Hitless Network Re-Optimization to Reduce Spectrum ... - IEEE Xplore

Hitless Network Re-Optimization to Reduce Spectrum Fragmentation in Distributed GMPLS Flexible Optical Networks Domenico Siracusa(1), Attilio Broglio(1), Andrea Zanardi(1), Elio Salvadori(1), (2) (2) Gabriele Galimberti , Domenico La Fauci (1) (2)

CREATE-NET, Trento, Italy, [email protected] Cisco Photonics, Monza, Italy, [email protected]

Abstract This paper presents a network re-optimization procedure moving the spectrum allocation of connections in a distributed fashion to defragment flexible optical networks, according to the proposed policies. Without changes to GMPLS standards, it improves network performance without traffic disruptions or excessive burdens for the control plane. Introduction The adoption of coherent transmission techniques and the introduction of flexible software-defined transponders have paved the way to new angles of optimization in the field of optical control planes (OCPs). In fact, while the former has simplified the evaluation of Physical Layer Impairments (PLIs), the latter has enabled a more efficient utilization of available optical resources. Despite these advantages, inefficient spectrum utilization is typically experienced due to its fragmentation induced by inoculate allocations and unexpected network evolutions, channel tear-down, network recovery and maintenance. Spectrum fragmentation is not ascribable to specific network architectures, but it is self-evident it has a more remarkable impact in distributed architectures than in centralized ones, since the source node (i.e., the one that computes the path) has a limited vision of the network status and cannot perform optimal choices w.r.t. the actual traffic in the network. In order to address the aforementioned issue several de-fragmentation techniques have been recently proposed. The basic idea is to re-route established lightpaths (LPs) on different resources, to compact the spectrum frequencies and ensure a larger amount of available resources to incoming connections. Recent studies have proposed valuable heuristics or ILP-based solutions1,2. However, these solutions are either not suitable for a dynamic context or they count on the possibility of exploiting information on the current network status that is not available on a distributed GMPLS-based computation scheme. Moreover, from the implementation perspective, they consider a disruptive solution since, in optical networks, Make-Before-Break (MbB)3 is not hitless as in packet networks. In MbB, paths are established on alternative routes and assigned frequency slots before releasing the original paths, minimizing the disruption time. An alternative approach is called push-pull4; it operates at the

physical layer by re-tuning the transmitting laser source of the selected working LPs, without causing any traffic disruption. This paper proposes, for the first time (to the best of our knowledge), a GMPLS-based Distributed Hitless Network Re-optimization (DHNR) that can be exploited to reduce spectrum fragmentation. We present the main operations carried out by the DHNR procedure and the related policies. Results show that blocking probability is decreased with a tolerable additional burden for the OCP. Distributed Hitless Network Re-Optimization procedure In this work we are considering a Flexible Source Routing (FSR) distributed OCP architecture with coherent transmission technique. Coherent devices allows the full evaluation of the path feasibility at the source node with a very high level of confidence w.r.t. the actual feasibility (this is not the topic of this work; we will delve into it in future works). In the FSR solution, route computation, resource discovery (i.e., spectrum availability) and optical validation are performed in the routing phase by the source node, while the signaling phase is mainly deputed to configure the devices. Route selection is based on constrained k-shortest path evaluation: if the activation of the service on the selected route fails (e.g. no resource available or optically not feasible), an alternative path is selected and attempted. OSPF-TE protocol carries a spectrum bitmap containing the information on the available spectrum slots. When a source node (the head) receives a LP setup request, it computes the k-shortest paths to the destination and triggers the reservation procedure by sending a PATH message with both the LABEL_SET and the upstream label defining the Frequency Range (FR) to be reserved downstream and upstream, respectively. The receiver node (the tail) checks the LABEL_SET and the upstream label and

Routing and Spectrum assignment policies For both the Routing and Spectrum Assignment (RSA) and the DHNR we have considered two policies: (i) First Fit (FF) and (ii) Leave Granularity Combination (LGC). FF and LGC are exploited in DHRN to choose if FR should be moved and where. The FF policy is the wellknown policy. In case of DHRN procedure, the FR of the LP is moved if there is at least one available spectrum slot with a lower index. The LGC is a new policy proposed to support the DHRN procedure. Fragmentation is mainly provided by the non-optimal combination of allocated FRs among LPs that shares some links. This effect is evident in distributed networks and it is magnified by the presence of requests with different granularities. An example is reported in Fig. 1 (top): assuming spectrum granularities that can be requested equal to

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sends a RSVP RESV message with the label containing the same FR indicated in the PATH message. During the RESV procedure, tail, transit, and head nodes configure the interfaces. In the FSR framework we have implemented a preliminary DHNR technique. Each source node maintains a table containing, for each established LP, the time in which the RSVP setup has been completed and an update interval parameter. Then, re-optimization events are called periodically (w.r.t. the update interval) for each LP. Each source node, according to the information about spectrum availability provided by OSPF-TE, check if (i) shifting the Frequency Range (FR) occupied by the LP in a different position (FRNEW) reduces the fragmentation, according to a given policy; (ii) a continuous FRO&N including both the original FR and FRNEW can be reserved in advance, in order to let the transponders to get aligned on the new FR, without causing service disruption. If both the conditions are satisfied, 1) the source node reserves the FRO&N according to the previous procedure, 2) the transponders are tuned on the FRNEW, 3) the source node reserves the FRNEW according to the previous procedure (i.e., unused spectrum is released). Please note that (i) the FR is moved according to the push-pull4 technique, thus no disruption is introduced; (ii) DHNR does not need any specific extension to the GMPLS protocols standards (extensions for carrying spectrum information are currently under discussion within IETF); (iii) FR is moved in the spectrum domain, we do not change the paths or squeeze the amount of spectrum assigned to a LP. Finally, in order to avoid unnecessary blocks, LP requests have priority over the re-optimization events.

