Optical Switching and Networking 8 (2011) 171–180

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Optical Switching and Networking

journal homepage: www.elsevier.com/locate/osn

Energy optimization in IP-over-WDM networksYi Zhang a,b,∗, Massimo Tornatore c,a, Pulak Chowdhury a, Biswanath Mukherjee a

a University of California, Davis, CA, 95616, USAb Tsinghua Univerisity, Beijing, 100084, Chinac Politecnico di Milano, Italy

a r t i c l e i n f o

Article history:Available online 29 March 2011

Keywords:EnergyOptimizationIP-over-WDMNetworksTraffic grooming

a b s t r a c t

The energy crisis and environmental protection are gaining increasing concern in recentyears. ICT (Information and Communication Technology) has a significant impact on thetotal electricity consumption all over theworld. Telecomnetworks, being an important partof ICT, consume significant energy since more network equipment is deployed annually.Specifically, in IP-over-WDM networks, energy is consumed by network elements at bothIP and WDM layers. Routers in the IP layer are the largest energy consumer in thisarchitecture, and current network infrastructures have no energy-saving scheme, so a largeamount of energy is wasted when traffic load is low. In this paper, we propose a novelapproach to save energy in IP-over-WDM networks by shutting down idle line cards andchassis of routers based on time-of-the-day network traffic variation. A method basedon Mixed Integer Linear Programming (MILP) is proposed to ensure that the energy costincurred by the IP routers and optical cross-connects is minimized by our approach. Wealso propose some possible approaches to minimize potential traffic disruption when thenetwork elements are shut down.

© 2011 Elsevier B.V. All rights reserved.

1. Introduction

Energy efficiency has been gaining increasing interestin our society in recent years. In particular, energyconsumption of ICT (Information and CommunicationTechnology) is increasing fast, since more equipmentfor networking and communication is being deployedannually. From the data of 2009, ICT consumes about 8%of the total electricity all over the world [1]. Although thispercentage is not high, the energy consumption of ICT isstill considerable because the total amount of electricityusage in the world is enormous. Telecom networks, whichrepresent a significant part of the ICT, are penetratingfurther into our daily lives. The traffic volume of broadbandtelecom networks is increasing rapidly and so is its energyconsumption. Considering both the growing energy price

∗ Corresponding author at: University of California, Davis, CA, 95616,USA. Tel.: +1 530 752 5129.

E-mail address: zhangyith@gmail.com (Y. Zhang).

1573-4277/$ – see front matter© 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.osn.2011.03.006

(expected with the decline of availability of cheap fossilfuels) and the increasing concern on the greenhouseeffect, the energy consumption of ICT is already raisingquestions. It is imperative to develop energy-efficienttelecom solutions. We need to design new networkingparadigms so that telecom networks will maintain thesame level of functionality while consuming less energy infuture.

IP-over-WDM is a promising network architecture fornext-generation telecom networks. In an IP-over-WDMnetwork, energy is consumed in both electronics (e.g., IP)and optics (e.g., WDM). IP routers, switches, networkgateways, etc. consume most of the energy in electronics(which is loosely referred to as the IP layer here), whileoptical cross-connects (OXCs), EDFAs, and transmitters arethe main energy consumers in optics (which is looselyreferred to as the physical layer). According to recentresearch of energy efficiency, electronic devices in the IPlayer, especially routers, consumemuchmore energy thanoptical devices in the physical layer [2]. Therefore, saving

172 Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180

energy at the IP layer should be very beneficial to this two-layer network infrastructure.

Traffic load in an IP-over-WDMnetwork is always vary-ing as a consequence of users’ behavior over various timesof the day. However, in current telecom networks, energyconsumption of network equipment is not considered asa critical issue so that majority of the IP routers of the net-work are kept powered-on day and nightwithout concern-ing their levels of utilization. In this case, a large amount ofenergy is wasted when the traffic load is low. To improvethis situation, in this paper, we propose a novel schemeto reduce the energy consumption of IP-over-WDM net-works, especially in the IP layer, according to the trafficvariation during the time of the day. When the traffic loadis low (at night or in early morning), line cards and chas-sis of IP routers can be of very low utilization or idle. Inthis case, we shut the idle line cards and chassis down andreroute the affected traffic, thereby saving the energy con-sumed by them. If all the line cards of a chassis are shutdown in our scheme,we also shut down this chassis to savethe energy consumed by this idle chassis. On the contrary,if traffic load increases and additional line cards and chas-sis are needed, we turn them on. When this equipment isbeing shut down or turned on, traffic interruption may oc-cur since re-routings are needed. In this case, our approachis also focusing on minimizing the amount of reconfigura-tion of network elements and reroutings, so that potentialtraffic interruptions can be minimized.

This paper is organized as follows: Section 2 introducesrelated work published in recent years; Section 3 formallystates the problem; Section 4 proposes a method basedon Mixed Integer Linear Programming (MILP) to minimizethe network-wide energy cost and potential traffic disrup-tions; Section 5 provides numerical results; and Section 6concludes the paper.

