We proposed a heuristic algorithm, Minimum Hops with Least Slots spectrum (MHLS), to solve the Routing and spectrum assignment problem in elastic optical networks. The proposed MHLS is implemented in conjunction with the transponder supporting slice-ability

Ahmad, Arsalan

Alam, Shahzad

Curri, Vittorio

Ghafoor, Salman

Khan, Ihtesham

Masood, Muhammad Umar

English

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

16 July 2022POLITECNICO DI TORINORepository ISTITUZIONALERouting and Spectrum Allocation Heuristic for Sliced Elastic Optical Network System / Alam, Shahzad; Khan, Ihtesham;Masood, Muhammad Umar; Ahmad, Arsalan; Ghafoor, Salman; Curri, Vittorio. - ELETTRONICO. - (2021), pp. 1-2.((Intervento presentato al convegno IEEE Photonics Society Summer Topicals Meeting Series (SUM) tenutosi a CaboSan Lucas, Mexico nel 19-21 July 2021 [10.1109/SUM48717.2021.9505961].OriginalRouting and Spectrum Allocation Heuristic for Sliced Elastic Optical Network SystemIEEE postprint/Author's Accepted ManuscriptPublisher:PublishedDOI:10.1109/SUM48717.2021.9505961Terms of use:openAccessPublisher copyright©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in anycurrent or future media, including reprinting/republishing this material for advertising or promotional purposes, creatingnew collecting works, for resale or lists, or reuse of any copyrighted component of this work in other works.(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2917840 since: 2021-08-15T14:35:55ZIEEERouting and Spectrum Allocation Heuristic forSliced Elastic Optical Network SystemShahzad AlamRCMS, NUST, Pakistanshahzad alam105@yahoo.comIhtesham KhanDET, Politecnico di Torino, Italyihtesham.khan@polito.itMuhammad Umar MasoodDET, Politecnico di Torino, Italymuhammad.masood@polito.itArsalan AhmadSEECS, NUST, Pakistanarsalan.ahmad@seecs.edu.pkSalman GhafoorSEECS, NUST, Pakistansalman.ghafoor@seecs.edu.pkVittorio CurriDET, Politecnico di Torino, Italycurri@polito.itAbstract—We proposed a heuristic algorithm, Minimum Hopswith Least Slots spectrum (MHLS), to solve the Routing and spec-trum assignment problem in elastic optical networks. The pro-posed MHLS is implemented in conjunction with the transpondersupporting slice-ability.Keywords—Elastic Optical Network, Off-line routing, SBVTI. INTRODUCTIONDuring the last decades, extensive growth in the internettraffic of the core optical networks is observed [1]. Also,the nature of traffic is changing and becoming more dynamicand less predictable [2] mainly due to the implementations ofbandwidth hungry and Internet of Things (IoT) applications.This has urged the request from the network operators toexploit the maximum residual capacity of the existing deployednetwork architecture. To better utilize the residual capacity ofthe existing infrastructure, the data transport layer needs topush to maximum capacity. The key enabler technology foroptimal exploitation of data transport is Dense WavelengthDivision Multiplexing (DWDM) transmission technology. Thetraditional fixed-grid DWDM based optical networks typicallyhold 50 GHz spectrum spacing systems as defined by Inter-national Telecommunication Union (ITU) standards. This non-granular distribution of the spectrum leads to inefficient scalingperformance to cope with the growing internet traffic demand.This mismatch cause the inefficient utilization of spectrum andis expected to degrade the utilization performance with increas-ing data rates. To overcome this bottleneck, a more granularapproach in the spectrum slicing is adopted and the fixed-grid systems are evolved towards flex-grid systems. An opticalnetwork based on flex-grid systems is equipped with adaptivetransceivers and network elements (NE) that are capable toprovide the flexibility according to the traffic demands. Suchcombination of adaptive transceivers and NE enables next-generation optical network known as Elastic Optical Network(EON), allowing services providers to tackle the continuousgrowth of traffic volume efficiently [3]. The flexibility in EONsis generally achieved by dividing the spectrum into flexibleslots and allocating them by considering traffic demands [4].With the evolution of DWDM towards EONs, along withthe benefits of flexibility, some new challenges also arise.Most significant of them