Green IP Over WDM Networks With Data Centers

JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 29, NO. 12, JUNE 15, 2011 1861

Green IP Over WDM Networks With Data CentersXiaowen Dong, Taisir El-Gorashi, and Jaafar M. H. Elmirghani

Abstract—Most of the previous research on data centers powerconsumption has focused on understanding how to minimize thepower consumption inside the data center. It is, however, alsoimportant to investigate the power consumption associated withtransporting data between data centers and end users. In thispaper, we consider three problems. First, through linear pro-gramming (LP) models and through simulations we determine theoptimal location of a data center or multiple data centers in an IPover wavelength-division-multiplexing network so as to minimizethe network’s power consumption. Here, we consider the impactof network topology, traffic profile, upload and download rates,number of data centers and the impact of power minimizationon delay. Second, we study how to replicate content that hasdifferent popularity to minimize power consumption through theuse of an LP model. Here, we consider five classes (but the modelsare general) of content that have different levels of popularityand consider multiple data centers. The optimization attempts toidentify where to store a data object that has a given popularitysuch that the network’s power consumption is minimized. Wehave also developed a novel routing algorithm, energy-delay op-timal routing, to minimize the power consumption of the networkunder replication while maintaining QoS. Third, we investigatethrough LP the problem of whether to locate data centers next torenewable energy or to transmit renewable energy to data centersin a given network topology under different traffic conditions andtaking into account the network components’ power consump-tion. Given a number of wind Farms whose locations are knowntogether with the electrical power transmission losses, we identifythe optimal location of data centers such that the network’s powerconsumption is minimized and consider a network where thenodes that are not connected to wind farms have access to solarpower. The results show that by identifying the optimum datacenter locations, combining the multi-hop bypass heuristic withrenewable energy and the replication scheme, power consumptionsavings of up to 73% can be achieved.

Index Terms—Data centers, IP over wavelength-division-multi-plexing (WDM) networks, renewable energy, replication.

I. INTRODUCTION

W ITH the increasing public awareness of the possibleenvironmental impacts of the steady growth in the

energy consumption of the Information and CommunicationTechnology (ICT) industry, recent research efforts have focusedon energy-aware ICT solutions. The power consumption ofdata centers is rapidly increasing with the incredible growth invideo, data-intensive applications such as medical informatics,genomics, financial, and other large datasets. According to

Manuscript received November 01, 2010; revised February 05, 2011; ac-cepted April 21, 2011. This work was supported by the Engineering and PhysicalSciences Research Council, U.K.The authors are with the School of Electronic and Electrical Engineering,

University of Leeds, Leeds, LS2 9JT, U.K. (e-mail: [email protected]).Digital Object Identifier 10.1109/JLT.2011.2148093

[1], data centers’ power consumption ranges from 75 W/ftfor small to medium-sized enterprises to 150–200 W/ft fortypical service providers. The trends predict an increase to200–300 W/ft [1]. In addition to the environmental impact,these increasing energy requirements have resulted in a 25%increase in the annual energy costs over the past few years, andthis has increased the total cost of ownership of data centerspreventing easy expansion [2].The “greening” of data centers creates challenges in at-

tempting to reduce power consumption while maintainingperformance. A group known as “Green Grid” was formed toincrease the energy efficiency in data centers [3]. Most of thework that investigates energy efficient data centers has focusedon understanding how to minimize the power consumption in-side the data center. However, as the networking infrastructureof data centers alone, without considering the cooling equip-ment energy requirements, is responsible for about 23% of theoverall power consumption [4], it is also important to considerthe power consumption associated with transporting data be-tween data centers and between data centers and end users. Thetotal energy consumed by networking elements in data centersin 2006 in the United States alone was 3 billion kWh, and thiscontinues to rise [5]. In [6], the power consumption of datacenters is optimized by powering off unused links and switcheswhile maintaining performance and fault tolerance goals.In practice, 228 grams of CO approximately are produced

by a network component that consumes 1 kWh of traditionalelectrical energy [7]. If the network can be designed such that iteliminates 1 kW non-renewable power consumption, then thiswill lead to a significant reduction in CO emission, about 2tones every year. A family vehicle typically emits 150 g/km ofCO ; therefore, in a year a 1 kW router port contributes COpollution equivalent to approximately 13 k journeys in a familyvehicle.Power consumption in backbone networks has received

increased attention for two reasons. First, the percentage thatbackbone networks are responsible for of the total networkpower consumption is expected to significantly increase withthe growing popularity of bandwidth intensive applicationssuch as high-definition IPTV. In addition, as the power con-sumption of the backbone network is often limited to a fewlocations, heat dissipation becomes an important consideration.Therefore, minimizing the power consumption of the IP overwavelength-division-multiplexing (WDM) backbone networkis essential. In [8], lightpath bypass routing is implemented toreduce power consumption in the IP over WDM network byreducing the number of IP router ports needed. A mixed in-teger linear programming (MILP) optimization model and twoheuristics to minimize power consumption are implemented.The presence of data centers in IP over WDM networks can

create a hot node scenario where more traffic is destined to or

0733-8724/$26.00 © 2011 IEEE

1862 JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 29, NO. 12, JUNE 15, 2011

Fig. 1. IP over WDM network with data centers.