2. Related work

Recently, more and more literature has been publishedon energy efficiency in telecom networks. In general,the related works can be divided into three categories:energy-efficient network design, green traffic grooming,and selectively turning down network elements.

On ‘‘energy-efficient network design’’, the authors in [3]propose an approach of network design and planningwhich minimizes energy consumption of IP-over-WDMnetworks by studying energy usage of components in bothIP and physical layers and using total energy consumptionas the objective of network design.

On ‘‘green traffic grooming’’, in [4], total energyconsumption of an optical WDM network is modeled interms of the energy consumed by individual lightpaths.An ILP (Integer Linear Program) formulation of theenergy-aware grooming problem is defined. In [5], theauthors propose both an MILP and a heuristic approachto solve the routing and wavelength assignment anddecrease the number of lightpath interfaces in orderto minimize the energy consumption of the network.In [6], the authors consider the energy consumed byeach network operation needed while grooming trafficin optical backbone networks. Energy consumption of

every operation in traffic grooming is investigated, and anauxiliary-graph based model is proposed to identify theenergy consumed by the operations. Results show thatenergy-aware traffic grooming saves a significant amountof energy compared to the traditional traffic groomingscheme. In [7], the authors focus on the energy-awaredynamic traffic grooming problem in optical networks,with the methodology of auxiliary graph.

As for ‘‘selectively turning down network elements’’,the authors in [8] propose a scheme to reduce energyconsumption by switching off idle physical nodes andlinks in a hierarchical network topology according tothe traffic variation during time of the day. But shuttingdown physical nodes or links may lead to much longerroutes for some traffic demands, so the performance of thewhole network may be affected. The authors in [9] alsopropose an approach to selectively switching off opticallinks. Furthermore, in [10], a scheme is proposed to shutdown idle line cards (and corresponding optical circuit, orlightpath, associated with the line cards) when the trafficload is low. Note that shutting down lightpaths may leadto undesirable traffic disruptions but this problem is notaddressed. As a further difference with respect to [10],in our approach, when traffic load is low, we shut downnot only idle line cards, but also the chassis of IP routers.This will lead to: changes only in the virtual-networkconnectivity, without affecting the underlying physicaltopology. Also, we minimize the amount of networkreconfigurations, such as reconfiguration of connectivity ofline cards and lightpaths, to minimize the potential trafficdisruptionwhen the line cards and chassis of IP routers arebeing shut down. In addition, we also consider minimizingthe energy consumption from OXCs in the network.

Today, telecomnetworks are going through technologi-cal changes to support enormous data trafficwhile control-ling their energy consumption. Our research is a timely onewhich can enable carriers to design energy-efficient net-works (and their energy-efficient upgrade) by dynamicallyreconfiguring network devices, i.e., shutting down idle net-work elements to save energy, ensuring a minimal impacton the living traffic served by the network.

3. Problem statement

3.1. Motivation of time-aware energy optimization

Traffic load in telecom networks is determined bythe behavior of users who are using the network. Ingeneral, most enterprise and residential network usersaccess the Internet in the daytime or evening, respectively,which induces high bandwidth utilization of the networkconcentrated in some specific times of the day. On thecontrary, during earlymorning or aftermidnight, the trafficdemand by users reduces significantly to a much lowerlevel.

Some institutions have done some investigations onuser behaviors of the telecom networks. As an example,Fig. 1 shows the real-time traffic load variation of Internetin Netherlands during time of the day. It is measured byservice provider AMS-IX in Amsterdam [11]. From 6:00 to16:00, the traffic load increases continuously. It is because

Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180 173

200 G

300 G

400 G

500 G

600 G

700 G

800 G

Bits

per

sec

ond

04 08 12 16 20 00 04 08 1:

Fig. 1. Daily graph of traffic load (form AMS-IX) [11].

most activities of enterprise users happen during thistime. Then, from 16:00 to 20:00, the traffic load increasesa little faster because residential users begin to accessthe Internet while some of the enterprise users are stillconnecting to the network. Then, from 20:00 to 6:00, thetraffic load decreases rapidly, because the users from bothenterprise and residential area are going to sleep. The ratioof maximum load and minimum load is about 4:1, whichis very large.

In traditional telecom networks with no energy-savingmode, network capacity is dimensioned to support peak-hour traffic and IP routers and switches are alwayspowered-on day and night, ready for routing/switchingtraffic, even though some line cards and chassis may beidle for a considerable time. According to the traffic profileshown in Fig. 1, if there is an approachwhich is able to shutdown idle network elements during off-peak hours, ideally75% of energy can be saved when the traffic load decreasesto its minimal value around 6:00.

3.2. Different schemes of lightpath bypass and their energyconsumption

An IP-over-WDM network has a two-layer networkinfrastructure, which is shown in Figs. 2a–2c. Routers atthe IP layer can be interconnected by virtual connections,while OXCs at the physical layer connect to each other byfibers and compose the physical topology. Each router atthe IP layer is connected to a corresponding OXC at thephysical layer.