is the optimized management ofthe spectrum that is the Routing and Spectrum Assignment(RSA). RSA in EON is analogous to Routing and WavelengthAssignment (RWA) in DWDM. RSA is NP-hard problem [5]which is the tight coupling between RSA. In RSA, the primarytarget is to route the traffic over the spectrum in such a waythat minimum resources are utilized to accommodate a specifictraffic request. RWA considers the continuity constraint but inRSA, an additional constraint of contiguity of spectrum slotsis also considered over the routing path.In this paper, a novel heuristic algorithm, MHLS is pro-posed to solve the offline RSA problem in EONs. The proposedMHLS algorithm is implemented in conjunction with theFlex-Optical carrier source module (Flex-OCSM) SBVT as itprovides better hardware and in-device utilization of resources[6]. The synergic use of MHLS and Flex-OCSM provideeffective improvement in the spectrum utilization compared toexisting benchmark algorithms. The proposed algorithm is runover the random network topology to validate its performance.II. PROPOSED MHLS TO SOLVE RSAIn this section, the proposed heuristic algorithm MHLS iscomprehensively explained. For performance evaluation pur-poses, we are considering some traditional algorithm, namingthem as benchmark algorithm. These algorithms are First Fit(FF), Most Sub-carrier First (MSF) MSF and Longest PathFirst (LPF). The MHLS heuristic is designed while consideringtwo general key points. The first is the total number of HopsH and the second is the total number of slots FS for eachtraffic request in the network. In algorithm MHLS, the inputparameter is D, which represents the set of offline trafficdemands that needed to be fulfilled. For each demand fromthe respective source to the destination, required traffic bit-rates in Gb/s is known. Well, the main function of the MHLS,is for each t request from s→ d (Line 1), first calculates theoptical reach (OR) for the respective s and d (Line 4). ThenH is calculated (Line 5) using Dijkstra − shortest − pathalgorithm and OR for given traffic request t. MHLS, find anappropriate modulation level M for the given traffic requestwith the help of OR and required bit-rates (Gb/s) (line 6)[7]. Transmission data rate Z is calculated after selecting M(Line 7). After the calculation of OR,H,M and Z , slots FScalculation is performed (line 8). For a given s and d, FSare calculated as FSs→d = ts→dZs→d . After calculating all theseparameters, a new parameter is being introduced N P (Line9), which is the product of two parameters FS and H. NPAlgorithm 1 Minimum Hops with Least Slot SpectrumRequire: Total number of slots required by given s→ d in networkEnsure: Physical Topology Graph G with V number of nodes1: Input Ds→d,t∀(s, d, t) ∈ V2: Output Ψ3: for all Ds→d,t ∈ D do4: ORs→d = calculate optical reach;5: Hs→d = calculate hops(ORs→d);6: Ms→d = find modulation level (ORs→d);7: Zs→d = find data rate (Ms→d);8: FSs→d = Slot calculation (Zs→d);9: N P = FSs→d × Hs→d;10: end for11: Sort the N P in the ascending order;12: Ψ = sorted N P ;13: return Ψ;helps us find the total number of slots required by given sand d in the network. The same steps will be repeated forthe entire traffic matrix D (line 3-9). After that, sorting isdone in the ascending order of the N P (line 11) and saveas output in matrix Ψ (line 11). The proposed algorithmMHLS is reported in Algo. 1, combines two parameters Hand FS, then sort it in ascending order and then sequentiallyserved to our design network. Results reflect that the proposedMHLS minimize Blocked Requests (BR), Blocked Traffic (BT)and Slots Used (SU) in the network as compared to otherconsidered benchmark algorithm.III. RESULTS AND PERFORMANCE ANALYSISThis section compares simulation results in the proposedalgorithm MHLS and benchmark algorithms (FF, MSF andLPF). The performance of the proposed MHLS is analyzedunder dynamic network parameters such as varying numberof nodes, node degree, optical reach and the nature of traffic(heterogeneous traffic and homogeneous traffic). This permitsus to demonstrate which algorithm performs better in minimumblocked requests (BR), blocked traffic (BT) and spectrum-slots used (SU) in the network. The random physical networktopology considered in this work have 20 nodes, 50 