Greening the Multi-Granular Optical Transport Network Design under the Optical Reach Constraint

Transcription

1 Greening the Multi-Granular Optical Transport Network Design under the Optical Reach Constraint Nabil Naas, urak Kantarci, and Hussein T. Mouftah School of Electrical Engineering and Computer Science University of Ottawa Ottawa, Canada s: {nnaas, kantarci, Abstract Significant portion of the energy consumption of the optical networks is expected to be in the transport segment. esides its improved bandwidth utilization advantage, multigranular switching concept further helps rectifying the energy bottleneck problem in the backbone. One of the important challenges faced by the multi-granular optical networks is the optical reach enforcement. In this paper, we compare the multi-granular optical network design to the conventional avelength Division Multiplexing (DM)-based network design by enforcing the optical reach limitation as a design constraint. e introduce the heuristics to solve the Routing and Multi-Granular Path Assignment (RMGPA) problem. Our simulation results show that multi-granular optical network design outperforms the DM-based network design in terms of Operational Expenditure (Opex) as it significantly reduces the power consumption in the backbone. Furthermore, through simulations, we show that the green multi-granular design is efficient in terms of the Capital Expenditure (Capex) as the network cost is also degraded. Keywords- Energy-efficient network design; multi-granular switching; GMPLS network; optical reach I. INTRODUCTION Recent research reports that around 8% of the global energy consumption is expected to be contributed by the Information and Communication Technologies (ICTs) while energy consumption of the transport networks and their corresponding Greenhouse Gas (GHG) emissions increase tremendously with the rising peak data rate. Hence, greening the transport networks appears as an emergent concept which has several aspects such as energy savings via power saving designs, architectures and protocols, and utilization of renewable resources [1,2]. In the optical backbone, following the breakthrough of the avelength Division Multiplexing (DM) technology, Multi-Granular Optical Transport Networks (MG-OTNs) have adopted the Generalized Multi-protocol Label Switching (GMPLS) idea and offered the opportunity of separated control and data planes in order to enhance bandwidth utilization and fulfill the requirements of the traffic flows from multiple Quality of Service (QoS) classes. To the best of our knowledge, the most comprehensive survey in this topic has been presented in [3] focusing on the past and current state of the art on MG-OTNs. MG-OTNs introduce further advantages such as enhanced utilization of the switching ports as switching in finer granularities is possible in MG-OTNs [3]. Energy-efficient design of DM networks and green design of IP over DM networks haves been widely studied in the literature [4-7]. However, yet greening the MG-OTN design is not fertile enough for the operators to make strategic decisions. The research in [9], proposes the first study where optimization methods have been proposed to guarantee reduced operational costs and power consumption of MG-OTNs. Recently, in [10], we have studied the effect of the wavelength capacity, number of wavelengths per waveband, and number of wavebands per fiber combination on the power-minimized design of MG-OTNs. In the corresponding study, we have shown that an appropriate combination of wavelength capacity, waveband size and wavebands per fiber can lead to significant power savings in MG-OTN design. Although related work proposes solid solutions for energy-efficient design of MG-OTNs, to the best of our knowledge, existing solutions lack optical reach-constrained energy-efficient MG-OTN design. Optical reach is one of the crucial design parameters, and it denotes the distance that an optical signal can travel without requiring regeneration [11]. Thus, in this paper we propose an MG-OTN design concept under the optical-reach constraint where the objective is joint reduction of power consumption (i.e., Operational Expenditure (Opex)) and network cost (i.e., Capital Expenditure (Capex)). e evaluate our proposal by simulations and compare it to the conventional DM-based design concept. Our simulation results show that the proposed MG-OTN design scheme can jointly ensure power savings and cost-efficiency. The rest of the paper is organized as follows. In Section II, we define the network model. Section III presents the energy-efficient design and planning methodology of MG- OTNs. In Section IV, we present and discuss the numerical results. Finally, we conclude the paper in Section V and give future directions. II. NETORK MODEL An MG-OTN consists of a group of nodes interconnected by fiber links. e assume that the network fiber-conduit layout already exists in which each conduit between a node pair consists of two unidirectional physical links working at opposite directions. Each unidirectional physical link, as /12/$ IEEE