When we consider traffic grooming and routing, thereare three different methods of establishing lightpaths,i.e., without lightpath bypass (for short, non-bypass), directlightpath bypass (for short, direct bypass) and multi-hoplightpath bypass (for short, multi-hop bypass) [3]. Fig. 2ashows a connection between node a and node d usingthe non-bypass scheme. In this scheme, routers and OXCsat intermediate node b and node c are not allowed tobe bypassed. In other words, three lightpaths (A–B, B–C ,and C–D) have to be established in the physical topologyand three virtual connections (a–b, b–c , c–d) have to beestablished in the IP layer. In this case, besides node a andnode d, line cards and chassis of the routers at node b andnode c have to be used to transfer andprocess traffic,whichconsumes energy.

Fig. 2a. An IP-over-WDM network (non-bypass).

Fig. 2b. An IP-over-WDM network (direct bypass).

Fig. 2c. An IP-over-WDM network (multi-hop bypass).

Fig. 2b shows a connection between node a and noded using the direct bypass scheme. In this scheme, routersand OXCs at intermediate node b and node c are able tobe bypassed. In other words, only one lightpath (A–D) isneeded to be established in the physical topology and onlyone virtual connection (a–d) is needed to be establishedin the IP layer. In this case, line cards and chassis of therouter at node b and node c are not used to transfer andprocess traffic. We can shut them down to save energy.However, if there is another traffic demand from node ato node b, a new lightpath from node A to node B has tobe established. Therefore, additional overhead of energyconsumption may occur [6], while in non-bypass scheme,the traffic demand from node a to node b can be groomedto the existing light path from node A to node B.

Considering the advantages and disadvantages of non-bypass and direct bypass, the third scheme — multi-hopbypass ismore flexible. Fig. 2c shows a connection betweennode a and node d using the multi-hop bypass scheme.It is a combination of the previous two schemes. Therouter at node b is used for transferring and processingtraffic, and the router at node c is bypassed. In otherwords, two lightpaths (A–B, B–D) are established in the

174 Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180

physical topology, and two virtual connections (a–b, b–d)are established in the IP layer. In this case, the lightpathbetween node A and node B can be used for grooming newtraffic, while line cards and chassis of the router at node ccan be shut down for energy saving.

4. Proposed optimization method

4.1. Energy-optimization scheme

Considering the characteristics of the three differentschemes for traffic grooming described in Section 3, weapply the multi-hop bypass approach, the most flexibleone, for our routing and wavelength assignment.

Besides, in order to simulate the traffic-load variationduring the day, we simplify the real traffic profile inFig. 1 with the step function shown in Fig. 3. The functionin Fig. 3 expresses the trend of traffic variation duringthe day and maintains the ratio between the maximumand minimum traffic. We assume that, in our simplifiedscenario, the traffic demand is static in each time period,i.e., 2 h durations.

Then, during each time period, we use a Mixed IntegerLinear Programming (MILP) approach tominimize the totalenergy consumption of line cards and chassis of IP routersand also OXCs in the network, and shut down the idle linecards and chassis of IP routers so that maximal amountof energy is able to be saved. (OXCs do not need to beshut down because the next-generation all-optical OXCsconsume much less energy than the routers, though wealso try to minimize the energy consumption of OXCs).

This paper deals with a static network planningproblem. It is intended to be run off-line based onpredictedtraffic variation during the time of the day. So every singlevalue in our traffic matrices (e.g. Table 1) is a predictedaverage value of traffic demand (in the unit of Gbps) fromone source node to one destination node and, we do notfocus on how many traffic demands of lower granularitycompose this average traffic demand in the trafficmatrix orwhat the duration of each traffic demand is. In this context,when the traffic load changes from one level to another,some traffic demands (e.g., those lasting for the entireday) may have to ‘‘suffer from’’ traffic disruption causedby re-routings applied to minimize energy. However, weare not addressing here the minimization of the exactamount of traffic-flow disruptions since we do not specifythe duration of each smaller-granularity traffic demand.Therefore, potential traffic disruptions may happen at themoment that we are shutting down or turning on workingline cards and chassis when traffic load changes in thiscontext.

However, we are able to measure the amount ofreroutings of traffic and reconfigurations of lightpaths, linecards and chassis when traffic load changes from one toanother. We assume that less reconfiguration and lessreroutings lead to less traffic disruption in the realisticnetworks.

We propose three different schemes to demonstratedifferent performance of minimizing energy consumptionand traffic disruption.(a) Unconstrained Reconfiguration

In this scheme, we run MILP to obtain the minimalenergy consumption of the network in each 2 h time

period and reconfigure the lightpath, line cards,chassis, etc., without any constraint which jointlyconsiders theneighboring timeperiods. In otherwords,the configurations of lightpath, line cards, chassis, etc.of the network are independent in each 2h timeperiod.