originates from a data center node. This can lead to a signifi-cant increase in the power consumption of data center nodes asthe number of ports (which are the major power consumptioncomponent) increases. In this paper, we study the power con-sumption of IP over WDM networks that contain data centersand in particular investigate three problems.First, the optimization of the data centers locations to min-

imize the power consumption. We develop a linear program-ming (LP) model with this objective. We investigate three fac-tors that affect the optimum location of the data centers in IPover WDM networks: the IP over WDM routing approach (by-pass and non-bypass), the regularity of the network topologyand the number of data centers in the network.Second, we investigate the energy savings introduced by im-

plementing a data replication scheme [9]–[11] in the IP overWDM network with data centers, where frequently accesseddata objects are replicated over multiple data centers accordingto their popularity. Unlike [9]–[11] our goal here is to minimizepower consumption and although delay minimization is often aby-product, it is not our main goal. We propose a novel algo-rithm, energy-delay optimal routing (EDOR) to minimize thepower consumption under the replication scheme while main-taining QoS. We investigate the power savings achieved by themulti-hop bypass and the non-bypass heuristics.Third, we investigate introducing renewable energy sources

(wind and solar energy) to the IP over WDM network with datacenters. It is worth noting that in [12] we investigated the useof solar energy to reduce the non-renewable energy consump-tion and, consequently, the CO emission of IP over WDM net-works. Our main focus here, however, is to evaluate the meritsof transporting bits to where renewable energy is (wind farms),instead of transporting renewable energy to where data centersare. We are, therefore, interested here in the power losses as-sociated with transporting electrical power to data centers and

the impact of this on the optimum data center locations, as wellas the impact of the network topology, routing, traffic and otherfactors on the optimum data center locations from the powerminimization point of view. An LP model is set up to optimizethe location of data centers by minimizing the network non-re-newable power consumption taking into account the utilizationof the renewable energy resources and the losses.The remainder of this paper is organized as follows: Section II

introduces the LP model developed to optimize the data centerslocations in the IP over WDM network. Section III investigatesa replication scheme to minimize the power consumption of thenetwork. In Section IV, we investigate the use of renewable en-ergy. Finally, the paper is concluded in Section V.

II. DATA CENTERS IN AN IP OVER WDM NETWORK

Fig. 1 shows an IP overWDM network with data centers. TheIP over WDM network includes two layers, the IP layer, andthe optical layer. In the IP layer, an IP router is connected to anoptical switch in each node. The router aggregates data trafficfrom access networks. The optical layer can provide large ca-pacity and wide bandwidth for data communication between IProuters. Optical switches are connected to optical fiber links. Oneach fiber, a pair of wavelength multiplexers/demultiplexers isused to multiplex/demultiplex wavelengths [8]. The transpon-ders can provide OEO processing for full wavelength conver-sion at each switching node. In addition, for long distance trans-mission, the erbium-doped fiber amplifiers (EDFAs) are used toamplify the optical signal in each fiber.IP over WDM networks can be implemented by either light-

path non-bypass or bypass. With lightpath non-bypass, all thelightpaths passing through an intermediate node must be ter-minated, processed, and forwarded by IP routers. On the otherhand, the lightpath bypass approach allows all the lightpaths,whose destination is not the intermediate node, to be directly

DONG et al.: GREEN IP OVER WDM NETWORKS WITH DATA CENTERS 1863

bypassed via a cut-through. Under lightpath bypass, lightpathsare treated as virtual links in the IP layer. Lightpath bypass cansignificantly reduce the total number of IP router ports required.IP routers are the major power consuming components in an IPover WDM network. Therefore, minimizing the number of IProuter ports reduces the power consumption of IP over WDMnetworks. The economic advantages of optical bypass have beenverified in [13]. However, nodes are required to have intelli-gence to support optical bypassing, and many technological ob-stacles must be resolved in order to implement optical bypassingcapabilities in the physical layer. In addition, the non-bypass ap-proach allows security monitoring and deep packet inspection.Therefore, it is important to evaluate the network under the twoapproaches.In [8], the multi-hop bypass heuristic was proposed where the

bandwidth utilization is improved by allowing traffic demandsbetween different source--destination pairs to share capacity oncommon virtual links (lightpaths). Improving the wavelengthbandwidth utilization results in fewer virtual links, and, there-fore, fewer IP router ports and lower power consumption.In this section, we investigate IP over WDM networks with

data centers under the lightpath non-bypass and the multi-hopbypass heuristics. The multi-hop bypass heuristic is based onshortest-path routing. Shortest-path routing is suitable for datacenter traffic where end users do not tolerate high delay inaccessing data centers. We also assume shortest-path routingunder non-bypass.

A. Mathematical Model

In this section, we develop LP models to minimize the powerconsumption of the network by optimizing the locations of datacenters in the IP over WDM network under the non-bypass andthe multi-hop bypass approaches. The LP model for the non-bypass approach is developed under the following assumptions:1) Each node writes and retrieves data from all data cen-ters equally [14] (content popularity is dealt with inSection III).

2) We assume different data centers have different content.3) We consider regular traffic demand, i.e., the traffic demandbetween regular nodes, and also consider data center trafficdemand, which includes the traffic demand between datacenters and regular nodes and the traffic demand betweendata centers (a data center can access data objects availablein another data center).

4) The traffic demand between data centers and nodes at timeis assumed to be a certain ratio of the regular traffic de-mand between nodes [15].

5) The uplink traffic demand ratio from nodes to data centers,Ru, is smaller than the downlink traffic from data centersto nodes ratio, Rd [15].

6) The power consumption of different components in thenetworks is assumed to follow an “ON--OFF” energy pro-file. A Detailed treatment of Adaptive link rate (ALR) in-cluding , and cubic (voltage and frequencyscaling) energy profile is given in [12].

In this LP model, the parameters are defined as:

and Denote end points of a virtual link in the IP layer.

and Denote source and destination points of regulartraffic demand between a node pair.

and Denote end points of a physical fiber link in theoptical layer.The set of neighbor nodes of node in the opticallayer.The length of the link between nodes and inthe optical layer.The set of time points.