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Fig. 1. Example for spectrum fragmentation (top) and weights distribution for LGC policy (bottom).

LGC has been proposed to address this issue. The idea of LGC is to take advantage from the packing capacity of FF and to combine it with a strategy that discourages the allocation of a FR if it leaves in all the links of the path a number of available spectrum slots that is not linear combination of the requested bandwidth granularities. Following the previous example, Fig. 1 (bottom) shows the distribution of the LGC weights for R-5-4. The y-axis shows the weights for each position of the first slot (on the x-axis) of the R-5-4 FR. For simplicity, we have assumed 50 frequency slots per link. As it can be appreciated, the same position of FF (slot 8) is good for link N5-N2 but bad for link N2-N4, thus, it is highly penalized. On the other side, slots {12,15,16,19, …} are preferred, since they will leave in both links a free FR with a number of slots that is combination of {4,7,10,12} slots. Due to space reasons, the pseudo-code for LGC policy is not reported here. Simulation Results In this section we show the results of the proposed DHNR procedure. Results have been obtained in the Deutsche Telekom (DT) network

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topology consisting of 14 nodes and 46 directed links5. We have assumed 384 frequency slots per link. Requests are chosen with a uniform distribution from a set of {100,200,300,400} Gbit/s corresponding to {4,7,10,12} slots of 12.5 GHz. In the simulations we assume a classical traffic model based on LP requests with Poisson arrivals at an average rate 1/μ=100s and an average exponential holding time of Ȟ sec, chosen so as to have a target network load. The traffic load ȕ is defined as the average network resource (slots) usage computed in percent as: ȕ = (Nc×Lc×Sc)/(M×SL)×100%, where Nc is the average number of active connections and equals Ȟ/μ; Lc is the average number of hops in the network considering only shortest paths between all pairs of nodes; Sc is the average number of slots per connection; M is the number of links; SL is the number of frequency slots in each link. Simulation results, carried out with an Omnet++-based simulator, have a confidence level of 95% and a confidence interval of 1%. Fig. 2 shows the blocking probability against the injected load. We have tested four solutions, the original source routing solution with SP routing combined with both the presented policies (curves FF and LGC) and then we applied the DHNR to these solutions, using the same policies both for spectrum allocation and reoptimization (curves DHNR-FF and DHNRLGC). FF shows a good performance, which is slightly improved by LGC at low traffic load values. This mainly demonstrates that even complex fragmentation avoidance techniques may not be effective, due to the fact that they do not manage network evolutions like LP teardown. Moreover, LGC discourages “bad” spectrum allocations, but it does not prevent them, thus fragmentation is anyhow possible. The DHNR procedure is able to further reduce the blocks, since it addresses the network status changes (setup or teardown). To this account, we have set the update interval of the DHNR much smaller than the LP inter-arrival time (i.e., the chosen ratio is 1:10). The DHNR-FF improves the spectrum utilization when a tear-down event occurs compacting the spectrum and reducing the fragmentation. It is not activated on LP setup events, since the FF policy already packs each new LP with the already active ones. The DHRN-LGC is the best solution in terms of performance; it improves the spectrum utilization of LGC also for LP setup events (a new allocated LP may create opportunities to reduce fragmentation). This is reflected in the average number of LP spectrum re-optimizations per active LP produced by the two DHRN solutions, as shown in Table 1.

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DHNR-LGC 0.13% 0.37% 1.04% 1.88% 4.94% 12.19%

Fig. 2. Blocking probability vs traffic load.

The number of re-optimization increases as the load increases (more LPs to move); the maximum value is 22.95 for the DHNR-FF and 40.47 for DHRN-LGC. Please note that the latter is almost the double of the former since DHRNLGC is activated not only for LP teardown events (as DHNR-FF), but also for LP setup ones. Considering that optical connection requests are not very frequent, we think that this number of spectrum refreshes is feasible and affordable by a distributed control plane. Table 1. Average number of LP spectrum reoptimizations per active LP. Min Max

DHNR-FF 17.22 22.95

DHNR-LGC 22.48 40.47

Conclusions We presented a distributed hitless network reoptimization procedure to reduce fragmentation in flexible coherent optical networks. It works well when coupled with an effective policy that chooses if moving the spectrum allocation of a request reduces the fragmentation. The proposed procedure improves the network performance and it is easily implementable without additional costs and standardization efforts. References [1] A. Patel et al., Proc. OFC’11, OTuI8 (2011). [2] X. Yu et al., Proc. OFC’12, JTh2A.35 (2012). [3] T. Takagi et al., Proc. ECOC’11, Mo.2.K.3 (2011). [4] F. Cugini et al., J. Lightwave Technol. 31, 1 (2013). [5] F. Agraz et al., OFC’10, PDPD5 (2010).