Design for Energy-Efficient IP Over WDM Networks With ... · sign of energy-minimized IP over WDM...

Design for Energy-Efficient IP Over WDMNetworks With Joint Lightpath Bypassand Router-Card Sleeping Strategies

Yunlei Lui, Gangxiang Shen, and Weidong Shao

Abstract—To reduce the energy consumption of the In-ternet, much research effort has been dedicated to the de-sign of energy-minimized IP over WDM networks. In thisstudy, we design energy-minimized IP over WDM networksbased on modular router cards and by jointly applying thelightpath bypass and router-card sleeping strategies. As akey advantage, the design does not require rerouting ofIP flows or optical channels in either the IPor optical layer.Rather, to save energy, we only need to sleep or wake up therouter cards according to user traffic demandswhen apply-ing the sleeping strategy, which significantly simplifies thenetwork control and management. To minimize the totalenergy consumption of the IP over WDM network in a timeperiod (e.g., 1 day), we develop a mixed integer linear pro-gramming (MILP) optimization model. Moreover, to allevi-ate the computational complexity of the MILP model, wedivide the minimization problem into two subproblems,namely, 1) establishing a virtual topology and 2) allocatingrouter ports on router cards to the optical channels on dif-ferent virtual links. For each of the subproblems, we de-velop a separate MILP optimization model. Further, forlarge-network design, we also propose two efficient heuris-tics to optimize energy consumption. Simulation studies in-dicate that the joint MILP model with the lightpath bypassand router-card sleeping strategies can maximally reduceenergy consumption, up to 40% compared to the nonsleep-ing case. In addition, under the router-card sleeping strat-egy, the approach of allocating the router ports on differentrouter cards to the optical channels plays an important rolein the design of an energy-minimized IP over WDM net-work. A mixed mode that jointly performs interleavingand sequential router-port allocations can achieve thehighest energy savings.

Index Terms—Energy-minimized design; Green Internet;IPoverWDMnetwork; Lightpath bypass; Sleeping strategy.

I. INTRODUCTION

T he Internet keeps on changing our lifestyle, increas-ing productivity, and boosting economic development

across the world [1]. Internet traffic is estimated to grow toup to 50 times the current volume within the next 10–15years [2,3]. This increase is mainly attributed to thefast growth of new types of high-speed network access

technologies, various network applications, and large-volume multimedia traffic [4]. However, this growth is ac-companied by an increase in both the amount and energyconsumption of network equipment, which consequentlyraises concerns about operational costs and environmentalimpacts [5]. Electricity consumption by networks is grow-ing fast, and its relative contribution to the total worldwideelectricity consumption has increased from 1.3% in 2007 to1.8% in 2012 [6]. The Internet has also become a notablecontributor of carbon emissions, e.g., the information andcommunication technology sector generates about 2% ofglobal carbon emissions [7,8]. Consequently, both economi-cal and environmental concerns are major drivers for pro-moting a “greener” Internet.

Different energy-aware networking techniques may beconsidered in order to increase the energy efficiency ofoptical transport networks; some of these are summarizedin [5,9,10]. A common efficient approach is lightpath bypass,which maximally reduces the number of highly energy-consuming optical–electrical–optical conversions. The au-thors in [11] exploited the lightpath bypass strategy todesign an IP over WDM network that minimized power con-sumption by reducing the number of required IP routerports. Another interesting approach is to turn off somedevicesor put some of them into a sleeping mode (low energy state)when the traffic is low. In [12], a realistic IP network topologywas considered and evaluated for the amount of energythat can potentially be saved when nodes and links that arefree of traffic are turned off during off-peak periods. Mixed-line-rate (MLR) network technology is another importantmethod toward a more energy-efficient network comparedwith single-line-rate network technology. This is because ofthe trade-off that exists between capacity and energy con-sumption and because the MLR network is more flexible inprovisioningbandwidthondemand [1,13]. Inaddition to theseideas, other technologiesandmethodssuchas trafficgrooming[14–16] and energy-aware routing and wavelength assign-ment (RWA) [17–19] were employed to achieve a moreenergy-efficient network. In [20], the authors designed anovel network architecture with a master–slave IP routerconfiguration that claimed to use up to 45% less energy.

Among the above existing studies, much work was per-formed to reduce energy consumption by independentlyputting to sleep router ports when they are free of networktraffic [15,21,22]. However, in reality, it is not easy to puteach router port to sleep independently. In a core router,http://dx.doi.org/10.1364/JOCN.5.001122

Manuscript received April 8, 2013; revised July 27, 2013; accepted July30, 2013; published October 14, 2013 (Doc. ID 188434).

The authors are with the School of Electronic and Information Engineer-ing, Soochow University, Suzhou, Jiangsu 215006, China (e-mail: [email protected]).

1122 J. OPT. COMMUN. NETW./VOL. 5, NO. 11/NOVEMBER 2013 Lui et al.

1943-0620/13/111122-17$15.00/0 © 2013 Optical Society of America

http://dx.doi.org/10.1364/JOCN.5.001122

router ports are typically grouped (arranged) on a commonrouter card [23]. For example, a Cisco CRS-1 four-port OC-192c POS card contains four router ports [24]. For thesleeping strategy, it is more practical to sleep a routercard,1 instead of independently sleeping a router port.The condition for which a router card can sleep is one whereall the contained ports are free of traffic. Otherwise, thecard has to be active. Under the router-card-based strategy,multiple ports can share a common router card controllerand power supply circuit. Thus, based on such a strategy, itis relatively easier to turn off or sleep the common control-ler and power supply circuit to save energy. In contrast, forthe port-based sleeping strategy, we may need to imple-ment an independent controller and power supply for eachport in order to support the port-based sleeping strategy.This, therefore, can greatly increase the complexity andcost of a router card.

In this paper, based on the router-card sleeping ap-proach, we consider employing the lightpath bypass strat-egy [11,25] to design an IP over WDM network, which aimsto minimize the total energy consumption in a given timeperiod (i.e., 1 day). Based on an assumed hourly traffic dis-tribution in a network, we develop a joint mixed integerlinear programming (MILP) optimization model to mini-mize the total energy consumption. Because of the highcomputational complexity of the MILP model, we also de-velop a detached approach that divides the original jointoptimization problem into two subproblems. For each ofthe subproblems, correspondingMILP optimizationmodelsare developed. Specifically, we first develop anMILPmodelto design an energy-minimized IP over WDM network withthe lightpath bypass strategy for peak-hour traffic. Next,based on the peak-hour design, another MILP model thatsupports router-card sleeping is followed to minimize theenergy consumption of 1 day. Although the detached MILPmodels are easier to solve than the joint MILP model, theystill suffer from computational difficulty for large net-works. Thus, for large networks, we further develop two ef-ficient heuristic algorithms for the energy-minimized IPover WDM network design. Similar to the detached MILPmodels, the algorithms also divide the original probleminto the two subproblems and provide efficient heuristicsteps for each of the subproblems.

From the network control plane point of view, the pro-posed design approaches show an important advantage,i.e., there is no user traffic flow rerouting along theoptical-layer virtual topology or optical channel reroutingalong the fiber links in the physical layer. After the virtualtopology and optical channels are established in the opticallayer, we only need to adjust the amount of user traffic tra-versing the optical channels (but no rerouting) at differenttime slots and sleep or wake up router cards to minimizethe energy consumption of the IP over WDM network.

The key contributions of this work are summarized asfollows: 1) compared to many existing works, this study

is more practical, as it considers router-card sleeping in-stead of router-port sleeping; 2) to minimize the energy con-sumption, we develop anMILPmodel that jointly considersthe router-card sleeping and lightpath bypass strategies;3) to alleviate the computational difficulty of the problem,we disintegrate the joint MILP model into two detachedMILP models for a suboptimal solution, and moreover,for large networks, we develop two efficient heuristic algo-rithms, in which two important router-port allocationmodes are proposed to maximize router-card sleepingopportunities.