(b) Virtual-Topology-Constrained ReconfigurationWe modify the MILP in scheme (a) by adding

some constraints considering the relationship betweentwo neighboring time periods. In order to reduce thereconfiguration of the network and rerouting of thetraffic, we use a top–down scheme to establish andremove the lightpaths, minimizing the reconfigurationof lightpaths, line cards and chassis. In this scheme,we first run MILP to calculate the minimal number ofline cards and chassis which are needed to support thetraffic demand during traffic peak time. Based on theresults of traffic peak time, we calculate the number ofline cards and chassis for traffic off-peak time. Using asequential top–down scheme (also shown in Fig. 3), wereconfigure the set of lightpaths, line cards and chassisunder lower traffic load so that they are sub-sets of theones under higher traffic load.

This operation requires additional constraints inenergy optimization, but allows us to reduce thenumber of reconfiguration operations, and, in return,to reduce the potential traffic interruption while thetraffic is being rerouted while moving from one loadlevel to another. In this context, when the traffic loaddecreases, we shut down line cards and chassis of IProuters which are no longer used, and no additionalline cards or chassis need to be turned on. On theother hand, when the traffic load increases, we turn onadditional line cards and chassis which are needed tobe used while no line cards or chassis need to be shutdown.

(c) Full-Constrained ReconfigurationFull-Constrained Reconfiguration also use the

top–down scheme mentioned in Virtual-Topology-Constrained Reconfiguration. However, this kind ofreconfiguration not only applies the constraints ofthe subset to lightpaths, line cards and chassis inlower traffic load, but also applies the constraintsof the subset to traffic flows in the virtual topologyand physical routings in the physical topology. Thisscheme, to the full extent, minimizes the reconfigura-tion of network and rerouting of traffic. This schemeimposes even more additional constraints in energyoptimization than Virtual-Topology-Constrained Re-configuration and, as a consequence, the total energyconsumption of the network may be larger.

4.2. Mathematical formulation

Based on the simplified traffic profile in Fig. 3 and theenergy-optimization schemes provided by Section 4.1, weuseMixed Integer Linear Programming (MILP) to formulateand solve this problem. By line cards wemean line cards inrouters only. The notations are shown as follows:Given.

G(N, E) Network topology.N Set of nodes in the network.E Set of edges in the network.W Set of wavelengths in a fiber.

Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180 175

Fig. 3. Simplified graph of traffic load and the top–down scheme.

Pmn Number of fibers from nodem to node n.Λsd Traffic demand from node s to node d.C Capacity of each wavelength channel.V ′

ij Number of lightpaths from node i to j in thevirtual topology under higher traffic load.

q′

ij Number of transmitting line cards at node icorresponding to V ′

ij (under higher traffic load). Itis also equal to the number of receiving line cardsat node j corresponding to V ′

ij. (There is only oneinterface per line card, either for transmitting orfor receiving, so there are line cards from bothsides corresponding to V ′

ij.)B Bandwidth of a line card.L Number of slots in the chassis of router.Eel Energy consumption of a line card in a IP router.Eec Energy consumption of a chassis in a IP router.Eo Energy consumption of a connection in anOptical

Cross-connect.V ′

ij,w Number of lightpaths established from node i tonode j, which use wavelength channel w (underhigher traffic load).

λ′sdij Traffic flowing from node s to node d, employing

Vij as an intermediate lightpath (under highertraffic load).

P′ ij,wmn Number of lightpaths between nodes i and j,

being routed through fiber link mn, and usingwavelength channelw (under higher traffic load).

Variables.

Vij Number of lightpaths established from node i tonode j.

Vij,w Number of lightpaths established from node i tonode j, which use wavelength channel w.

λsdij Traffic flowing from node s to node d, employing

Vij as an intermediate lightpath.P ij,wmn Number of lightpaths between nodes i and j,

being routed through fiber link mn, and usingwavelength channel w.

nc(i) Number of working chassis in node i.nl(i) Number of working line cards in node i.qij Number of transmitting line cards at node i

corresponding toVij. It is also equal to the numberof receiving line cards at node j corresponding toVij (There is only one interface per line card, eitherfor transmitting or for receiving, so there are linecards from both sides corresponding to Vij).

w Wavelength ID in a fiber.

Minimize.−i

[Eel × nl(i) + Eec × nc(i)]

+ Eo ×

−w

−i,j

−m,n

P ij,wmn + Vij,w

. (1)

Constraints.−j

λsdij −

−j

λsdji

=

Λsd i = s− Λsd i = d0 i = s, d

∀i, s, d ∈ N (2)

−sd

λsdij ≤ C × Vij ∀i, j ∈ N (3)−

n

P ij,wmn −

−n

P ij,wnm

=

Vij,w m = i−V ij,w m = j0 m = i, j

∀i, j,m ∈ N, ∀w ∈ W (4)

−w

Vij,w = Vij ∀i, j ∈ N (5)−ij

P ij,wmn ≤ Pmn ∀(m, n) ∈ E, ∀w ∈ W (6)

Vij ≤ V ′

ij ∀i, j ∈ N

(Applied for Virtual-Topology-Constrained Reconfiguration) (7)qij ≤ q′

ij ∀i, j ∈ N

(Applied for Virtual-Topology-Constrained Reconfiguration) (8)

qij ≥

−sd

λsdij

B ∀i, j ∈ N (9)

nl(i) =

−j

(qij + qji) ∀i ∈ N (10)

nc(i) ≥1L

−j

(qij + qji) ∀i ∈ N (11)