Distance between neighboring EDFAs.

Number of wavelengths in a fiber.

The set of nodes.

The capacity of each wavelength.

The number of EDFAs on physical link .Typically , where isthe distance between two neighboring EDFAs [8].The number of fibers on physical link

PR Power consumption of a router port.

PT Power consumption of a transponder.

PE Power consumption of an EDFA.

Power consumption of an optical switch in node .

PMD Power consumption of a multi/demultiplexer.

Ndc The total number of data centers.

The following variables are also defined:

The number of wavelength channels (integer) inthe virtual link at time .The number of wavelength channels (integer) inthe virtual link for downlink traffic at time .The number of wavelength channels (integer) inthe virtual link for uplink traffic at time .The number of wavelength channels (integer) inthe virtual link for regular traffic at time .The number of wavelength channels in thephysical link at time .The number of wavelength channels in the virtuallink that traverse physical link attime .The number of multi/demultiplexers in node

The number of ports in node used for dataaggregation at time .

if node is a data center, otherwise .

if the source of the traffic demand is a datacenter, otherwise .

if the destination of the traffic demand isa data center, otherwise .The downlink traffic flow from data center tonode that traverses the virtual link at time.The uplink traffic flow from node to data centerthat traverses the virtual link at time .

The regular traffic flow from node to node thattraverses the virtual link at time .

The total power consumption of the network at time underthese assumptions is composed of:


1) The power consumption of ports in regular nodes and datacenters at time

2) The power consumption of transponders in regular nodesand data centers at time

3) The power consumption of EDFAs at time

4) The power consumption of optical switches in regularnodes and data centers at time

5) The power consumption of multiplexers and demultiplexerin regular nodes and data centers at time

The LP model is defined as follows:Objective: minimize the power consumption of the network

by optimizing the locations of the data centers.

(1)

Subject to: The objective function is minimized subject to flowconservation constraints, link capacity constraints, number ofdata centers constraint and number of aggregation ports con-straint. The constraints are:1) Flow Conservation Constraints:

(2)

(3)

(4)

Constraints (2), (3), and (4) represent the flow conservationconstraints for the downlink, uplink, and regular traffic flows,respectively. They represent the fact that in all nodes the totaloutgoing traffic should be equal to the total incoming traffic ex-cept for the source and the destination nodes. They also ensurethat the traffic flows can be split and transmitted through mul-tiple flow paths in the IP layer.2) Virtual Link Capacity Constraint:

(5)

A virtual link can carry downlink, uplink, and regular trafficflows. Constraint (5) ensures that the summation of all trafficflows through a virtual link does not exceed its capacity.3) Flow Conservation Constraints in the Optical Layer:

(6)

Constraint (6) represents the flow conservation constraint inthe optical layer. It represents the fact that in all nodes the totaloutgoing wavelengths in a virtual link should be equal to thetotal incoming wavelengths except for the source and the desti-nation nodes of the virtual link.4) Physical Link Capacity Constraints:

(7)

(8)

Constraints (7) and (8) represent the physical link capacityconstraint. Constraint (7) ensures that the number of wavelengthchannels in virtual links traversing a physical link does not ex-ceeded the capacity of fibers in the physical links. Constraint (8)ensures that the number of wavelength channels in virtual linkstraversing a physical link is equal to the number of wavelengthsin that physical link.5) Number of Data Center Constraint:

(9)


Fig. 2. Irregular network topology with link distances in kilometer.

Constraint (9) gives the number of data centers.6) Number of Data Aggregation Ports Constraint:

(10)

Constraint (10) gives the total number of data aggregationports in each node as the total traffic flow from the node dividedby the capacity of a wavelength. The total traffic flow from anode consists of the regular traffic flow, the downlink traffic flowif the node is a data center and the uplink traffic flow (traffic todata centers).The model can be extended to represent the bypass approach

by redefining the power consumption of ports in regular nodesand data centers at time as follows:

Therefore, the objective function becomes:

B. Simulation and Results

To evaluate the energy savings introduced by optimizing thelocations of data centers in IP overWDMnetworks, we considerthe irregular topology depicted in Fig. 2 (we later consider amore regular topology in the form of NSFNET). The networkconsists of 10 nodes and 14 bidirectional links. We assume thatthe network has a single data center.

Fig. 3. Average traffic demand between regular nodes.

TABLE IINPUT DATA FOR THE SIMULATION

Fig. 3 shows the average traffic demand between regularnodes during different hours of the day [16]. The average trafficdemand between each node pair ranges from 20 to 120 Gb/s andthe peak occurs at 22:00. The traffic demand between nodes anddata centers is generated based on the regular traffic demand inFig. 3 where we assume that and . Thesevalues match the input and output rates of a typical data center[17].Table I shows the simulation environment parameters in

terms of number of wavelengths, wavelength capacity, distancebetween two neighboring EDFAs, and the power consumptionof different components in the network. Some of the parametersare similar to those in [8], which are derived from Cisco’s 8-slotCRS-1 data sheets [18]. The AMPL/CPLEX software was usedto solve the LP model.We evaluated the optimal location of the single (in this case)

data center through the LP model. We investigated the optimallocation under the non-bypass and the multi-hop bypass heuris-tics in two traffic scenarios: In the first traffic scenario, we onlyconsider the traffic to and from data centers. In the second sce-nario, we consider the traffic between regular nodes in additionto the data center traffic.For the non-bypass approach the LP model results give the

optimal data center location as node 5 under both traffic sce-narios. For the multi-hop bypass heuristic, the LP optimal datacenter location is node 7 and node 4 under the first and thesecond traffic scenarios, respectively. To verify the LP results,we evaluated, through simulation, the power consumption ofthe network assuming different locations of the data center (see


Fig. 4. (a). Total energy consumption of the irregular topology with a singledata center under different data center locations (x-axis) with different heuristicsconsidering only the data center traffic. (b) Total energy consumption of theirregular topology with a single data center under different data center locations(x-axis) with different heuristics considering the data center traffic and regulartraffic.