The rest of the paper is organized as follows. Section IIintroduces the energy-consumption model and router-cardsleeping strategy in the IP over WDM network. Section IIIpresents the research problem and the MILP optimizationmodels for the joint and detached cases. Two heuristics areproposed in Section IV. Section V introduces the studycases and test networks of the current research.We presentand analyze simulation results in Section VI and concludethe paper in Section VII.

II. IP OVER WDM NETWORK

In this section, we first introduce the network model con-sidered in this study, followed by the proposed router-cardsleeping strategy.

A. Network Model

We focus on the energy-minimized design for the IP overWDM optical transport network. As shown in Fig. 1, the IPover WDM network consists of two layers, the optical layerand the IP layer. Nodes in the optical layer are opticalcross-connects (OXCs), which are interconnected by physi-cal fiber links. Each fiber link carries up to L wavelengths,and the capacity of each wavelength is assumed to be BGbits/s. Associated with each fiber link, a paired wave-length multiplexer and demultiplexer are deployed tomultiplex and demultiplex wavelengths at the two endsof the link. Also, to enable optical signals to travel longdistances, erbium-doped fiber amplifiers (EDFAs) are de-ployed along the fiber links. On the optical layer, thereare many end-to-end lightpaths or optical channels con-necting source and destination nodes. For each end-to-end optical channel, a pair of transponders is requiredfor data transmission. In addition, we do not allow wave-length conversion in this study; thus, the constraint ofwavelength continuity should be ensured for every end-to-end optical channel, which requires an identical wave-length to be assigned on all the fiber links traversed bythe optical channel. These end-to-end optical channelsform a virtual topology for the upper IP layer, in which eachvirtual link can contain multiple end-to-end optical chan-nels that connect the same pair of source and destinationrouter nodes. In the IP layer, a core router connects to anOXC via short-reach (SR) interfaces and the core routersconnect to each other via virtual links, which correspondto end-to-end lightpaths (optical channels) in the opticallayer.

1To the best of our knowledge, at this time there seem to be no availablecommercial routers whose cards can support the sleeping mode. In this pa-per, we assume that routers have such card-based sleeping functionality.

Lui et al. VOL. 5, NO. 11/NOVEMBER 2013/J. OPT. COMMUN. NETW. 1123

In the traditional point-to-point IP over WDM network,IP traffic should be groomed and forwarded in the elec-tronic domain by routers at every intermediate node. Sucha network configuration is referred to as lightpath non-bypass. Conversely, IP traffic can also be groomed in theelectronic domain by routers and directly switched in theoptical domain by intermediate OXCs, i.e., IP flows can di-rectly bypass intermediate routers along an end-to-endlightpath to reach their destinations. This network configu-ration is called lightpath bypass (see Fig. 1). The lightpathbypass strategy can save a large number of router ports atthe intermediate nodes compared to the case of lightpathnon-bypass. Since the router ports are the major energyconsumers in the IP over WDM networks, the bypass strat-egy can significantly reduce energy consumption comparedto its non-bypass counterpart [11].

B. Router Card Sleeping

In the design of an IP over WDM network, a large num-ber of IP router ports may be required, which are provi-sioned by different router cards. Consequently, becausethe traffic demands between node pairs fluctuate at differ-ent times, a situation can occur where all the router portson a router card are free of traffic. In that case, an energy-saving option will be to allow us to sleep the entire routercard. An important condition on router-card sleeping isthat not one of the contained router ports carries any traf-fic. Otherwise, the card has to be active. In addition, when-ever a router card sleeps, all the transponders connected tothe router ports in the optical layer can also sleep to saveenergy.

To maximally save energy consumption under therouter-card sleeping strategy, it is very important to effi-ciently allocate the router ports on different router cardsto the optical channels on different virtual links. We usethe example in Fig. 2 to illustrate how different router-portallocations can affect router-card sleeping opportunities.Here we assume that node A has two virtual links con-nected to nodes B and C, respectively. Meanwhile, eachrouter card is assumed to contain four ports. At the peakhour, we assume that virtual links (A–B) and (A–C) bothcontain four optical channels, and thus, four router portsshould be allocated to carry traffic on each of the virtuallinks, which corresponds to a total of eight ports atnode A and requires two router cards to provision the eightports.

We can have two possible ways to allocate the eight portson the two cards to the optical channels on the two virtuallinks [26]. Figure 2(a) shows the first scheme, i.e., scheme

(1), which allocates all the ports on a common router card tothe optical channels on a common virtual link. That is, allthe optical channels on virtual link (A–B) are allocatedwith the four ports on the first card, and all the opticalchannels on virtual link (A–C) are allocated with the fourports on the second card. In contrast, as a second option, wemay also allocate the router ports in the way shown inFig. 2(b), i.e., scheme (2), which allocates the router portson the two cards to the optical channels on the virtual linksin an interleaving mode. As shown in Fig. 2(b), all the oddports on each card are allocated to the optical channels onvirtual link (A–B), and all the even ports are allocated tothe optical channels on virtual link (A–C). As a result, wecan see that there is a mixture of router ports allocated tothe optical channels destined to different nodes on each ofthe router cards. Next, we describe how different router-port allocation strategies can affect energy saving underthe router-card sleeping strategy.

At the peak hour, because all the router cards are fullyloaded and must be active, the two schemes consume thesame amount of energy. However, at low traffic demand (as-suming that the traffic demand is only half of the peak-hour demand, only two router ports are required for eachof the destination nodes B and C), the two schemes showdifferent energy-saving capabilities. Specifically, underscheme (1) as shown in Fig. 2(c), the same amount of energyis consumed as that of the peak hour, as no router cardcan sleep (since there is at least one active port on eachof the cards). In contrast, under scheme (2) as shown inFig. 2(d), the second card can sleep, as there is no trafficon any port of the second card. As a result, half of theenergy relative to the peak hour can be saved. The aboveexample clearly demonstrates that an efficient router-portallocation strategy plays an important role in networkenergy saving when applying the router-card sleepingstrategy.

IP layer

Optical layerEDFA

Physical link

Transponder

OXC

Router

Fig. 1. Architecture of the IP over WDM network with lightpath bypass.

To Node B

To Node C

(a) (b) (c) (d)

Fig. 2. Comparison of router-port allocation strategies:(a) scheme (1), (b) scheme (2), (c) scheme (1) under low trafficdemand, and (d) scheme (2) under low traffic demand.


III. OPTIMIZATION MODELS FOR ENERGY-MINIMIZED DESIGN

OF AN IP OVER WDM NETWORK

The problem of energy-minimized IP overWDM networkdesign in the current study consists of the virtual topologydesign in the optical layer and router-port allocation for dif-ferent optical channels on the virtual links under therouter-card sleeping strategy. The virtual topology designproblem can be formulated as an MILP model [11,27,28],and the “port-allocation” problem discussed above can alsobe formulated as an ILP model [26]. In this section, we de-scribe all these MILP models.

A. Problem Statement

We aim to design an IP over WDM network that con-sumes the least energy in a day by jointly applying thelightpath bypass and router-card sleeping strategies. Thefollowing inputs are given:

1) A physical topology Gp � �N;E�, which consists of aset of nodes N and links E. The node set correspondsto IP routers and OXCs. Within a single node, an IProuter is connected to an OXC via SR interfaces. Thelink set consists of the physical fiber links in thenetwork.

2) A forecast traffic demand matrix �Λt� in each time slot(one time slot per hour is assumed), in which each traf-fic demand element Λsd;t indicates the traffic demandbetween node pair �s; d� in time slot t. Particularly,we define Λsd as the traffic demand between node pair�s; d� at the peak hour.

3) The number of wavelength channels carried by each fi-ber is assumed to be L, and the capacity of each wave-length and each router port is assumed to be B Gbits/s.