Vij,w ≤ V ′

ij,w ∀i, j ∈ N, ∀w ∈ W

(Applied for Full-Constrained Reconfiguration) (12)

λsdij ≤ λ

′sdij ∀i, j, s, d ∈ N

(Applied for Full-Constrained Reconfiguration) (13)

P ij,wmn ≤ P

′ ij,wmn ∀i, j ∈ N, (m, n) ∈ E, ∀w ∈ W

(Applied for Full-Constrained Reconfiguration). (14)

The formulation is based on the multi-hop lightpathbypass scheme introduced in Section 3.2. Eq. (1) givesthe objective function, which minimizes the total energyconsumed by line cards and chassis of routers in electricallayer and optical cross-connects in the optical layer ofthe IP-over-WDM networks. The highlighted part of theequation: ‘‘Eo ×

∑w∑

i,j(∑

m,n Pij,wmn + Vij,w)’’ denotes

the energy consumed by optical cross-connects, which isdetermined by the total number of physical connections.

176 Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180

Eq. (2) is the multicommodity-flow-based equationgoverning the flow of traffic in the virtual topology atthe IP layer [12]. Eq. (3) specifies the capacity constraintin the virtual-topology links (lightpaths). Eqs. (4) and (5)are the multicommodity-flow-based equations governingthe routing of lightpaths over the physical topology fromsource to destination, while using wavelength channelw [12]. These constraints ensure the wavelength continu-ity through all-optical OXCs. Eq. (6) ensures that the totalnumber of lightpaths using the physical linkmn andwave-length channel w is not greater than the number of fibers,i.e., one wavelength channel can carry only one lightpath.

Eqs. (7) and (8) are applied only by the Virtual-Topology-Constrained Reconfiguration introduced in Sec-tion 4.1. They denotes that the set of working lightpathsand line cards when traffic load is lower should be the sub-set of working lightpaths and line cards when traffic loadis higher. We use a first-fit method to assign Vij and qij, sothat ‘‘Vij is a subset of V ′

ij’’ corresponds to ‘‘Vij ≤ V ′

ij’’, and‘‘qij is a subset of q′

ij’’ corresponds to ‘‘qij ≤ q′

ij’’. Therefore,as shown in Fig. 3, we first calculate the values of Vij and qijfor the traffic demand of peak time, i.e., at 20:00–22:00 inFig. 1, and then use this value of Vij and qij as the given pa-rameter V ′

ij and q′

ij for the next or the previous lower traffic,i.e., at 22:00–0:00 or 18:00–20:00. After that, we continueto go ‘‘down step’’ for calculation until the lowest trafficlevel.

Eq. (9) accounts that qij is equal to ‘‘the total traffic flowon lightpath from node i to j ’’ divided by the bandwidth ofa line card. Note that this count holds since we assume afirst-fit scheme to assign the traffic into the line cards. Theoperator ‘‘ ≥’’ equals the ceiling function if we define qij tobe an integer. Eq. (10) denotes that the number of line cardsin each node i is equal to the number of line cards used bythe flows departing from node i (qij) and arriving at nodei (qji), and similarly, Eq. (11) denotes that ‘‘the number ofchassis used at node i ’’ is equal to ‘‘the number of line cardsused by the flows departing from node i (qij) and arrivingat node i (qji)’’ divided by the number of slots in a chassis.Here we also use a first-fit scheme to assign the line cardsinto the chassis, and use the operator ‘‘≥’’ to denote theceiling function.

Eqs. (12)–(14) are applied only by the Full-ConstrainedReconfiguration scheme. Eq. (12) denotes that the Vij,w(lightpaths established load from node i to node j usingwavelength channel w) for lower traffic load is the subsetof the V ′

ij,w for previous higher traffic load. Similarly, theEqs. (13) and (14) denotes the λsd

ij (traffic flows from nodes to node d, employing lightpath Vij) and P ij,w

mn (Numberof lightpaths between nodes i and j, being routed throughfiber link mn, and using wavelength channel w) for lowertraffic load are the subsets of the ones for previous highertraffic load. Note that the Eqs. (7) and (8) are containedlogically in Eqs. (12) and (13). Eqs. (12)–(14) are able toensure the traffic flows, lightpaths, line cards and physicalroutings for lower traffic load are all the correspondingsubsets of the ones for previous higher traffic load sorerouting of traffic is avoided.

Except Eqs. (7), (8), (12)–(14), other equations areapplied by all the three schemes, i.e., Unconstrained Re-

Fig. 4. Sample network topology.

configuration, Virtual-Topology-Constrained Reconfigura-tion and Full-Constrained Reconfiguration.