Fig. 4). From Fig. 4, the simulation results confirm the LPmodelresults for both heuristics under the two traffic scenarios.In Fig. 4(a) where we consider the first traffic scenario (only

the traffic to and from data centers), the optimal location hasachieved an average power saving of 37.5% and 11.2% com-pared to the worst location for the non-bypass and the multi-hopbypass, respectively. It is clear that under the non-bypass ap-proach the power savings introduced by optimizing the locationof the data center is more significant compared to the multi-hopbypass heuristic. This is due to the power requirements of thenon-bypass approach where an IP router port (the most powerconsuming component in a node) is required for each inter-mediate node; therefore, reducing the number of intermediatenodes by optimizing the location of the data center (i.e., reducethe average hop number) has a significant impact on the net-work power consumption. However, under the multi-hop by-pass, where IP routers are only required at the source and des-tination nodes, the location optimization will only affect thepower consumption of EDFAs, transponders, wavelength mul-tiplexers and demultiplexers and optical switches at interme-diate nodes whose power consumption is much lower than theIP routers ports power consumption.In Fig. 4(b), the energy consumption reduction achieved by

optimizing the location of data centers under the second trafficscenario (data center traffic and regular traffic) decreases to

Fig. 5. Propagation delay experienced by each node in the irregular topologyunder different data center locations.

17.2% and 6.3% for the non-bypass and multi-hop bypass,respectively. This is due to the fact that the energy consumptionattributed to regular traffic is not affected by optimizing thedata center location. Therefore, the saving compared to thetotal energy consumption is lower.Fig. 5 gives the propagation delay experienced by all the

nodes in the irregular topology in accessing the data centerunder different data center locations. Note that both the non-by-pass and multi-hop bypass heuristics are based on shortest-pathrouting. It is clear that the optimal (from an energy pointof view) data center location (node 5) has not increased thepropagation delay compared to other node choices. It is alsoclear that nodes at the center of the network are less affected bya change in the data center location compared to nodes at theedge as nodes in the center have a lower average hop count toother nodes in the network.To evaluate the impact of the optimization of data center lo-

cations in a realistic network, we considered the NSFNET net-work, depicted in Fig. 6 and used our LP models and simulators.The NSFNET network consists of 14 nodes and 21 bidirectionallinks and is considered to be more regular where all the nodeshave comparable nodal degrees. As NSFNET covers the US,different parts of the network fall in different time zones, i.e.,nodes experience different traffic demands at any given point intime. There are four time zones, Eastern Standard Time (EST),Central Standard Time (CST), Mountain Standard Time (MST)and Pacific Standard Time (PST). There is an hour time differ-ence between each time zone and the next; we use EST as thereference time. Note that time zones dictate habits, and, there-fore, network utilization and traffic demands in our case. Fig. 7shows the average traffic demands in different time zones.We optimized the location of the data centers for the NSFNET

with a single data center for the same traffic scenarios discussedearlier and using the parameters in Table I. The optimal loca-tion we obtained from running the LP model matches the simu-lator results in Fig. 8. The optimal data center location under dif-ferent heuristics and traffic scenarios is node 5, which is locatedat the center of the network. As discussed earlier, optimizingthe location of data centers minimizes the energy consumptionof the network by reducing the total power consumed by IP


Fig. 6. NSFNET network with time zones.

Fig. 7. Average traffic demand in different time zones.

router ports; transponders and EDFAs, which are related to thenumber of hops and distance between source and destination, re-spectively. Therefore, data centers should be located to provideoptimal number of hops and distance to all nodes. Under thefirst traffic scenario (only data center traffic), the optimal datacenter location has reduced the energy consumption by 26.6%and 12.7% compared to the worst location for the non-bypassand the multi-hop bypass, respectively. The difference betweenthe optimal and the worst location under the second traffic sce-nario (data center traffic and regular traffic) decreases to 8.6%and 4.6% for the non-bypass and the multi-hop bypass, respec-tively. Similar trends to those observed in Fig. 4 can be seenin Fig. 8 for NSFNET. However, the difference between thedifferent locations under the different heuristics and traffic sce-narios is reduced. This is due to the regularity of the NSFNETtopology where all nodes have comparable nodal degrees.In addition to the scenarios evaluated earlier, we evaluated a

scenario where the NSFNET has a larger number of data centers. Fig. 9(a) gives the optimal locations for data centers

under the different heuristics and traffic scenarios. The optimaldata center locations are distributed throughout the network toprovide optimal number of hops and distance to all nodes.Fig. 9(b) shows that the optimal data center locations for the

multi-hop heuristic with data center and regular traffic are dis-tributed throughout the network under different values ofand .In Figs. 10 and 11, we show the NSFNET power consump-

tion under the optimal data center locations in Fig. 9 obtained

Fig. 8. (a) Total energy consumption of the NSFNET network with a singledata center under different data center locations (x-axis) with different heuristicsconsidering only the data center traffic. (b) Total energy consumption of theNSFNET network with a single data center under different data center locations(x-axis) with different heuristics considering the data center traffic and regulartraffic.