4) Other given inputs include the power consumption ofeach router card PL, the power consumption of eachtransponder Ptr, and the power consumption of eachEDFA Pe.

All these inputs function as the given parameters of theoptimization problem. The optimization objective of theproblem is to minimize the total energy consumption ofan IP over WDM network in 1 day. The constraints ofthe problem include 1) fully serving all the traffic demandsbetween node pairs in each time slot, 2) a limited maximalnumber of wavelengths on each fiber (however, no limit isset on the number of fibers deployed on each physical link),3) the constraint of wavelength continuity that requireseach end-to-end lightpath (or optical channel) to use thesame wavelength on all the traversed fiber links, 4) the lim-ited transmission capacity of each router port or wave-length, and 5) a limited number of router ports on eachrouter card (each router card contains four router portsin this study).

The problem aims to find 1) an optimal virtual topologyin the optical layer, 2) RWA in the optical layer, 3) the num-bers of used wavelengths, fibers, and EDFAs required oneach physical link, 4) the router-port allocation status on

each router card at each node, and 5) the router card status(active or sleeping) in different time slots at each node.

B. Other Terms

We define additional terms as follows:

Indices: The following indexing rules are applied for theoptimization model:

m and n Node indices in the physical topology Gp. Aphysical link connects two such end nodes,and they are neighboring nodes in the physicaltopology.

i and j Node indices in the virtual topology. A virtuallink connects two such end nodes, which isa pair of IP routers connected by the virtuallink.

s and d Indices of the source and destination nodes ofend-to-end user traffic demand. This demandis routed over the virtual topology in the opti-cal layer.

Sets and parameters: In addition to all the parametersintroduced in Subsection III.A, we define other sets andparameters as follows:

Nm Set of neighboring nodes of node m in the physicaltopology Gp

W Set of wavelengths on each fiber link.T Set of time slots within 1 day. We divide 1 day into 24

time slots, which means that each time slot corre-sponds to 1 h.

CIi Set of router cards at node i (because the exact num-ber of router cards at each node is not preknown be-fore a design, sufficient router cards are assumed ateach node).

PI Set of router ports on each router card (withoutlosing generality, we assume that each router cardcontains four router ports).

Lmn Distance of the physical link between nodesm and n.This distance can be used to determine the numberof required EDFAs on each fiber link.

Amn Number of EDFAs that should be deployed on eachfiber of physical link �m;n�. Specifically,Amn � ⌈�Lmn∕S� − 1⌉� 2, where S is the span dis-tance between two neighboring in-line EDFAs and⌈�Lmn∕S� − 1⌉ is the number of in-line EDFAs re-quired on the link. The “2” counts a postamplifierand a preamplifier at the two ends of a fiber link.

Ω Number of router ports on a router card (we assumethat there are four ports on each card, i.e., Ω � 4, inthis study).

Δ A large value.

Variables:

λsdij Amount of user traffic demand between node pair�s; d� that traverses virtual link �i; j� at the peak hour(real)


λsd;tij Amount of user traffic demand between node pair�s; d� that traverses virtual link �i; j� at time slot t(real).

vijl Number of optical channels that are establishedon virtual link �i; j� and use wavelength l (integer).

Vij Number of optical channels established on virtuallink �i; j� at the peak hour (integer).

Vtij Number of optical channels required on virtual link

�i; j� to serve the user traffic demand in time slot t(integer).

wijmnl Number of optical channels on virtual link �i; j� that

traverse physical link �m;n� and use wavelength l(integer).

f mn Number of fibers that should be deployed on physicallink �m;n� (integer).

Ci Number of router cards that should be deployedat node i for the peak-hour traffic demand(integer).

Cti Number of router cards that should be active to serve

the traffic demand in time slot i (integer).xabij A binary indicating whether the bth router port

on the ath router card at node i is occupied for estab-lishing an optical channel on virtual link �i; j�(see Fig. 3).

xab;tij A binary indicating the status (active or sleeping) ofthe bth router port on the ath router card in time slott. The port has been allocated to establish an opticalchannel on virtual link �i; j�.

zai A binary indicating whether the ath router card atnode i is used at the peak hour (because enoughrouter cards are assumed to be deployed at eachnode, some cards may not be used in the final design.In that case, all these router cards should be re-moved; thus, zai � 0 means that the router card isnot deployed).

za;ti A binary indicating the status (active or sleeping) ofthe ath router card at node i in time slot t.

C. MILP Models

1) Joint MILP Model: We develop a joint MILP model tominimize the energy consumption of the IP over WDM net-work in 1 day by jointly applying the lightpath bypass and

router-card sleeping strategies. The MILP model concen-trates on minimizing the total energy consumption contrib-uted by different active network components including IProuters (the sum of the energy consumption of all routercards as the total energy consumption of a router [11]),WDM transponders, and EDFAs over the whole day, duringwhich some components may sleep in low-traffic-demandhours. Though a sleeping router card can still consume asmall amount of energy [29], in this study we ignore thisenergy consumption when computing the total energyconsumption of a network. In addition, we assume thatall EDFAs do not sleep to ensure the connectivity of net-work nodes and that when a router card sleeps, all its con-nected optical transponders sleep as well. The number offibers on each physical link (i.e., f mn) is a variable. Therecan be multiple fibers required on each link between twonodes depending on the traffic demand or the optical chan-nels traversing the link. Thus, the total number of EDFAsdeployed and corresponding total energy consumption arenot constant for different design scenarios under differenttraffic demands. Mathematically, we present the jointMILP model as follows:

Objective:Minimize the total network energy consump-tion of the whole day:

Xt∈T

Xi∈N

Cti · PL �

Xt∈T

Xi∈N

Ω · Cti · Ptr

�Xt∈T

Xm∈N

Xn∈Nm

Amn · f mn · Pe. (1)

Constraints:

– On virtual topology establishment:

Xj∈N∶i≠j

λsdij −X

j∈N∶i≠jλsdji �

8>><>>:Λsd i � s

−Λsd i � d

0 otherwise

∀ s; d; i ∈ N∶s ≠ d; (2)

Xj∈N∶i≠j

λsd;tij −X

j∈N∶i≠jλsd;tji �

8>><>>:Λsd;t i � s

−Λsd;t i � d

0 otherwise

∀ s; d; i ∈ N∶s ≠ d; t ∈ T; (3)

Xs∈N

Xd∈N∶s≠d

λsdij ≤ B · Vij ∀ i; j ∈ N∶i ≠ j; (4)

Xs∈N

Xd∈N∶s≠d

λsd;tij ≤ B · Vtij ∀ i; j ∈ N∶i ≠ j; t ∈ T; (5)

λsd;tij � λds;tji ∀ i; j; s; d ∈ N; t ∈ T∶i ≠ j; s ≠ d; (6)

Router in node A

To Node B

To Node C

LC1 LC2 LC3

Fig. 3. Example of router-port allocation. [The third routerport on the third router card is used to establish virtual link(A–B).]