Based on these formulations, for Unconstrained Recon-figuration,we run theMILPwithout Eqs. (7), (8), (12)–(14),independently in each time period. For Virtual-Topology-Constrained Reconfiguration, we first run the MILP in peaktraffic time without the Eqs. (7), (8), (12)–(14). Then, weuse the results of Vij and qij to be the values of V ′

ij and q′

ijfor the lower traffic load and run the MILP again with theEqs. (7) and (8), in a ‘‘top–down’’ sequence to get the re-sults in off-peak traffic time. Finally, for Full-ConstrainedReconfiguration, we first run the MILP in peak traffic timewithout the Eqs. (7), (8), (12)–(14). Then, we use the re-sults of Vij, qij, Vij,w , λsd

ij and P ij,wmn to be the values of the cor-

responding V ′

ij, q′

ij, V′

ij,w , λ′sdij and P

′ ij,wmn for the lower traffic

load, and run the MILP again with the Eqs. (12)–(14), in a‘‘top–down’’ sequence to get the results in the off-peak traf-fic time.

Therefore, the Virtual-Topology-Constrained Reconfig-uration constrains the reconfiguration of virtual topologyof the network, and ensures that the set of light path,work-ing line cards and chassis at lower traffic load is a subsetof the corresponding ones at previous higher traffic load,then the amount of reconfiguration of network elementswhen traffic load changes is reduced, also leading to a de-crease of actual traffic disruptions. However, reroutingsmay still occurwhenwe reconfigure the lightpaths and linecards in different time period since we do not constraintraffic flows in Virtual-Topology-Constrained Reconfigura-tion. In this context, the Full-Constrained Reconfigurationeliminates the re-routings of traffic. Besides, since we donot shut down optical links in physical layer, the physicalrerouting is not likely to happen when virtual topology isfixed. So the reroutings we are investigated in this paperare the flows rerouted in Virtual Topology.

5. Numerical results

5.1. Energy consumption

To evaluate the performance of our energy optimizationscheme, we use a typical enterprise network topology(Fig. 4) for illustration. It has 14 nodes and 21 links,one pair of bidirectional fiber between each neighbornodes, with 32 wavelengths in each fiber, and capacityof each wavelength channel is 40 Gbps (C). We usea model of a typical enterprise router to capture itsenergy consumption. The line card of the router has theoutput bandwidth of 2.5 Gbps (B). We multiplex 16 line

Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180 177

Fig. 5. Energy consumption and energy savings by Virtual-Topology-Constrained Reconfiguration.

cards of 2.5 Gbps into one 40 Gbps wavelength channel.The energy consumption of the line card is 140 watt(Eel). The chassis can support at most 16 line cards (L)and its energy consumption is 5700 watt (Eec , excludingthe energy consumption of line cards) [13]. Note thatin our case, shutting down line cards and chassis iscompletely powered off. They will no longer consumeenergy after being shut down. We also use a model of atypical enterprise OXC (with all-optical switching fabric) tocapture its energy consumption. The energy consumptionof the OXC is 470 mW per connection (Eo) [14].

We use the data of a realistic value of traffic measure-ment in [15] to generate the trafficmatrix (Table 1).Weusethe realistic traffic matrix in [15] and proportionally scaleit to meet the traffic load requirement in Fig. 3. We ensurethe total traffic demand of all node pairs in each time pe-riod to be equal to the traffic load in Fig. 3. Our problem is avariant of the virtual-topology design problem, which hasbeen shown to be NP-hard [12]. We use a computer withIntel Core2 Quad CPU Q9650 (3.00 GHz) and 4 GB Memoryand AMPL/CPLEX to do the computation. It takes about 3 hto obtain each optimization result.

Table 2 shows the results by Virtual-Topology-Constrained Reconfiguration. It demonstrates the amountof energy consumed by line cards, chassis of IP routers,and OXCs when using our scheme to shut down idle linecards and chassis during various time of the day. Note thatthe trend of the variation of the total energy consumptionfollows the trend of the traffic variation after our energy-saving scheme. Table 2 also reports the energy savingratio, which is calculated by comparing the energy con-sumption by our approach with the energy consumptionwhere line cards and chassis are deployed for peak trafficload (20:00–22:00) and never turned off. The energy sav-ings vary from 0% to 63.5%, with an average value of 31.0%.

The results in Table 2 demonstrate that Virtual-Topology-Constrained Reconfiguration at the IP layer mayenable the relevant savings in the energy consumptionin IP-over-WDM networks. The results also inform usthat the energy consumed by chassis and line cards inIP layer is much more than the energy consumed byOXCs in the optical layer. Hence, shutting down line cardsand chassis in IP layer is very efficient to reduce theenergy usage in IP-over-WDM networks. Furthermore, thecontribution to the energy consumption due to chassis is

Fig. 6. Energy consumption of three approaches during time variation ofthe day.

dominant with respect to line cards, which indicates thatan effective energy-saving scheme should also considershutting down of entire chassis together with line cards.For better visualization, we plot the variation of energyconsumption of line cards and chassis of IP routers in Fig. 5.Energy consumption of OXCs is not plotted in the figure(as well as in later illustrations), because it is much smallerthan those of the line cards and chassis of IP routers and it isnot possible to plot them together clearly. We also plot thevariation of energy savings during various times of the day.