2 shown in Fig. 1, is composed of a number of dark fibers that can be lighted up as required. Each fiber is coded by its ID on a link and consists of uniform or fixed size wavebands, which in turn consist of a number of wavelengths and all wavelengths have the same capacity (i.e., they operate at the same bit rate). Physical Link Unidirectional Fiber aveband λ λ -1 band-1 Sub-λ IP ATM FR GE TDM MPLS Ethernet Fine granular switching Input Fibers F LSC SC LSC SC F Output Fibers aveband MUX aveband DEMUX λ MUX λ DEMUX Coarse granular switching fiber F-1 FSC Add/Drop Traffic FSC TDMSC PSC L2SC FSC SC LSC Figure 1. Multi-granularity traffic flow within the physical link. Each node consists of a Multi-Granular Cross-connect (MG-OXC) segment and an Electronic Cross-connect () segment as illustrated in Fig. 2 [12,13]. The MG-OXC segment is responsible for the (de)grouping and switching of optical flows. The MG-OXC segment consists of the following three switching stages: i) Fiber switching through Fiber Cross-connects (), ii) aveband switching through aveband Cross-connects (), iii) avelength switching through avelength Cross-connects (). The and stages are interconnected via wavelength (de)multiplexers. Similarly, the and stages are interconnected via waveband (de)multiplexers. On the other hand, the segment performs the switching of subwavelength flows and grouping/ degrouping of subwavelength flows into/from wavelengths. In addition, since the enables Optical-Electronic-Optical (O-E-O) conversion with the use of back-to-back transceivers, it introduces the benefits of the 3R (Re-amplification, Reshaping, and Re-timing) regeneration and color conversion of optical signals at all granularities. The regeneration functionality of the is needed to reduce impairments as the impairments accumulate along the all-optical path. However, deployment of, helps avoiding the use of expensive dispersion compensators along network spans [14]. The color conversion functionality removes the colorcontinuity constraint, improves the resource utilization of the network, and ultimately decreases the fiber requirements, which consequently reduces the computational complexity of the control plane. It is worth mentioning here that when the signal regeneration is required color conversion is possible and vice versa. The is interconnected with the MG- OXC through different types of interfaces (i.e., transceivers) [15]. A Fiber/ and/ avelength-switch-capable (FSC/ SC/ LSC) interface is used to launch and terminate a fiber/waveband/wavelength-switched path. It is worth to note that the tributary-side interfaces are left to the future extensions of our model. Figure 2. The multi-granular node architecture. e consider the following path types between any source-destination (s-d) pair in the MG-OTN [12,13]: - Clear λ/ and/ Fiber-Switched (CS/ CS/ CFS) path can carry a sub-λ or λ / band/ fiber demand, as shown in Fig. 3/ 4/ 5. Here, λ denotes a wavelength channel. In order to use the same λ on the same band within the same fiber / band within the same fiber / fiber along the entire path without undergoing O-E-O domain conversion, i.e., path length must be compliant with the optical reach limitation. The launch/termination of this path is at the LSC/ SC/ FSC interface. The path can enter the intermediate finer optical switching stages for grouping/ degrouping the path segment with/from other optical flows that share the same band or fiber. band 0 λ 1 & λ 1 Figure 3. Clear avelength-switched (CS) path. & band 1 band 1 band 1 Figure 4. Clear and-switched (CS) path. λ /12/$ IEEE