(λsd;tij � λsdij t� tpeakλsd;tij ≤ λsdij otherwise

∀ i; j; s;d ∈N;t ∈ T∶i ≠ j; s ≠ d;

(7)

Vtij � Vt

ji ∀ i; j ∈ N; t ∈ T∶i ≠ j; (8)

(Vt

ij � Vij t � tpeakVt

ij ≤ Vij otherwise ∀ i; j ∈ N; t ∈ T∶i ≠ j; (9)

Xl∈W

vijl � Vij ∀ i; j ∈ N∶i ≠ j: (10)

– On physical topology:

Xn∈Nm

wijmnl −

Xn∈Nm

wijnml �

8>><>>:vijl m � i

−vijl m � j

0 otherwise

∀ i; j;m ∈ N∶i ≠ j; l ∈ W; (11)

Xi∈N

Xj∈N∶i≠j

wijmnl ≤ f mn ∀ m ∈ N;n ∈ Nm; l ∈ W: (12)

– On router-port allocation:

Xa∈CIi

Xb∈PI

xabij � Vij ∀ i; j ∈ N∶i ≠ j; (13)

Xa∈CIi

Xb∈PI

xab;tij � Vtij ∀ i; j ∈ N∶i ≠ j; (14)

Xj∈N∶i≠j

xabij ≤ 1 ∀ i ∈ N;a ∈ CIi; b ∈ PI; (15)

xab;tij ≤ xabij ∀ i; j ∈ N∶i ≠ j; a ∈ CIi; b ∈ PI; t ∈ T; (16)

Xa∈CIi

zai � Ci ∀ i ∈ N; (17)

Xa∈CIi

za;ti � Cti ∀ i ∈ N; t ∈ T; (18)

Δ · zai ≥X

j∈N∶i≠j

Xb∈PI

xabij ∀ i ∈ N;a ∈ CIi; (19)

Δ · za;ti ≥X

j∈N∶i≠j

Xb∈PI

xab;tij ∀ i ∈ N;a ∈ CIi; t ∈ T; (20)

�za;ti � zai t � tpeakza;ti ≤ zai otherwise

∀ i ∈ N;a ∈ CIi; t ∈ T: (21)

Explanations of the equations:

– Equation (1) is the objective function for minimizing thetotal energy consumption in a day.

– Constraints (2) and (3) ensure the conditions of flow con-servation in the IP layer at the peak hour and duringeach time slot, respectively.

– Constraints (4) and (5) ensure that each virtual link hassufficient capacity to carry the traffic flows at the peakhour and during each time slot, respectively.

– Constraint (6) ensures that IP traffic flows are bidirec-tional in each time slot and are corouted on the optical-layer virtual topology. In a backbone network, it isreasonable to make such an assumption.

– Constraint (7) says that all the traffic flows in differenttime slots follow the same routes that are established forthe peak-hour traffic. By doing this, we canminimize net-work reconfiguration and thus make the scheme morepractical. Of course, it is also possible to reconfigurethe user traffic flows along different virtual links in dif-ferent time slots. This may, however, cause overload inthe network control plane, because of the rerouting ofmany user traffic flows.

– Constraint (8) means that the virtual links in the opticallayer are bidirectional in each time slot.

– Constraint (9) means that the required capacity on eachvirtual link in different time slots should never exceedthe capacity required at the peak hour. Similar to con-straint (7), this constraint can ensure the stability ofthe optical channel layer, not requiring any virtual top-ology reconfiguration.

– Constraint (10) counts the total number of wavelengthchannels on each virtual link.

– Constraint (11) ensures the condition of flow conserva-tion in the optical layer.

– Constraint (12) ensures that a sufficient number of fibersare deployed on each physical link so as to provide a suf-ficient number of optical channels that are of the samewavelength. (This constraint can also count the totalnumber of fibers deployed on the physical link.)

– Constraints (13) and (14) count the total number ofrouter ports required for establishing the optical chan-nels on each virtual link at the peak hour and duringeach time slot, respectively, (i.e., an end-to-end opticalchannel or lightpath is allocated with a router port).

– Constraint (15) ensures that each router port is onlyallocated to a single optical channel.

– Constraint (16) says that only if a router port is allocatedto establish an optical channel at the peak hour can it beactive or sleep in different time slots.

– Constraint (17) counts the total number of deployedrouter cards at the peak hour at each node.

– Constraint (18) counts the total number of active routercards at each node in each time slot t.


– Constraints (19) and (20) tell whether or not a routercard should be active at the peak hour and during differ-ent time slots, respectively. Note that if any router porton a router card is active, the router card must be active.

– Finally, constraint (21) means that the router cardsdeployed for the peak hour can be active or sleeping.

There are also several additional constraints that do notaffect the result but can accelerate the computation of theMILP model because they can reduce the feasible solutionregion for the model. They include

λsdij � λdsji ∀ i; j; s; d ∈ N∶i ≠ j; s ≠ d; (22)

Vij � Vji ∀ i; j ∈ N∶i ≠ j; (23)

Xa∈CIi

Xb∈PI

Xj∈N

xabij ≤ Ω · Ci ∀ i ∈ N; (24)

Xa∈CIi

Xb∈PI

Xj∈N

xab;tij ≤ Ω · Cti ∀ i ∈ N; t ∈ T; (25)

�Ct

i � Ci t � tpeakCt

i ≤ Ci otherwise ∀ i ∈ N; t ∈ T: (26)

Explanations of these constraints:

– Constraint (22) says that the traffic flows in the IP layerare bidirectional and corouted on the optical layer at thepeak hour. This constraint is implied in constraint (6).

– Constraint (23) means that the virtual links in theoptical layer are bidirectional at the peak hour. This con-straint is implied in constraint (8).

– Constraints (24) and (25) ensure the deployment of suf-ficient router cards such that sufficient router ports canbe provided to carry traffic demand in different timeslots. These two constraints are redundant given con-straints (17)–(20).

– Constraint (26) means that total number of active routercards should never exceed the total number of deployedrouter cards for the peak-hour traffic. This constraint isalso redundant given constraint (21).

2) Detached MILP Models: The joint MILP optimizationmodel jointly considers the lightpath bypass and route-card sleeping strategies, which can ensure a globalenergy-minimized design for an IP over WDM network.However, the joint effort makes the problem very compli-cated and not solvable even for a medium-sized network.To alleviate computational complexity, we divide the jointoptimization problem into two subproblems and solve eachof them sequentially to obtain a suboptimal solution.

The first subproblem is to establish a virtual topologywith the objective of minimizing the power consumptionof the IP overWDMnetwork at the peak hour by employing

the lightpath bypass strategy. The second subproblem is toallocate router ports on different router cards to the opticalchannels on different virtual links and, through sleepingand waking up the router cards in different time slots,to minimize the energy consumption for the whole day.

The two subproblems can be formulated as two separatebut correlated MILP optimization models. In particular,the first one is to establish a virtual topology with the con-straint of wavelength continuity and to output the numberof fiber links in the physical topology, the number of opticalchannels on each virtual link, and the number of routercards deployed at each node. (This still does not decidehow the router ports of the router cards are allocated toestablish the optical channels on each virtual link. Thiswill be decided in the next subproblem.) Taking the outputof the first MILP model as an input, the second MILPmodel optimally allocates each router port on the routercards to each of the optical channels (we call such a process“router-port allocation”) to minimize the total energy con-sumption for 1 day when the router-card sleeping strategyis considered.

Mathematically, the first MILP model is as follows:

Objective:Minimize the total network energy consump-tion at the peak hour:

Xi∈N

Ci · PL �Xi∈N

Ω · Ci · Ptr �Xm∈N

Xn∈Nm

Amn · f mn · Pe: (27)

The constraints are the same as those of the first twoparts (i.e., on virtual topology establishment and on physi-cal topology) in the joint model, including constraints (2),(4), and (10)–(12). Constraints (22) and (23) are also re-quired to ensure that the traffic flows and virtual links arebidirectional at the peak hour, respectively. In addition, onemore constraint is required to ensure that there are suffi-cient router cards deployed at each node at the peak hourfor virtual link establishment:

Xj∈N

Vij ≤ Ω · Ci ∀ i ∈ N: (28)

The first MILP model establishes a virtual topology thatrequires the smallest number of router cards and thus con-sumes the least energy at the peak hour. The solution to themodel outputs the number of router cards deployed at eachnodeCi, the number of optical channels on each virtual linkVij, and the number of fibers on each physical link f mn. Theoutput is then used as the input to the second MILP model,which assigns router ports on the router cards to each of theoptical channels on the virtual links to minimize the totalenergy consumption for the whole day when the router cardsleeping strategy is applied. The second MILP model is asfollows:

The objective function is Eq. (1).

The related constraints include constraints (13)–(16),(18), and (20) of the joint model.