More constraints for minimizing the reroutings maylead to worse performance of energy consumption. Wecompare the energy consumption of Full-Constrained Re-configuration, UnconstrainedReconfiguration, andVirtual-Topology-Constrained Configuration respectively in Fig. 6.The Full-Constrained Reconfiguration costs 22.36% moreenergy consumption on average during the time variationof the day. Notably, the Constrained Reconfiguration re-quires almost the same energy as the Unconstrained Re-configuration.

5.2. Reconfigurations of lightpaths and line cards

Figs. 7a–7c give a snapshot of the number of establishedlightpaths, working line cards, and chassis in each node inthe scenario by the Virtual-Topology-Constrained Recon-figuration approach.We select three different time periodsto demonstrate three different traffic loads, i.e., peak loadat 20:00–22:00, minimal load at 6:00–8:00 and interme-diate load at 0:00–2:00. The results show that the num-ber of lightpaths, working chassis, and line cards are fewerfor every node when the traffic load is lower. These resultsshow that the scheme of subset-constrained introduced inSection 3 does work. Furthermore, the nodes which havehigher traffic load, such as, 2, 8, 9, 12, 13, have higher linecards and chassis utilization. (These are also the areas ofhigher population density in United States.)

Then we compare the reconfigurations of lightpathsand line cards by Full-ConstrainedReconfiguration, Virtual-Topology-Constrained Reconfiguration and UnconstrainedReconfiguration. The number of newly established light-paths, shut-down lightpaths, turned-on line cards, andshut-down line cards by Full-Constrained Reconfigura-tion, Virtual-Topology-Constrained Reconfiguration and

178 Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180

Table 1Traffic matrix at peak-load time (20:00–22:00).

NodeID

Traffic demands (Gbps)

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 – 2.657 1.160 0.269 1.947 0.087 0.533 2.466 3.088 0.339 0.183 0.958 1.896 0.4382 7.122 – 6.042 2.984 5.807 2.593 3.950 15.349 7.917 1.134 2.121 10.215 7.684 5.4713 1.081 4.711 – 4.617 0.843 3.602 0.857 8.574 5.114 0.994 0.458 0.615 2.137 1.3784 0.695 0.615 1.351 – 0.189 0.060 0.069 0.285 1.299 0.198 0.323 1.204 0.691 0.2375 12.159 15.846 1.884 0.340 – 0.400 1.068 6.163 7.142 2.379 1.775 11.742 2.155 1.3066 0.182 1.638 0.339 0.547 0.337 – 0.259 0.266 0.600 0.087 0.383 0.478 0.689 0.1537 3.665 6.141 10.133 0.443 2.183 0.782 – 11.308 15.255 1.964 2.175 9.243 16.230 2.3448 1.481 23.230 20.833 0.844 2.794 0.264 9.615 – 8.919 4.353 3.269 7.048 8.805 2.0019 1.106 3.725 5.774 0.502 0.935 1.287 1.861 3.753 – 2.028 2.488 5.910 3.684 3.197

10 8.412 1.975 3.699 0.595 2.475 0.675 2.483 6.043 10.962 – 3.924 14.620 6.253 4.52411 0.185 4.152 1.017 0.370 2.213 0.939 0.494 5.654 3.597 0.678 – 2.592 1.423 1.25712 3.093 13.057 1.968 1.448 4.259 0.708 1.716 5.677 20.961 3.922 2.914 – 6.534 2.77313 8.112 22.483 5.377 2.274 8.842 3.152 3.239 9.097 12.845 3.031 0.165 13.628 – 6.21414 3.900 5.481 1.842 0.747 0.834 0.084 0.445 2.416 6.851 11.655 3.535 7.845 5.170 –

Table 2Energy consumption and energy savings.

Time of the day Energy consumption of Chassis(W)

Energy consumption of Linecards (W)

Energy consumption of OpticalCross-connect (W)

Energy savings (%)

6:00–8:00 96,900 26,320 115.15 63.5

8:00–10:00 108,300 33,880 131.60 57.9

10:00–12:00 153,900 46,760 156.04 40.6

12:00–14:00 176,700 58,800 167.79 30.3

14:00–16:00 182,400 63,280 174.37 27.3

16:00–18:00 210,900 70,840 183.30 16.6

18:00–20:00 233,700 78,960 186.59 7.46

20:00–22:00 250,800 87,080 196.93 0

22:00–0:00 233,700 78,960 186.59 7.46

0:00–2:00 193,800 67,200 178.13 22.7

2:00–4:00 153,900 46,760 156.04 40.6

4:00–6:00 108,300 33,880 131.60 57.9

Fig. 7a. A snapshot of the established lightpaths at each node by Virtual-Topology-Constrained Reconfiguration.