from running the LPmodel for the different heuristics and trafficscenarios and compare it with the case where random nodes areselected to serve as data centers. The power consumption ob-tained from running the LP model under the optimal locationsrepresents a lower bound on the power consumption.In Fig. 10 compared with the random data center locations

under the non-bypass heuristic, the power consumption underthe optimal locations has been reduced by an average of 11.4%and 4.4% under the first and the second traffic scenarios, respec-tively. The power consumption obtained from the LP model islower than the power consumption obtained under the non-by-pass with shortest path routing as shortest path routing willnot necessarily result in the minimum number of hops, and thenumber of hops determines the number of IP ports used underthe non-bypass heuristic. The number of IP ports used has thelargest impact on power consumption.Under multi-hop bypass (see Fig. 11), the power consumption

has been reduced to an average of 6.5% and 1.7% under the firstand the second traffic scenario, respectively. In a fashion similarto the non-bypass case, shortest path routing results in higherpower consumption in the multi-hop bypass heuristic. How-ever, the difference between the LP model and the multi-hopbypass heuristic is smaller than the difference in the case of thenon-bypass heuristic as under the multi-hop bypass heuristic IP


Fig. 9. (a) Optimal data center locations under different heuristics and trafficscenarios. (b) Optimal data center locations for the multi-hop bypass heuristicunder different and values with data center and regular traffic.

router ports are eliminated at intermediate nodes; therefore, thenumber of hops becomes less critical.From the results mentioned earlier, power savings are higher

under the non-bypass heuristic. However, compared to the casewith one data center, the power savings achieved by optimizingthe multiple data center locations are limited. This is due to thefact that with a larger number of data centers the average dis-tance between a node and a data center is reduced. Therefore,optimizing the locations of data centers has a smaller effect onthe average number of hops, i.e., the distance between nodesand data centers, and, therefore, it has a limited effect on powerconsumption. Therefore, optimizing the location of data cen-ters is more critical if the number of data centers is small or thetopology is irregular as seen earlier in this section.Table II gives a summary of the power consumption savings

obtained under different topologies, heuristics, and number ofdata centers.Similar observations can be made in relation to delay in the

more regular NSFNET topology by comparing Figs. 12(a) and5, both under a single data center. Fig. 12(b) compares the prop-agation delay experienced by different nodes in the NSFNETnetwork with 5 data centers under the optimal data center lo-cations and under other random locations. The optimal (power

Fig. 10. (a) Power consumption of the NSFNET network with different datacenter locations in a 24 hour period under the non-bypass heuristic with 5 datacenters considering only the data center traffic. (b) Power consumption of theNSFNET network with different data center locations in a 24 hour period underthe non-bypass heuristic with 5 data centers considering the data center trafficand regular traffic.

consumption minimization criterion) data center locations havehad limited effect on the average propagation delay experiencedby different nodes in accessing data centers and as Fig. 12(b)shows, some nodes experience lower delay. Therefore, the de-veloped LP model optimizes the data center locations to min-imize the network power consumption while maintaining QoS(propagation delay here).

III. A REPLICATION SCHEME FOR THE IP OVER WDMNETWORK WITH DATA CENTERS

The results in Section II-B were obtained under the assump-tion that each data center has different content, i.e., a user in-terested in that particular content has to access it from the datacenter in question. However, in practice large operators (e.g.,BBC, YouTube, Amazon ) have multiple data centers wherethey replicate content (that has different popularity) to reducethe access delay experienced by users. In terms of power con-sumption, replicating data objects to multiple data centers al-lows a node to access a data object from a closer data center,and, therefore, reduces the power consumption by reducing thenumber of hops and the distance from source to destination. Inthis section, we investigate the power savings introduced by im-plementing a replication scheme in the IP over WDM networkwith data centers where we assume that data objects are repli-cated according to their popularity.


Fig. 11. (a) Power consumption of the NSFNET network with different datacenter locations in a 24 hour period under the multi-hop bypass heuristic with5 data centers considering only the data center traffic. (b) Power consumptionof the NSFNET network with different data center locations in a 24 hour periodunder the multi-hop bypass heuristic with 5 data centers considering the datacenter traffic and regular traffic.

TABLE IISUMMARY OF POWER CONSUMPTION SAVINGS OBTAINED UNDER OPTIMAL

DATA CENTER LOCATIONS


An LP model is developed to optimize the selection of datacenters to replicate data objects under the lightpath bypass ap-proach. In addition to the assumptions in Section II-A, the LPmodel is developed under the following assumptions:

Fig. 12. (a) Delay experienced by each node in the NSFNET network with asingle data center under different data center locations. (b) Delay experiencedby each node in the NSFNET network with 5 data centers under the optimaldata center locations.

1) The optimal data center locations are obtained using the LPmodel in Section II-A.

2) Data objects in the network are classified into five dif-ferent popularity groups. A traffic demand between a nodeand a data center is distributed among different data objectgroups according to their popularity. Previous research oncontent popularity [19]–[21] has established that the popu-larity of content can be approximated using a Zipf distribu-tion, which states that the relative probability of a requestfor the ’th most popular data object is proportional to .The Zipf’s distribution is given by [21]:

(14)

where

(15)

where is the number of data objects. In our scenario, weassume five data object groups . Therefore, thepopularity of the data object groups, is: 43.7%, 21.8%,14.5%, 10.9%, and 9%.


Fig. 13. Popularity versus total number of data centers.