In the detached models, as the first step, we haveemployed the peak-hour traffic demand design for the


optical-layer virtual topology. Then, based on the found vir-tual topology and required router ports, we employ the portallocation strategy as the second step to allocate all theports for maximal energy saving in different time slots.Thus, constraints (3) and (5)–(9) are not required in thefirst MILP model, and constraints (17), (19), and (21) arenot required in the second MILP model, in which Vt

ij be-comes a parameter, given that Vij for the peak-hour trafficis found in the first step and traffic variation ratios in dif-ferent time slots are provided.

3) Reconfigurable and Fixed Split Ratio Joint MILPModels: To find the lower bound on the energy consump-tion of IP over WDM networks with the joint lightpathbypass and router-card sleeping strategy, we also developan MILP model (referred to as a reconfigurable MILPmodel) that can freely reconfigure the virtual topology aswell as port allocation independently in each time slot.

In addition, under constraint (7) in the previous jointMILP model, though we can ensure that all the IP flowscan follow the same routes, they are not guaranteed withfixed split ratios in different time slots if the traffic demandbetween a pair of nodes goes through multiple differentpaths. This means that we need the network control planeto reconfigure the split ratios on multiple routes in differ-ent time slots. In order to further simplify network control,we propose an MILPmodel that can guarantee a fixed splitratio between multiple routes for each IP traffic flow.Specifically, we use constraint (29) to substitute for con-straint (7), in which the ratio of the traffic demand in timeslot t and the peak-hour traffic demand, Λsd;t∕Λsd, is incor-porated to ensure the fixed split ratios between IP trafficflows on different routes in different time slots. Constraint(29) is as follows:

(λsd;tij � λsdij t� tpeakλsd;tij � Λsd;t

Λsd · λsdij otherwise∀ i; j; s;d ∈N; t ∈ T∶i ≠ j; s ≠ d:

(29)

IV. HEURISTIC APPROACHES

For computational complexity, the joint MILP model hasa total of O�L ·N4� variables and O�T ·N4� constraints,where N is the total number of nodes in a network, L isthe number of wavelengths in a single fiber, and T is thenumber of time slots in a day (L � 80,T � 24 in this study).For a largeN, there are huge numbers of variables and con-straints. For example, ifN � 100, there are a total of about8 × 109 variables and 2.4 × 109 constraints, which makesthe problem intractable. Similar computational difficultyalso occurs in the detached models, even though theyare computationally much simpler than the joint model.The first detached model has a total of O�L ·N4� variablesand O�N4� constraints, and the second model has a total ofO�T · Ω · CI ·N2� variables and O�T ·Ω · CI ·N2� con-straints, where Ω is the number of router ports on eachrouter card and CI is the number of router cards deployedat each network node. Thus, for the design of a large-size

network, efficient heuristics are still required for a fast sol-ution. Next, we propose two efficient heuristics for theproblem.

In both of the heuristic algorithms, there are two keysteps, which correspond to the two subproblems in the de-tached MILP models. The first step is to establish a virtualtopology that aims to minimize the total number of re-quired router ports (also, the number of router cards), tran-sponders, and EDFAs by employing the lightpath bypassstrategy. The output of the first step includes the numberof required router cards at each node and the number ofoptical channels on each virtual link. With this informa-tion, the next step assigns the router ports on the routercards to each of the optical channels on different virtuallinks, aiming to minimize the energy consumption for 1day with the router-card sleeping strategy applied.

In the first step, we employ the “multihop bypass” algo-rithm, which was developed in our previous work [11], toestablish an energy-efficient virtual topology. The algo-rithm allows traffic demands between different node pairsto share capacity on a common optical channel so as to im-prove capacity utilization. Minimizing the number of opti-cal channels can minimize the total number of requiredrouter ports and transponders, thereby reducing the en-ergy consumption. Our previous work has proved thatthe above “multihop bypass” algorithm is efficient in thedesign for an energy-minimized IP over WDM networkat the peak hour [11].

Specifically, we implemented the following two steps:1) divide the whole IP traffic demand matrix into two ma-trices, with the first containing an integral number of op-tical channel capacity B and the second containing all theremaining traffic, whose amount is smaller than B; 2) forthe first matrix, employ the single-hop bypass strategy todirectly establish lightpath virtual links between nodepairs, while for the second matrix, implement the multihoptraffic grooming to use the remaining capacity on consecu-tive optical-channel virtual links to serve low-rate IP trafficflows between node pairs.

After we find the number of required router cards in thefirst step, the second step is to assign a router port on thecards to each of the optical channels on different virtuallinks. Different router-port assignment strategies can beapplied, which will lead to varying energy-saving capabil-ity when the router-card sleeping strategy is applied.Figure 2 shows a good example of how different router-portallocation strategies can affect energy consumption undertime-dependent traffic demands.

The first router-port allocation strategy is straightfor-ward and sequentially allocates the router ports on eachrouter card to the optical channels on different virtuallinks, as shown by scheme (1) in Fig. 2. The detailed assign-ment steps of this strategy are shown by the flow chart inFig. 4. For each node, we first arrange all the virtual linksto different destination nodes in R according to the numberof optical channels contained from the largest to the small-est. In the second step, we assign the router ports on therouter cards sequentially to each of the optical channels[from the left to the right and from the top to the bottom


as shown in Fig. 2(a)] until all the optical channels are as-signed with router ports. We call such a method the “se-quential mode.” This method is advantageous for itsintuition and simplicity. However, allocating optical chan-nels on the same virtual link with the router ports on acommon router card can cause some inconvenience whensleeping router cards at nonpeak hours, as shown byscheme (1) in Fig. 2, i.e., a router card cannot sleep becausethere is still a partial set of active router ports on the card.

To overcome the above disadvantage, we also propose aninterleaving allocation method that allocates the opticalchannels on a common virtual link with the router portsfrom different router cards. Please see the example inFig. 2(b), wherein the optical channels on a virtual link(link A–B or A–C) are allocated in an interleaving modewith the router ports on different router cards. We call sucha method the “interleaving mode,”whose detailed steps areshown by the flow chart in Fig. 5. Specifically, we first ar-range all the virtual links to different destination nodes inR according to the number of optical channels containedfrom the largest to the smallest. In the second step, we scanthe ordered virtual link list R. If a virtual link has opticalchannels that are not allocated with router ports yet, weget the first unassigned optical channel and allocate thefirst unused router port on the router cards (counted fromthe left to the right and from the top to the bottom) to theoptical channel. Then, we move to the next virtual link inR. Once all the virtual links are processed as in the secondstep, we restart the scanning process (i.e., restart the sec-ond step) from the first to the last virtual link in R until allthe optical channels on all the virtual links in R are allo-cated with router ports.

The interleaving allocation approach implements a fullinterleaving router-port allocation mode, which, however,may not be necessary under some situations. This is be-cause even in the time slot with the lowest traffic demand,there are still a certain number of active optical channels

between any pair of nodes. This implies that in any timeslot there are a certain number of router ports that shouldalways be on to support the above active optical channels.Since these router ports are always active all the time, it isefficient to employ the sequential allocation mode to gatherthem on common router cards so that these router cardsare always on with all the contained ports active all thetime. For the remaining optical channels and associatedrouter ports, we can apply the interleaving allocationmode, which can take advantage of router-card sleeping.

