 0 * demand_, routing_2 4 * demand_, routing_ infinity maximum allowed p-cycle length in km ig. 8. VWP case: the efficiency ratio over the allowed maximum physical p-ycle length for the scaled patterns. less than 60% can be reached allowing cycle-lengths greater than 4500 km. This efficiency improves in the situation where more traffic is routed in the network through a scaling by 0 of the pattern. n explanation of this behavior can be seen in igure 9. scaling of the demand pattern by 0 may yield better spare to working ratios ig. 9. scaling of the demand pattern may yield better spare to working ratios: 7/7 = 00%, compared to the scaled demand pattern 60/70 = 86%. 0 lthough feasible solutions are available for cycle lengths greater 4000 km only, the capacity needs between 45% to 59% as much as working capacity. significant gain can be reached by using routing2. The routed demand is distributed more evenly in the network, as it is the case for the scaled pattern. ongested links, links with much more demand capacity in comparison to their neighbor links, can be prevented and cycles with less capacities are sufficient to detour the traffic. The network capacity limit is achieved through a scaling by 4 of the demand pattern. However, feasible solutions can still be achieved using routing2 with a cycle-length restriction greater than 5500 km. Working demands use 6% of the network capacity and the capacity takes not more than 36% of the working capacity. Similar results on capacity efficiencies can be found in [], [2]. lthough the OST 239 network has 7062 different elementary cycles, the set of p-ycles used by the optimization process is only between 7 and 26 for routing. The overall spare to working capacity ratio of the Wavelength Path (WP) network is given in igure 0. The wavelengths are routed by routing and assigned first by order of the fiber and then by the first fit principle. The behavior of the efficiency ratio is similar to the VWP case (ig. 8). In a WP network, however, a p-ycle must have the same wavelength along its entire cycle. No wavelength conversion is possible at any node in case of a failure. Thus, a p-ycle has to have a color. The cycle is only accesible for lightpaths with the same wavelength as the cycle. This characteristic adds additional constraints to the optimization process [8]. Thus the WP case performs slightly less efficient. fficiency ratios lower 73% can still be reached by restricting the maximum allowed p-ycle length to values greater 5000 km. spare capacity to working capacity ratio demand_, routing_ 2.5 * demand_, routing_ 5.0 * demand_, routing_ infinity maximum allowed p-cycle length in km ig. 0. WP case: the efficiency ratio over the allowed maximum physical p-ycle length for the scaled patterns.. omputation Times igure shows the computation times of the optimization process. The long lasting cycle search algorithms must be performed only once for any given network. This computation can be done off-line. or VWP the MPL/PLX optimization times are very fast and are in the order of seconds for our testnetwork. n optimization and adaption of the chosen set of cycles can be done in a very short time. This offers the possibility to add, drop or reroute traffic in the network with a following on-line optimization or adaption process of p-ycles. VIII. ONLUSION The p-ycle concept represents an attractive solution for the of a large variety of networks: oth fast restoration times, and high capacity efficiency can be achieved while the planning and management process can be kept simple.

7 computation time in seconds Preparation ycle search MPL processing PLX infinity maximum allowed p-cycle length in km ig.. omputation times for preparation (graph construction and routing) for the cycle search, for the MPL preprocessing (data generation and processing) and for the execution of the solver PLX in the VWP case with 0 * demand and routing [6] onald. Johnson, inding all the elementary circuits of a directed graph, SIM J. omput., vol. 4, no., pp , 975. [7] H. lt, U. uchs, and K. Kriegel, On the Number of Simple ycles in Planar Graphs, in Springer Lecture Notes in omputer Science and 335, erlin, 997, Proceedings, Workshop on Graph-Theoretic oncepts in omputer Science - WG 97, pp [8] ominic. Schupke, laus G. Gruber, and chim utenrieth, Optimal onfiguration of p-ycles in WM Networks, in Proc. of the I I 2002, New York, [9] ominic. Schupke, chim utenrieth, and Thomas ischer, Survivability of Multiple iber uct ailures, in Proc.of RN Workshop, 200. [0] chim utenrieth and ndreas Kitrstaedter, R-QoS The Integrated Provisioning of Resilience and QoS in MPLS-based Networks, in Proc. of the I I 2002, New York, 200. [] P. atchelor et al., Ultra high capacity optical transmission networks: inal report of action OST 239, [2] R. ourer,. M. Gay, and. W. Kernighan, MPL: Modeling Language for Mathematical Programming, oyd & raser Publishing o., anvers, M, US, 993. [3] PLX 6.6.0, In this paper we described the functionality of p-ycles protecting nodes and links. n optimal configuration of p-ycles can be obtained by off-line methods for the green-field planning case and for the in-the-loop management of topology and demand changes or reconfigurations. However, automated plug-and-play like methods using decentralized configuration instances are also possible. p-ycles involve advantages for the operations of the networks. Since p-ycles adapt to a given demand routing, paths selected for working demands can be chosen independently from the mechanism. ny (re-)configuration of the capacity for the p-ycles does not affect the (active) working paths. We further presented a case study that shows high efficiency values of link p-ycles in WM-networks. One specific issue becomes the lengths of the p-ycles. The paths can become long. Improvements are possible by using heuristic methods instead of an integer linear optimization for the off-line computation and are for further study. RRNS [] W.. Grover and. Stamatelakis, ridging the Ring-Mesh ichotomy with P-ycles, in Proc. of RN Workshop, [2] W.. Grover and. Stamatelakis, ycle-oriented distributed preconfiguration: ring-like speed with mesh-like capacity for self-planning network restoration, in Proc. of I, 998. [3] W.. Grover, John ourcette, and Matthieu loqueur, New Options and Insights for Survivable Transport Networks, in I ommunications Magazine 0/2002, [4]. Stamatelakis and W.. Grover, IP layer restoration and network planning based on virtual cycles, I JS, vol. 8, no. 0, Oct [5]. Stamatelakis and W.. Grover, Theoretical underpinnings for the efficiency of restorable networks using preconfigured cycles ( p-cycles ), I Trans. on omm., vol. 48, no. 8, ug 2000.