3) Given that the least popular data object must exist in atleast one data center and assuming that the most popularobject exists in all data centers, a relationship can be con-structed different relationships can be constructed betweencontent popularity and the number of location where it ispresent. This does not however predetermine the locationof a data object based on its popularity. Different relation-ships can be constructed given the two points (maximumand minimum) discussed and shown in Fig. 13. The sim-plest being a linear relationship, which is the one selectedhere (see Fig. 13). Other Relationships (e.g., polynomial)can be investigated as an extension.The total number of data centers, , used to repli-cate a data object group, , is defined as a function of theobject’s popularity , as:

where is the set of data centers, and arethe popularities of the most and least popular data ob-jects, respectively. From (16) and given that andgiven the popularity values mentioned above we calculate

for all data object groups resulting in data objectgroups with the popularities 43.7%, 21.8%, 14.5%, 10.9%,and 9% having to exist in 5, 4, 3, 2, and 1 data centers, re-spectively.

4) We only consider data center traffic (uplink and downlinktraffic) when optimizing the replication locations (but wealso consider the mirroring traffic between data centers).We assume that data centers are synchronized.

5) We decompose the problem by solving the LP model eachtime for a particular data center X and a particular dataobject . While such decomposition yields a simplifiedtractable model, it may not lead to the global optimal solu-tion.

In addition to the parameters of the LP model in Section II-1,the following parameters are defined:

The data center with an original data object.

NN The set of normal nodes.

The total number of data centers used to replicatea data object group, , with popularity .

In addition to the parameters in Section II, the following vari-ables are defined:

The number of wavelength channels used fordownlink traffic demand on the virtual linkat time .

The number of wavelength channels used foruplink traffic demand on the virtual link attime .

if data center is chosen to replicate anobject present in data center , otherwise .

The downlink traffic demand from data center Xto node d.

The uplink traffic demand from node d to datacenter X.

The downlink traffic flow from data center tonode that traverses the virtual link at time.

The uplink traffic flow from node to data centerthat traverses the virtual link at time .

Objective: minimize

(16)

Note that we do not consider the power consumption of theaggregation ports in the objective function as it is related lin-early to the traffic demand and, therefore, will not affect the se-lection of the optimal replication locations.Subject to:

(17)


(18)

(19)

(20)

(21)

(22)

(23)

(24)

Constraints (17) and (18) represent the flow conservationconstraints. Constraint (17) ensures that the downlink trafficdemands from data center to node in the IP layer are canbe replicated to data center , split, and transmitted throughmultiple flow paths. Constraint (18) ensures that the uplinktraffic demands from node to the data center in the IPlayer can be replicated at data center , split, and transmittedthrough multiple flow paths. For constraints (17) and (18), theoptimization model first determines the location of the datacenters associated with an object, it keeps those locations andthen determines the location of the data centers associated withthe next object. This is not a global optimization (a simplifi-cation is introduced to maintain linearity); however, energysavings are obtained.Constraints (19) and (20) represent the virtual link capacity

constraints. They show that the downlink and uplink trafficflows cannot exceed the capacity of each virtual link.Constraint (21) represents the flow conservation constraints

in the optical layer. Constraint (22) states the number of datacenters used for replication. Constraint (23) and (24) representthe physical link capacity constraints.

B. Energy-Delay Optimal Routing Algorithm

Using shortest path routing to choose a replica of a data objectin the IP over WDM network can result in increasing the powerconsumption of the network as the shortest path may involvemore hops (hence IP ports), and furthermore more router portsand transponders may be required to establish a new virtual linkif enough capacity is not available on existing virtual links onthe shortest path. Therefore, we propose a new routing algo-rithm, EDOR, to route traffic demands to data objects. EDORaims to minimize the energy consumption while maintainingQoS (propagation delay). The flow chart associated with theEDOR algorithm is shown in Fig. 14.

Fig. 14. EDOR algorithm flowchart.

In this algorithm, all the traffic demands between data centersand nodes are reordered from the highest to the lowest and anempty virtual topology is created. A traffic demand is then re-trieved from the ordered list. All the available paths to all thedata centers containing the required data object are checked,e.g., if a data object is stored in 2 data centers, then there aretwo possible destinations. If more than one path has sufficientcapacity, the required data center with the shortest available pathis selected in order to reduce the propagation delay. If sufficientcapacity is not available in the virtual topology, a new virtuallink is established between the node and the data center withthe minimum number of hops in order to minimize the powerconsumption by reducing the number of transponders in inter-mediate nodes. After routing the traffic demand, the remainingcapacity on all the virtual links is updated. The earlier process isrepeated for all the traffic demands. After routing all the trafficdemands on the virtual topology, the total power consumptionof the network is calculated.Under the EDOR algorithm the same data object exists in

multiple data centers, so the data center routing problem be-comes a form of “anycasting,” which introduces a degree offreedom in selecting the destination with the minimum energyconsumption. The multi-hop bypass heuristic proposed in [8]is a unicasting algorithm, i.e., the destination is predetermined,and only the most energy efficient route is selected.

C. Simulation and Results

In this section, we identify the impact of the replicationscheme in the NSFNET network with 5 data centers. The data


Fig. 15. Power consumption of the IP over WDM network with optimal loca-tions of data centers under the non-bypass heuristic with and without replication.