For optimal router-port allocation, we propose a mixedrouter-port allocation strategy (called “mixed mode”), asshown in Fig. 6. In the algorithm, we define a key termcalled the interleaving ratio r, which means that amongall the router ports there are r percent allocated in the in-terleaving mode, while all the remaining ports are allo-cated in the sequential mode. Because it is difficult topredict an optimal interleaving ratio r under which theIP over WDM network will consume the least energy in1 day, we propose scanning different interleaving ratioswith 5% as a decreasing step for r. Specifically, we startwith an initial interleaving ratio r equal to 100% (i.e.,r � 1), which means that we allocate optical channels withrouter ports all in an interleaving mode [see scheme (2) inFig. 2]. Based on such a ratio, we assign router ports andcalculate the total network energy consumption for 1 daywith sleeping or waking up router cards and transpondersin different time slots. Next, step by step, we decrease theinterleaving ratio r by 5% each time, and for each interleav-ing ratio, we evaluate the total network energy consump-tion until the interleaving ratio becomes zero, whichcorresponds to the case where all the router ports are allo-cated sequentially. Finally, we compare the total networkenergy consumption at the different interleaving ratiosto select one that demonstrates the least energy consump-tion and output its corresponding energy consumption.

There exist port interleaving approaches across linecards for better survivability in the industry [30]. However,such interleaving approaches differ from the current

Fig. 4. Flow chart of sequential router-port allocation. Fig. 5. Flow chart of interleaving router-port allocation.


interleaving effort in the following aspects: 1) the mainpurposes of the two types of interleaving approaches aredifferent; the former is for the purpose of better networksurvivability, while the latter is for the purpose ofrouter-card sleeping and better energy saving. 2) Thoughthe interleaving for survivability would also consider carddisjointness between the ports that are on a common end-to-end route, in which some blindness or randomness couldexist in port allocation, the interleaving strategy in this pa-per aims to minimize the energy consumption, in which anoptimal interleaving ratio that requires a certain percent-age of router ports to interleave (while the remaining portsare not implemented with interleaving) so as to maximizethe router-card sleeping opportunity exists. In summary,though the interleaving approach for the survivabilitypurpose does already provide opportunity for router-cardsleeping, the strategy is not optimal to ensure maximal en-ergy saving, and therefore it is important to develop a dedi-cated and more efficient interleaving strategy, as proposedin this paper.

V. STUDY CASES AND TEST NETWORKS

To evaluate the performance of the different design ap-proaches, we considered five study cases. The first fourcases allow router-card sleeping, including 1) the jointMILP optimization design (“joint model,” for short),2) the detached MILP optimization design (“detachedmodel,” for short), 3) the heuristic algorithm with router-port allocation in the mixedmode (“mixedmode,” for short),and 4) the heuristic algorithm with router-port allocationin the sequential mode (“sequential mode,” for short).The final case (i.e., case 5) does not allow router-card sleep-ing (“nonsleeping,” for short).

Case 1 employs the MILP optimization model fromEqs. (1) to (26) to obtain an optimal solution with the jointcontributions of lightpath bypass and router-card sleeping.Case 2 is a suboptimal case of the original joint model thatdivides the design problem into two subproblems, i.e., 1)

establishing a virtual topology with lightpath bypassand 2) allocating router ports to the optical channels oneach virtual link to achieve the lowest energy consumptionunder the router-card sleeping strategy. The detached ap-proach contains two separate but correlated MILP models,of which each finds an optimal solution to their correspond-ing subproblems. Such an approach, however, cannot guar-antee a global optimal solution to the problem. Cases 3 and4 correspond to the two heuristic algorithms introduced inSection IV. Case 5 considers the energy-minimized designfor the IP overWDM network without router-card sleeping,which only designs for the peak-hour traffic with the light-path bypass strategy.

Five test networks were considered, including 1) afour-node, five-link (n4s5) network, 2) a six-node, eight-link (n6s8) network, 3) the 11-node, 26-link COST239 net-work, 4) the 14-node, 21-link National Science FoundationNetwork (NSFNET), and 5) the 24-node, 43-link U.S. back-bone network (USNET). These networks are shown inFig. 7, in which the physical distance of each link (kilo-meter) is indicated near the link. All the results wereobtained each with a single run based on a randomly gen-erated demand matrix, and the MIPGAP of all the MILPmodels is 1%.

We employed the AMPL/Gurobi software package(version 5.0.0) [31] to solve all the MILP models on a64 bit server with a 2.4 GHz CPU and 8 Gbyte memory.It took a long time to solve the models due to the high com-putational complexity of the problem. Specifically, for then4s5 network, it took more than a week to solve the jointmodel with 1% MIPGAP, and for the n6s8 network, it tookabout 1 day to solve the detached model. For all the othernetworks, we cannot solve the MILP models.

In addition, the following inputs were assumed:

1) The traffic demand between each pair of nodes at thepeak hour is randomwith a uniform distribution withina certain range, which is centered at an identical aver-age. More specifically, given an average demand inten-sity X ∈ f20;40;…; 120g Gbits∕s, the actual demand atthe peak hour between a pair of nodes is generated by arandom function uniformly distributed within therange f10;2X − 10g Gbits∕s.

2) We assume that the traffic demand varies according to aday–night pattern [12], as shown in Fig. 8. The patternis approximated to be a simple sinusoidal function as

λsd;t � λsd�

1 − ρ

2�1� sin�f 0�t − 8�� ρ

�;

where f 0 � π∕12. We also set ρ � 0.2, which means thatthe lowest traffic demand is equal to 20% of the peak-hour traffic demand. Each time slot corresponds to 1 h.

3) The physical distance between two neighboring in-lineEDFA amplifiers is assumed to be 80 km, and for anyfiber link, by default there is always a pair of post- andpre-EFDA amplifiers at the two ends of each link.

4) Maximally, 80 wavelengths are assumed to be multi-plexed in each fiber [32,33], and there is no limit on

Fig. 6. Flow chart of router-port allocation in the mixed mode.


the total number of fibers laid on each physical link.The transmission capacity of each wavelength is10 Gbits∕s [33], and each router card contains four

10 Gbits∕s router ports. Note that with more advancedrouter technology, we may also consider higher speedssuch as 40 Gbits∕s. Nonetheless, the methodology ap-plied for the design is the same.

5) According to the Cisco eight-slot CRS-1 router datasheet [24], the average power consumption of eachrouter card is assumed to be 606 W, and according tothe Cisco 10 Gbit∕s Multirate Enhanced TransponderCard in the ONS 15454 Multiservice Transport Plat-form [34], each 10 Gbit∕s WDM transponder is as-sumed to consume 50 W. The power consumption ofeach EDFA is assumed to be 8 W [11].

VI. RESULT ANALYSES

In this section, we provide our simulation and optimiza-tion results and carry out some performance analyses.

A. Energy Consumption of 1 Day

In this section, we compare the total energy consumptionin 1 day by the different approaches. Figure 9 first com-pares the results of energy consumption in 1 day by differ-ent joint MILP models in the n4s5 network. We compared1) the case that allows for full reconfigurations of virtualtopology and port allocation (i.e., “reconfigurable model”),2) the case where the IP traffic flows between node pairsmust be on the same routes and have fixed split ratios iftraversing multiple routes in different time slots (i.e.,“fixed split ratio model”), and 3) the case where the IP traf-fic flows between node pairs must be on the same routesand can have variable split ratios if traversing multipleroutes in different time slots (i.e., “variable split ratiomodel”). As expected, though the performance differenceis small, the “reconfigurable model” curve provides a lowerbound on the total energy consumption, the “fixed split ra-tio model” consumes the largest amount of energy, and the“variable split ratio model” lies between them. This is ex-plainable, because the “reconfigurable model” has the high-est flexibility in reconfiguring the virtual topology and portallocation in different time slots. However, the “fixed split

Fig. 7. Test networks.

Fig. 8. Daily traffic variation.


ratio model” shows the strongest limitation on the splitratios of the traffic flows on different fixed routes in allthe time slots. The flexibility of the “variable split ratiomodel” lies between the previous two, thereby achievingperformance in the middle as well. Though the “reconfigur-able model” shows the best performance, from a networkcontrol point of view, it needs the most complicated net-work control in both rerouting and re-allocation of the splitratio. In contrast, the “fixed split ratio model” is the sim-plest, as it does not need to change any network parame-ters. As an intermediate case, the “variable split ratiomodel” is still quite simple in terms of network control,since it only needs to change the split ratios of the IP flowson different routes. Thus, we can see that there is a trade-off between network control complexity and network per-formance optimality. As for which strategy or design shouldbe used, it is up to the network operator and is outside thescope of this study. However, as a representative of the jointoptimization models, in the following performance com-parison with the other approaches, we will always usethe “variable split ratio model,” and moreover, for simplic-ity we will call the model the “joint model.”