3 Node 1 d 12 optical reach d Node d 23 > optical reach Node 3 λ λ Figure 5. Clear Fiber-Switched (CFS) path. d : inter-node distance - Unclear λ/ and/ Fiber-Switched (US/ US/ UFS) path is used to carry a 'sub-λ or λ'/ band/ fiber demand, as shown in Figs Note that the path is a concatenation of CS/ CS/ CFS paths. This path undergoes O-E-O conversion at least once for the purpose of sharing the capacities of its CS paths with other sub-λ demands (as shown in Fig. 5), color conversion (as shown in Fig. 7/ 9/ 11) and/or optical signal regeneration (as shown in Fig. 8/ 10/ 12), if the length of the all-optical path segments exceeds the optical reach. Figure 8. US path, optical signal regeneration scenario. band 1 band 0 band 1 Node 1 Λ 12 & Λ Node 2 Λ 23 & Λ Node 3 Figure 9. Unclear and-switched (US) path, color conversion scenario. = Λ 1 Node 1 d 12 optical reach Node 2 d 12 + d 23 > optical reach Node 3 Λ 12 Λ 13 Λ 23 Λ 12 Λ 23 Λ 13 Λ : sub-λ demand Figure 6. Unclear avelength-switched (US) path, sub-wavelength flow (de)grouping scenario. band 0 d : inter-node distance Figure 10. US path, optical signal regeneration scenario. λ 1 fiber 1 λ 1 fiber 1 Figure 7. US path, color conversion scenario. Figure 11. Unclear Fiber-Switched (UFS) path, color conversation scenario /12/$ IEEE

4 Node 1 d 12 optical reach Node 2 d 12 + d 23 > optical reach Node 3 d : inter-node distance Figure 12. UFS path, optical signal regeneration scenario. IV. NUMERICAL RESULTS In the simulations, the raw traffic demands are uniformly chosen from the ranges (0,500) and (0,1000) Gb/s. The 14- node NSFNET and 11-node European Optical Network (EON) [13] topologies are considered, as shown in Figs. 13 and 14, respectively. The rest of the input parameters are as presented in Table 1. Also, every MG-OXC/ port of the same type has the same power consumption and cost weight and the ratio of the port cost weight to the MG-OXC port cost weight is decided by the relation shown in Equation 1 [11]. III. METHODOLOGY Given the physical topology, static raw demand matrix, and parameters the wavelength capacity (C), number of wavelengths per waveband (), number of wavebands per fiber (), number of fibers per unidirectional physical link (F), and optical reach (R) as well as power consumption and cost weights of all port types (note that the cost weight of the port is a function of R); the first subtask of the design and planning methodology is to create optimized multi-granularity demands using the Demand Adjustment Algorithm [12,13]. The algorithm maps the raw demand between any s-d pair over one or more of the following types of granularities: sub-wavelength, wavelength, waveband, and fiber demands. This first subtask constitutes part of the solution to the so-called Routing and Multi- Granular Path Assignment (RMGPA) problem [12,13]. The second subtask of the methodology aims at obtaining the best solution to the rest of the RMGPA problem by setting the objective to minimizing the total power consumption of the network and satisfying all demands at the same time. Here, we aim at reaching at a suboptimal solution to the problem; hence we adopt the heuristic approach proposed in [13] with the following enhancements to guarantee powerefficiency: i) hen deciding the best route, the cost metric is replaced with the power metric; ii) An additional network cost forecast function is called upon deciding the least power consumption solution. The RMGPA problem is solved for a given set of parameters R and F. For a certain value of R, the RMGPA problem is solved several times for different values of F starting from F=1 until a saturated value of F; i.e., F=F sat. Thus, beyond F sat the total network power consumption becomes saturated. The minimum number of fibers per unidirectional link needed to obtain a feasible solution is denoted by F min. Lowest total network power consumption is achieved by the optimal number of fibers per unidirectional link, which is in the range of [F min,f sat ]. In this work, we assume a fiber-rich scenario while future extension of this study will include case studies under the fiber-scarce scenario as well. Accordingly, the best design solution for the given ranges of R and F is the one that can introduce the lowest total power consumption at F opt. port weight MG - OXC port weight = 2 (1.25) log 2 ( R/1000) (1) TALE 1. SIMULATION SETTINGS C 10 Gbps 8 8 F [1, F sat ] R [1000, 9000] km Power of an MG-OXC port 1 power unit Power of an port 7 power unit [16] Cost of an MG-OXC port 1 cost unit Cost of an port determined by Equation Figure node NSFNET network fiber layout with span distances in km Figure node EON network fiber layout with span distances in km /12/$ IEEE