center traffic demand and other parameters are similar to theassumptions in Section II-B.In Fig. 15, we evaluate the performance of the replication

scheme under shortest path routing with the non-bypass scheme.Simulation results are reported under the optimal data center lo-cations ((5, 6, 8, 10, and 13) obtained from running the modelin Section II-A for the non-bypass heuristic) considering datacenter traffic and regular traffic. We determine the optimal se-lection of data centers to replicate data objects through the useof the LP model in Section III-A. Implementing the replicationscheme has resulted in an average power saving of 28%. Thissignificant saving is due to the reduction in the number of hopsand the distance between data centers and nodes (as under repli-cation objects are available in multiple data centers).We have also evaluated results under EDOR with the optimal

data center locations (3, 5, 8, 10, and 12) using the model inSection II-A for the multi-hop bypass heuristic considering thedata center traffic and regular traffic. We determined the optimalselection of data centers to replicate data objects from the LPmodel in Section III-A. Fig. 16 gives the power consumption ofthe IP over WDM network with data centers with and withoutthe replication scheme. It is clear that implementing the replica-tion scheme has resulted in power savings under both shortestpath routing and the EDOR algorithm. The difference be-tween the EDOR algorithm and the multi-hop bypass heuristicwith shortest path routing can be seen in Fig. 17, where weshow the power saving introduced by the replication schemeunder the two algorithms compared to the multi-hop heuristicwithout replication. While the EDOR algorithm achieves anaverage power saving of 4.5%, the multi-hop bypass heuristicwith shortest path routing average power saving is limitedto 3.7%. This is because the EDOR algorithm allows moretraffic demands to share the capacity on common virtual links,and, therefore, a smaller number of new virtual links need tobe established. It also routes using a minimum hop criterion(minimum number of IP ports, switches, transponders andmultiplexers and demultiplexers) when there is no sufficientcapacity on established lightpaths. Between 04:00 and 08:00,the difference between the two algorithms reaches its peak asduring this time period the average traffic demand is the lowest,and is lower than the capacity of a wavelength (see Fig. 7). The

Fig. 16. Power consumption of the IP over WDM network with optimal lo-cations of data centers under the EDOR algorithm and the multi-hop bypassheuristic with shortest path algorithm with and without replication.

Fig. 17. Reduction in the power consumption of the IP over WDM networkwith optimal locations of data centers under the EDOR algorithm and themulti-hop bypass heuristic with shortest path routing.

EDOR algorithm uses all available virtual links with sufficientcapacity; therefore, more traffic demands share the capacity oncommon virtual links.From Figs. 15--17, it is clear that the power savings achieved

by replication are more significant under the non-bypassheuristic compared to the multi-hop bypass heuristic with theshortest path routing and the EDOR algorithm. This differencecomes from the difference in power requirements between thenon-bypass multi-hop heuristics discussed in Section II-B.Fig. 18 gives the average propagation delays under different

algorithms. The multi-hop bypass heuristic without the replica-tion scheme gives the upper bound on the propagation delay.The multi-hop bypass heuristic with the shortest path algorithmresults in the lowest propagation delay (2.59 ms). With theEDOR algorithm, the propagation delay has not increasedsignificantly (the increase is less than 0.2 ms, i.e., less than8% compared to the lowest propagation delay) maintainingthe QoS. It should be noted that while the propagation delayof the multi-hop bypass heuristic without replication and withthe shortest path algorithm are almost constant in a 24 hourperiod, the average propagation delay of the EDOR algorithmfluctuates slightly as the routing paths are dynamic.


Fig. 18. Average propagation delays of IP over WDM network with optimallocations of data centers under different algorithms.

IV. APPLYING RNEWABLE ENERGY IN THE IP OVER WDMNETWORK WITH DATA CENTERS

In this section, we investigate introducing renewable energysources to the IP over WDM network with data centers andstudy a scenario where moving bits to where renewable en-ergy is (wind farms) is evaluated and compared to transportingrenewable energy to data centers. We study the impact of thepower losses associated with transporting electrical power todata centers on the optimal data centers locations. We also studythe impact of the other networking factors including networktopology, routing, and traffic. We further assume that solar en-ergy is employed to partly power regular nodes. Typically, a onesquare meter silicon solar cell can produce about 0.28 kW ofpower [22]. We assume that the maximum solar power avail-able to a node is 20 kW; therefore, a total solar cell area of about100 m is required, which can be practically built in a typicalcore routing node location. However, as the power consumptionof a data center is high, due to high traffic demand to and fromthe data center and the high consumption of the computing andcooling equipment inside the data center, the power generatedby limited size solar cells built in the local site will not be suf-ficient to power a data center. Therefore, we assume that datacenters are powered by energy generated from wind farms.


The LP model in Section II-A is extended to support the ob-jective of minimizing the non-renewable energy consumptionof data centers by optimizing the locations of data centers in theIP over WDM network assuming the lightpath bypass approachbut taking into account renewable energy sources and the trans-mission losses. In addition to the assumptions of the LP modelin Section II-A, the following assumptions are made:1) Renewable energy is only available to the ports andtransponders in regular nodes as these are the most powerconsuming elements in a node. Renewable energy fromwind farms is, however, also available to power the com-puting servers, cooling, and lighting.

2) Data centers and regular nodes have access to non-renew-able energy to guarantee QoS at all time in case the renew-able energy is low.

3) Each data center has access to only a single wind farm.Note that If the data center is connected to more than asingle wind farm, then more than one transmission linewill have to be constructed increasing the cost. Alterna-tively, the power from the wind farm can be injected intothe power grid and the data center can be connected to thepower grid; however, it is difficult to evaluate of the powerlosses attributed to the data center location in a complexpower grid network. From a power loss point of view, con-necting a data center to multiple wind farms, e.g., threewind farms can reduce the transmission power losses if thepower remains the same and the distance, d, to each windfarm (three loss components, each proportional to );however, the installation cost becomes high. Therefore, wehave selected the simplest/lowest cost option, where eachdata center is connected to a single wind farm.

4) A fraction of the total output power of a wind farm , de-noted as , is assumed to be available to power data cen-ters.

5) The power transmission loss is the power lost intransmitting renewable energy from wind farm to datacenter .

6) To be able to evaluate the non-renewable energy consump-tion separately from the renewable energy consumption,we redefine the variables representing the number of wave-length channels and traffic flows.