bi-directional links and a node degree of 2.5. In heterogeneouscase, traffic is randomly distributed on links such that thenetwork has an average traffic per node. First, traffic matricesare created by setting each traffic demand equal to a uniformlygenerated number in the range [0:1] and then scaling thesevalues to achieve a given target average traffic per node. Whilefor homogeneous traffic, traffic is uniformly distributed on thenetwork’s links. We consider 50 Gb/s, 100 Gb/s and 150 Gb/sof homogeneous traffic per node and 1000 Gb/s, 1500 Gb/s,2000 Gb/s and 2500 Gb/s of heterogeneous traffic averageper node. The detailed comparison of homogeneous trafficscenario is depicted in Fig. 1. Results in Fig. 1 reveal that theperformance of MHLS is significantly better compared to otherbenchmark algorithms. At first, for 50 Gb/s traffic performancefor algorithm MHLS: BT and BR is around 5.7% and 27.1% isSU, for FF: its BR and BT is 6.3%, and SU is 28.9% while forMSF: BR and BT is 9.20% and its SU is 32.30% and for LPF:BR and BT is 9.40%, and its SU is 33.20%. Lesser the valueof BR, BT and SU, the better the algorithm’s performance,which results in efficient utilization of network resources. Asinput traffic rate increases to 100 Gb/s and 150 Gb/s, theperformance of MHLS enhances with ≈ 23%, 25% less BR,BT and with 12%, 14% less SU as compared to FF, MSF andLPF as shown in Fig. 1. For heterogeneous traffic profile,results are demonstrated in Fig. Fig. 2. The results in Fig. 2depicts that at the traffic of 1000 Gb/s, MHLS accommodates50 100 150Traffic Gb/s020406080BR %FF MSF LPF MHLS50 100 150Traffic Gb/s020406080BT %50 100 150Traffic Gb/s020406080SU %Fig. 1: Homogeneous traffic analysis1000 1500 2000 2500Traffic Gb/s020406080BR %FF MSF LPF MHLS1000 1500 2000 2500Traffic Gb/s020406080BT %1000 1500 2000 2500Traffic Gb/s020406080SU %Fig. 2: Heterogeneous traffic analysis≈ 10%, 8% and 6% more traffic requests and saving 3%, 4%,and 6% spectrum compared to FF, MSF and LPF. As the trafficrate increases to 1500 Gb/s, 2000 Gb/s and 2500 Gb/s, theperformance of MHLS outperform FF, MSF and LPF againstall the considered performance parameters (BR, BT and SU).Furthermore, as traffic rate increases, demand of spectrumresources increase which in turn demands for efficient orderingpolicies. Due conventional methods of traffic grooming andhandling continuity/contiguity constraints in traditional algo-rithms, FF, MSF and LPF can not deliver efficiently comparedto MHLS. MHLS offers better traffic grooming with a betterordering policy, enabling more traffic requests and savingmore spectrum than other algorithms, even for large-sized realoptical networks.IV. CONCLUSIONIn this paper, a novel heuristic algorithm MHLS is pro-posed in conjunction with the Flex-(OCSM) SBVT, which canaccommodate the maximum number of traffic requests andminimize the total amount of block traffic and frequency Slotusage. To evaluate the performance of MHLS, simulations areconducted for a random generated topology with different traf-fic scenarios. Furthermore, for analysis purposes, a comparisonis also presented with traditional algorithms.REFERENCES[1] Cisco, “Cisco annual internet report,” Cisco white paper p. 35 (2020).[2] L. M. Contreras et.al, “Toward cloud-ready transport networks,” IEEECommunications Magazine 50, 48–55 (2012).[3] M. Jinno et.al, “Spectrum-efficient and scalable elastic optical pathnetwork: architecture, ben..” IEEE Communications Magazine 47 (2009).[4] L. Velasco et.al, “Modeling the routing and spectrum allocation problemfor flexgrid optical networks,” PNC 24, 177–186 (2012).[5] M. Jinno et.al, “Distance-adaptive spectrum resource allocation inspectrum-sliced elasti..” IEEE Communications Magazine 48 (2010).[6] M. U. Masood et.al, “Smart provisioning of sliceable bandwidth variabletransponders in elastic optical net.” in NetSoft, (IEEE, 2020), pp. 85–91.[7] I. Khan et.al, “Impact of data center placement on the power consumptionof flexible-grid optical networks,” OE 59, 016115 (2020).

Routing and Spectrum Allocation Heuristic for Sliced Elastic Optical Network System

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Routing and Spectrum Allocation Heuristic for Sliced Elastic Optical Network System

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