Unconstrained Reconfiguration, are shown as Figs. 8a, 8b,9a and 9b, respectively. As these figures show, the Full-Constrained Reconfiguration approach invokes the small-est amount of reconfigurations when traffic load changes,while the Unconstrained Reconfiguration approach in-

Fig. 7b. A snapshot of the number ofworking line cards at each node byVirtual-Topology-Constrained Reconfiguration.

vokes the largest one. Virtual-Topology-Constrained Re-configuration stands in the middle. For example, Fig. 8ashows the number of newly established lightpaths dur-ing various time of the day. When traffic load decreases(e.g. from 22:00 to 6:00), no new lightpaths need to beestablished by the Virtual-Topology-Constrained Recon-figuration approach, while the Unconstrained Reconfig-

Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180 179

Fig. 7c. A snapshot of the number of working chassis at each node byVirtual-Topology-Constrained Reconfiguration.

Fig. 8a. Number of newly established lightpaths.

uration establishes some new lightpaths. Fig. 8b showsthe number of newly shut-down lightpaths during timeof the day. When traffic load increases (e.g. from 8:00 to20:00), no existing lightpaths need to be shut down by theVirtual-Topology-Constrained Reconfiguration approach,while the ‘‘Unconstrained Reconfiguration’’ shuts downsome existing lightpaths. The Full-Constrained Reconfigu-ration do not shut downor turn on any lightpath during theentire day. The scenario is similar for the reconfigurationsof line cards shown on Figs. 9a and 9b. The only differenceis that the Full-Constrained Reconfiguration has non-zeroline-card reconfiguration (either shutting down or turningon during some time-period), which is less than Virtual-Topology-Constrained Reconfiguration. This fits the resultsthat Full-Constrained Reconfiguration saves less energythan Virtual-Topology-Constrained Reconfiguration.

5.3. Analysis of reroutings

We plot Fig. 10 to show the percentage of traffic whichis rerouted in virtual topology by the three different re-configuration schemes respectively when the traffic loadvaries from one time period to another during the day.The Full-constrained Reconfiguration causes zero rerout-ing since we constrained the reroutings of traffic flowsto be zero by the Eqs. (12)–(14). The Virtual-Topology-Constrained Reconfiguration causes fewer reroutings than

Fig. 8b. Number of newly shut-down lightpaths.

Fig. 9a. Number of newly turned-on Line cards.

Fig. 9b. Number of newly shut-down Line cards.

the Unconstrained Reconfiguration in each time period,whichmeans that subset-constraints of lightpaths and linecards for Virtual-Topology-Constrained Reconfigurationare able to reduce some number of reroutings. In addition,the results in Fig. 10 also show that the lower is the traf-fic load, the higher is the percentage of traffic needed to bererouted. The reason is that the absolute number of rerout-ings is similar in each time period, so the percentage of re-rerouting of traffic is larger when the traffic load is lower.

180 Y. Zhang et al. / Optical Switching and Networking 8 (2011) 171–180

Fig. 10. The Percentage of Traffic Rerouted in Virtual Topology.

Based on the analysis of energy consumption, line-card and lightpath reconfiguration and rerouting, threeschemes have their different advantages and deficien-cies. In summary, the Unconstrained Reconfiguration ob-tains best amount of energy savings, but largest numberof reroutings and reconfigurations; the Virtual-Topology-Constrained Reconfiguration causes fewer reroutings andreconfigurations than Unconstrained Reconfigurations,and cost almost the same energy of Unconstrained Re-configuration. The Full-Constrained Reconfiguration elim-inates the reroutings, but costs a 22.36% higher amountof energy consumption. The network operators are ableto make their best choice among these three approachesbased on their requirements and practical network sce-nario.

6. Conclusion and further discussion

Energy consumption of ICT has been gaining increasingconcern in recent years. In IP-over-WDM networks, IProuters in the electrical layer and OXCs in the optical layerboth consume energy. In this paper, we considered savingenergy at both layers by exploiting the opportunitiesdue to traffic variation during the time of the day. Weproposed a novel scheme to save energy in IP-over-WDMnetworks by shutting down idle line cards and chassisof IP routers based on the traffic profile during varioustimes of the day. Based on a realistic case study, about30% energy savings can be achieved by our scheme. Ourscheme also minimizes the potential traffic interruptionby minimizing the amount of reconfiguration of networkequipments when network elements are being shut down.Results also show that the energy consumption of chassisis dominant compared to the energy consumption of linecards in routers. Therefore, an effective energy-savingscheme should also consider shutting down of entirechassis together with line cards. Besides, all-optical OXCsconsume much less energy than the routers in the IP layerin IP-over-WDM networks. The reroutings and potential

traffic disruptions are addressed in our paper.We comparethe performance of energy consumption and reroutingamong three proposed schemes.

This paper deals with a static network planningproblem. It is intended to be run off-line based on thepredicted traffic variation during the time of the day. Weassume the network is in monitored. If traffic exceedscertain limits and other lightpaths should be provisioned,we can use a shortest-path approach to dynamically addother lightpaths. In future work, a dynamic on-line trafficengineering approach can be considered other than staticnetwork planning problem based on traffic prediction.Also, the duration of each traffic demand can be taken intoaccount. Since the problem complexity is expected to scalerelevantly, a new heuristic approach to minimize both theenergy consumption and traffic disruptions needs to beconsidered.

Acknowledgements

We gratefully acknowledge all the members of Net-works Lab, University of California, Davis, towards the ful-filment of this paper.

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