5 Fig. 15 compares the sub-optimum results under the MG and conventional DM concepts in terms of total power consumption with respect to the optical reach. As seen in the figure, the employment of the MG switching principle can lead to a consistent saving on the order of 71-75% compared to the case when the conventional DM technology is used. Furthermore, optical reach has an optimum value where beyond this value the total power consumption of the network remains almost stable. The reason of this behavior is as follows. hen the reach value exceeds the length of all physical spans, s-d pairs have larger selection spaces for route assignment. Hence, shorter and less congested routes are expected to require less O-E-O conversions. esides, the optimum optical reach is larger in the NSFNET when compared to the scenario under the EON since the average node-to-node distance is longer and the connectivity is lower in the NSFNET. Fig. 16 compares the total network costs in the fiber-rich situation when the RMGPA problem is solved with the objective of minimizing the power consumption and minimizing the network cost (adopting the heuristics in [13]). The following conclusions can be drawn from the Figure. First, the excessive network costs of the solutions produced by the heuristic approach with the power minimization objective are not more than 2% of the network cost that result from the heuristic approach with the cost minimization objective. Second, increasing the optical reach value raises the cost of the port and reduces the number of O-E-O conversions. Accordingly, increasing the optical reach leads to a relative extremum in which the minimal network cost occurs. Third, when Figs. 15 and 16 are compared to each other, it is clear that minimizing the power consumption leads to an increase in the optimum optical reach leading to a slight (2-3%) decrease in the total network power consumption (i.e., Opex). Finally, it can be concluded that these advantages are at the expense of a 3-8% increase in the total network deployment cost (i.e., Capex). V. CONCLUSIONS Green communications and networking concept has appeared as a recent challenge especially for the backbone networks, which can be addressed by either utilization of the renewable energy or power savings in the non-renewable resources. In this paper, we have focused on greening the backbone networks through energy-savings and have proposed power-efficient design and planning of green Multi Granular-Optical Transport Networks (MG-OTNs) with signal regeneration capability. e have shown that the employment of the MG switching concept leads to a considerable saving in the total power consumption (i.e., Opex) in comparison to the case where conventional DM technology is deployed. Furthermore, we have shown that the MG-OTN design heuristics are cost-effective (i.e., reduced Capex) as well. esides showing the efficiency of the presented MG-OTN design in terms of both Opex and Capex Finally, we have further shown that, the optimum optical reach obtained with the cost minimization objective can still lead to a green MG-OTN if the network operator does not include increasing the optical reach in its agenda. Future extension of this study includes green MG-OTN design under fiber-scarce scenario and under heterogeneous demand profiles. REFERENCES [1] Y. Zhang, P. Chowdhury, M. Tornatore,. Mukherjee, "Energy Efficiency in Telecom Optical Networks," IEEE Communications Surveys & Tutorials, vol. 12, no. 4, pp , Fourth Quarter [2] H. T. Mouftah,. Kantarci, Greening the Survivable Optical Networks: Solutions and Challenges for the ackbone and Access, Sustainable Green Computing: Practices, Methodologies and Technologies, edited by -C. Hu and N. 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6 Total Network Power Consumption [KPower Unit] DM with 1000G MG with 1000G 60 DM with 500G MG with 500G Optical Reach [km] Total Network Power Consumption [KPower Unit] DM with 1000G 45 MG with 1000G 35 DM with 500G MG with 500G Optical Reach [km] (a) (b) Figure 15. Total network power consumption at F opt vs. optical reach: (a) under the NSFNET; (b) under the EON. Total Network Cost [Unit cost] MG with power min at 1000G MG with cost min at 1000G MG with power min at 500G MG with cost min at 500G Optical Reach [Km] (a) Total Network Cost [Unit Cost] MG with power min at 1000G MG with cost min at 1000G MG with power min at 500G MG with cost min at 500G Optical Reach [Km] (b) Figure 16. Total network cost at F opt vs. optical reach: (a) under the NSFNET; (b) under the EON /12/$ IEEE