Figure 10(a) shows the results of energy consumption in aday by the n4s5 network. The curve “nonsleeping” providesan upper bound on the total energy consumption, and thecurve “joint model” provides a lower bound on the totalenergy consumption. Also, to show the energy-consumptionsaving capabilities of the different approaches, we provideenergy-saving percentages of all the sleeping cases relativeto the nonsleeping case in Fig. 11(a).

We can see that the joint MILP optimization model per-forms best and the detached approach follows. By applyingthe router-card sleeping strategy, we can maximally saveenergy consumption by up to more than 40% in 1 day underthe joint MILP model compared to the nonsleeping case.The detached approach can also save energy consumptionby between 26% and 35%, and the two sleeping-enabledheuristic algorithms can save energy consumption by upto 28% and 33%, respectively. All these results thus verifythat the router-card sleeping strategy is effective for

Fig. 9. Comparison of total energy consumption in 1 day by differ-ent joint MILP models for the n4s5 network.

Fig. 10. Comparison of total energy consumption of 1 daybetween different design approaches.


significantly saving energy consumption for the IP overWDM network.

In addition, comparing the performance of the two heu-ristic algorithms, we see that the algorithm with the mixedrouter-port allocation mode can perform very close to thedetached optimization approach (and sometimes even bet-ter due to the MIPGAP of the MILP solver). This impliesthat the “mixed mode” is very successful in router-port al-location for saving energy consumption for the IP overWDM network under the router-card sleeping strategy.Moreover, the heuristic algorithm based on the “mixedmode” significantly outperforms the algorithm based onthe “sequential mode,” especially under low traffic demand.This means that under the router-card sleeping strategy,allocating router ports to the optical channels on differentvirtual links in an interleaving mode is efficient for reduc-ing energy consumption. However, with the increasing traf-fic demand, this benefit seems to decrease. For example,the algorithm based on the “mixed mode” can save 22%of energy consumption, and as a comparison, the algorithmbased on the “sequential mode” saves no energy comparedto the “nonsleeping” case at 20 Gbits∕s average traffic de-mand. In contrast, when the average traffic demand growsto 120 Gbits∕s per node pair, the energy-saving percentagedifference between the two algorithms is only 4%. Theabove phenomenon can be attributed to the followingcauses.

When the average traffic demand is low, the number ofrouter ports required for the optical channels on each vir-tual link is small (less than four router ports). Under the“sequential mode,” all the router ports (less than four ports)allocated to the optical channels of a common virtual linkmay be from a common router card. Remember the exam-ple shown in Fig. 2. As long as there is at least one activerouter port on a router card, the router card must be active.Since all the optical channels on a virtual link are allocatedwith the router ports of a common router card, in any timeslot there must be at least one router port active (if the val-ley traffic on the virtual link is not zero), which means thatthe corresponding router card cannot be put to sleep in anytime slot [see scheme (1) in Fig. 2]. In contrast, the “mixedmode” employs the interleaving mode, as shown in Fig. 2,scheme (2), which provides the opportunity to allow somerouter cards to be fully slept. Thus, under low traffic de-mand, we see that the “mixed mode” can save more energy.

In contrast, with the increase in traffic demand, morerouter ports (more than four router ports) are requiredfor the optical channels on each virtual link, which leadsto a situation where the router ports allocated to the opticalchannels of a common virtual link exist on more than onerouter card even under the sequential allocation mode.According to the example in Fig. 2, if the router ports dedi-cated to a common virtual link are distributed on severalrouter cards, there are more opportunities to sleep routercards and save energy under fluctuating traffic demands.For example, if there are two router cards dedicated tothe optical channels of a virtual link, and the traffic de-mand on the link reduces to half, then at least one routercard can be put to sleep so that 50% energy is saved.

Fig. 11. Energy consumption savings by the different designapproaches relative to the nonsleeping situation.


However, under low traffic demand and in the sequentialallocation mode [i.e., scheme (1) in Fig. 2], when the trafficdemand reduces to half, the router card cannot sleep andthus no energy can be saved. Since both of the allocationmodes have opportunities for sleeping router cards underhigh traffic demand, their energy consumption amountsbecome less different.

Due to the computational difficulty, we only obtained theresult of the joint MILP model for the smallest n4s5 net-work but could not find solutions to the other, larger net-works. For the same reason, we only solved the detachedMILPmodels for the n4s5 and n6s8 networks. For the otherthree larger networks, COST239, NSFNET, and USNET,only the proposed heuristic algorithms were employed tofind solutions. The results for these networks are shownin Figs. 10(b)–10(e) and Figs. 11(b)–11(e). We can see thatfor all these networks, the router-card sleeping strategy isefficient and saves up to 30% energy consumption com-pared to the nonsleeping situation. Similar saving percent-ages by all the networks can be attributed to the sametraffic demand distribution per node pair for all these net-works. Again, comparing the results of the two heuristicalgorithms, we can see that the “mixed mode” is signifi-cantly more efficient in saving energy than the “sequentialmode.” However, their performance difference becomessmaller with increasing traffic demand.2

B. Lightpath Bypass Versus Non-bypass

We also evaluate the benefit of lightpath bypass for en-ergy saving in the design of IP over WDM networks.Figure 12(a) shows the energy consumption for 1 day ofthe two heuristic algorithms when the lightpath bypassand non-bypass strategies are, respectively, applied tothe n6s8 network. The solid curves show the results forthe case of lightpath bypass, denoted as “bypass” in thelegend. The dotted curves show the results of the case oflightpath non-bypass, denoted as “non-bypass” in thelegend. We can see that the bypass curves perform muchbetter than the non-bypass ones. This means that the light-path bypass strategy can significantly save energy con-sumption for the IP over WDM network. This conclusionis the same as that in [11]. In addition, comparing the per-formance difference between the two heuristics under thedifferent router-port allocation strategies, we can see thatthere is a larger performance difference under the light-path bypass strategy. This implies that under the non-bypass strategy, the router-port interleaving allocation isless beneficial for reducing the energy consumption forthe IP over WDM network. This is reasonable, since thenon-bypass case requires many more router ports andcards than the bypass case, and we have understood that

the benefit of the interleaving allocation strategy becomesweaker if the number of router ports is large enough torequire multiple router cards.

Similar results were obtained for the larger COST239,NSFNET, and USNET networks. As a representative, weshow the results of the NSFNET network in Fig. 12(b),from which we have similar observations to those summa-rized for the n6s8 network. We do not provide the resultsfor the other two larger networks here.

C. Impact of Interleaving Ratio in Router-PortAllocation

According to our previous analyses, the interleaving ra-tio in router-port allocation shows a strong impact on theenergy consumption of an IP over WDM network under therouter-card sleeping strategy. Figure 13 shows the energyconsumption of the n6s8 network for 1 day under differentrouter-port interleaving ratios subject to 40 Gbits∕s aver-age traffic demand per node pair. We can see that underthe lightpath bypass strategy [see Fig. 13(a)], the IP overWDM network consumes the lowest energy when the inter-leaving ratio is 0.8, which means that allocating 20% ofrouter ports sequentially and the remaining router portsin an interleaving mode is the most energy efficient. Inour traffic model, the lowest traffic demand is assumedto be 20% of the peak-hour traffic demand. Since 20%

Fig. 12. Energy consumption in 1 day with lightpath bypass andnon-bypass.

2We made performance comparisons between different schemes in a jointmode that considers both traffic grooming and port allocation. The two stepscan coaffect the final performance of the schemes. To purely evaluate thebenefit of the proposed port allocation schemes, excluding the interferencefrom traffic grooming, we have reported the results in [26], in which ourproposed port allocation schemes are proved to be very efficient.


traffic demand always exists regardless of the time slot in aday, it is efficient to directly allocate 20% of the router portsin the sequential mode on router cards and keep theserouter cards active all the time. Thus, it is not surprisingto see that the minimal energy consumption is achievedwhen the interleaving ratio is 0.8.

In contrast, for the non-bypass case [see Fig. 13(b)], theinterleaving ratio at which the network consumes the leastenergy is 0.65, smaller than 0.8. This can be attributed tothe fact that under the non-bypass situation, there aremore router cards required at each node, and multiplerouter cards can be dedicated to providing the ports forthe optical channels on a common virtual link. As discussedbefore, under a larger number of router cards, the inter-leaving port allocation mode shows less benefit, and thuswe observe that it is more energy-efficient to increasethe ratio of router ports that are allocated in the “sequen-tial mode.”

We also evaluate the impact of the interleaving ratio forthe USNET network. The results are shown in Fig. 14.Under the bypass strategy, we see that similar to the re-sults of the n6s8 network, the optimal interleaving ratiois at 0.8. However, under the non-bypass case, the optimalinterleaving ratio is much lower than that of the n6s8 net-work, which is 0.05, very close to zero. This means that forthe USNET network under the non-bypass strategy, the in-terleaving port allocation mode cannot bring much benefit

to energy saving. This is attributed to the fact that theUSNET non-bypass design can lead to a very large numberof router cards on each node, and moreover, for each virtuallink, there are also more router cards dedicated to accom-modating the router ports allocated to the optical channelsof the virtual link. Such a large number of router cardssignificantly reduces the benefit of the interleaving router-port allocation mode in energy saving under the router-card sleeping strategy. As a result, a very high percentage,95%, of router ports should be allocated in the sequentialmode to achieve the lowest energy consumption.

Figure 15 shows the optimal interleaving ratios underdifferent peak-hour traffic demands. For the lightpath by-pass case, the optimal interleaving ratios are always lo-cated at about 0.8 for the two test networks, n6s8 andUSNET. Since the lowest traffic demand is assumed tobe 20% of the peak-hour traffic demand, it is reasonableto allocate ports for 20% of the traffic demand in a sequen-tial mode and 80% of the traffic demand in an interleavingmode to achieve the best performance. However, for thelightpath non-bypass case, we can see that the optimal in-terleaving ratios become smaller with increasing peak-hour traffic demand in the n6s8 network and stay almostconstant at 5% in the USNET network. The reason is thesame as in the previous analyses, i.e., the non-bypass strat-egy requires a larger number of router cards, which reducesthe benefit of router port interleaving allocation. In then6s8 network, at the beginning, when the traffic demandis low, the benefit of interleaving is stronger; however, withthe increase in traffic demand and the number of router

Fig. 13. Energy consumption in 1 day by the n6s8 network underdifferent interleaving ratios.

Fig. 14. Energy consumption in 1 day by the USNET networkunder different interleaving ratios.


ports, the benefit of interleaving becomes weaker. That iswhy the optimal interleaving ratio decreases with increas-ing traffic demand. Since the USNET is a large network, therequired number of router ports at each node is very largeeven under low traffic demand, such as 20 Gbits∕s per nodepair. The large number of router ports overwhelms the ben-efit of the interleaving port allocation effort. Thus, for theUSNET network, for all the traffic demands, we have an al-most constant optimal interleaving ratio.

VII. CONCLUSIONS

It is important to explore energy-efficient networkingtechniques for future communication networks. This paperjointly employed the lightpath bypass and router-cardsleeping strategies to minimize the energy consumptionof the IP over WDM network over a period of 1 day. Thescheme has the advantage of requiring only simple net-work control when applying router-card sleeping strategy,without requiring rerouting or reconfiguration of the opti-cal channels and IP flows in the optical and IP layers. Wedeveloped the joint MILPmodel, the detached optimizationapproach, and the two heuristic algorithms to designthe energy-efficient IP over WDM network. It is foundthat both the lightpath bypass and router-card sleepingstrategies can significantly reduce energy consumption

for the IP over WDM network, and jointly considering bothof the strategies can result in much better performancethan applying either of the strategies separately. The sim-ulation results for the different test networks show that thejoint MILP design can reduce energy consumption by morethan 40% compared to the nonsleeping case under thelightpath bypass strategy. It is also found that assigningrouter ports in the mixed mode can save more energy con-sumption than the sequential allocation mode. By compar-ing the results of lightpath bypass and non-bypass, we notethat the strategy of lightpath bypass can significantlyreduce total energy consumption of the IP over WDM net-work. Finally, it is interesting to see that the interleavingratio of router port allocation has a strong impact on thedesign of the energy-efficient optical network. For the light-path bypass and non-bypass cases, different optimal inter-leaving ratios exist, at which the most energy-efficient IPover WDM network can be designed.

ACKNOWLEDGMENTS

Part of this work was presented at ACP 2012 [26] and ICCC2012 [35]. This work was supported in part by theNational 863 Plans Project of China (2012AA011302),the National Natural Science Foundation of China (NSFC)(61172057), the Research Fund for the Doctoral Program ofHigher Education of China (20113201110005), the NaturalScience Foundation of Jiangsu Province (BK2012179), andthe Open Fund of the State Key Laboratory of InformationPhotonics and Optical Communications (Beijing Univer-sity of Posts and Telecommunications), China. We wouldlike to thank the reviewers for their valuable commentsthat helped improve the paper and Prof. Sanjay K. Bosefrom I.I.T. (Guwahati) for proofreading the paper.

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Yunlei Lui received his B.Sc. degree from Hainan University,China, in June 2011. Currently, he is a graduate student at theSchool of Electronic and Information Engineering, SoochowUniversity. His research interests include optical communicationnetworks and green optical networks.

Gangxiang Shen [S’98—M’99—SM’12] received his B.Eng.degree from Zhejiang University, China; his M.Sc. degree fromNanyang Technological University, Singapore; and his Ph.D.degree from the University of Alberta, Canada, in January2006. He is a Distinguished Professor with the School of Electronicand Information Engineering of Soochow University in China.Before he joined SoochowUniversity, he was a Lead Engineer withCiena, Linthicum, Maryland. He was also an Australian ARCPostdoctoral Fellow with the University of Melbourne. His re-search interests include integrated optical and wireless networks,spectrum efficient optical networks, and green optical networks.

Dr. Shen is a Senior Member of IEEE. He has authored andcoauthored more than 80 peer-reviewed technical papers. He isa Lead Guest Editor of the IEEE Journal on Selected Areas inCommunications “Special Issue on Next-Generation Spectrum-Efficient and Elastic Optical Transport Networks” and a GuestEditor of the IEEE Journal on Selected Areas in Communications“Special Issue on Energy-Efficiency in Optical Networks.”He is anAssociate Editor of the Journal of Optical Communications andNetworking and an Editorial Board member of Optical Switchingand Networking and Photonic Network Communications. He is aSecretary for the IEEE Fiber-Wireless (FiWi) Integration Techni-cal Subcommittee. He received the Young Researcher New StarScientist Award in the “2010 Scopus Young Researcher AwardScheme” in China. He was a recipient of the Izaak Walton KillamMemorial Award from the University of Alberta and the CanadianNSERC Industrial R&D Fellowship.

Weidong Shao obtained his Ph.D. from the Technological PhysicsResearch Center in Shanghai, Chinese Science and Technology Re-search Institute. He is now an Associate Professor with the Schoolof Electronic and Information Engineering, Soochow University.His research interest is in green optical networking, and he haspublished more than 10 technical papers in the related areas.


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