In addition to the parameters in Section II-A, the followingparameters are defined:

The set of wind farms.

The output power of wind farm attime .The wind power consumption of arouter port, which is equal to PR.The solar power consumption of arouter port, which is equal to PR.The wind power consumption of atransponder, which is equal to PT.The solar power consumption of atransponder, which is equal to PT.The power consumption associatedwith cooling in a data center.The power consumption associatedwith computing in a data center.The output power of solar cells ofnode at time .

The following variables are defined:

The number of wavelength channels carryingdata center traffic in the virtual link whichstart or end at a data center and are powered bynon-renewable energy at time t.The number of wavelength channels carrying datacenter traffic in the virtual link which startor end at a data center and are powered by windenergy at time .The number of wavelength channels carryingdata center traffic in the virtual link whichstart or end at regular nodes and are powered bynon-renewable energy at time .


The number of wavelength channels carrying datacenter traffic in the virtual link , which startor end at a regular node and are powered by solarenergy at time .The number of wavelength channels carryingdata center traffic in the virtual link , whichstart or end at a regular node (connected to a datacenter) and are powered by wind energy at time .The number of wavelength channels carryingregular traffic in the virtual link , which arepowered by non-renewable energy at time .The number of wavelength channels carryingregular traffic in the virtual link , which arepowered by wind energy at time .The number of wavelength channels carryingregular traffic in the virtual link , which arepowered by solar energy at time .The downlink traffic flow from data center tonode that traverses the virtual link at time. This is associated with data centers (dc).The downlink traffic flow from data center tonode (for regular node) that traverses the virtuallink at time . This is associated with regularnodes .The uplink traffic demand from node to datacenter (for data center) that traverses the virtuallink at time . This is associated with datacenters (dc).The number of wavelength channels carryingdata center traffic in the virtual link whichtraverses physical link that starts or ends ata data center at time t.The number of wavelength channels carryingdata center traffic in the virtual link whichtraverses physical link that starts or ends ata regular node at time t.The number of wavelength channels carryingregular traffic in the virtual link whichtraverses physical link at time t.The uplink traffic demand from node to datacenter (for regular node) that traverses thevirtual link at time . This is associated withregular nodes .The number of wavelength channels carrying datacenter traffic in the physical link whichstart or end at a data center and are powered bynon-renewable energy at time .The number of wavelength channels carrying datacenter traffic in the physical link , whichstart or end at a data center and are powered bywind energy at time .The number of wavelength channels carrying datacenter traffic in the physical link , whichstart or end at a regular node and are powered bynon-renewable energy at time .The number of wavelength channels carrying datacenter traffic in the physical link whichstart or end at a regular node and are powered bysolar energy at time .

The number of wavelength channels carrying datacenter traffic in the physical link , whichstart or end at a regular node (connected with adata center) node and are powered by wind energyat time .The number of wavelength channels carryingregular traffic in the physical link poweredby non-renewable energy at time .The number of wavelength channels carryingregular traffic in the physical link poweredby solar energy at time .The number of wavelength channels carryingregular traffic in the physical link poweredby wind energy at time .The number of ports in node used for dataaggregation powered by non-renewable energyat time .The number of ports in regular node used fordata aggregation powered by solar energy at time.The number of ports in data center used for dataaggregation powered by wind energy at time .

if node is a data center and has access towind farm , otherwise, .

The power consumption of optical switches Multiplexers/de-multiplexer and EDFAs have the same definition as inSection II-A. However, the power consumption of routerports and transponders is redefined as follows:1) The power consumption of ports in regular nodes and datacenters at time

2) The power consumption of transponders in regular nodesand data centers at time

We also include the power consumption of the cooling andcomputing equipment inside the data center in the total non-renewable energy consumption.The LP model is defined as follows:Objective: minimize


(25)

Subject to:

(26)

(27)

(28)

(29)

(30)

(31)

(32)

(33)

(34)

(35)

(36)

(37)

(38)

(39)

(40)

(41)

(42)


Fig. 19. NSFNET network with wind farms and time zones.

(43)

(44)

Constraints (26)–(28), (29)–(31), (26)–(28), (32)–(34), (38),(39)–(41), (43), and (44) replace constrains (2)–(41) and (5), (6),(7), (8), (9), (10), respectively in Section II-A. Constraint (35)states that the renewable energy consumption of ports, transpon-ders, cooling, and computing in a data center should not belarger than the power provided by a single wind farm taking intoaccount the transmission losses. Constraint (36) ensures that therenewable energy consumption of ports and transponders at aregular node does not exceed the solar power available at thenode. Constraint (37) ensures that the renewable energy con-sumption of all data centers does not exceed the power providedby all wind farms. Constraint (42) ensures that one data centercan only access one wind farm.

B. Simulation and Results

The NSFNET network is considered as an example networkto identify the optimal location of data centers using the LPmodel. We have selected only three wind farms based on theirlocation and maximum output power [23], [24] to power thedata centers in the network (see Fig. 19): 1) WF1: Cedar CreekWind Farm, 2) WF2: Capricorn Ridge Wind Farm, 3) WF3:Twin Groves Wind Farm, all three in blue. The wind Farms areshown in Fig. 19. The maximum output power of the three windfarms is 300, 700, and 400 MW, respectively.The solar power [25] available to a node is shown in Fig. 20.

The geographical location of nodes affects the sunset and sun-rise time and, therefore has impact on the solar energy generated

Fig. 20. Solar power in different nodes at different geographic locations.

in each node. In [12], we give details of the solar power avail-able in each node. This is nonzero from 6:00 to 22:00, and themaximum output power occurs at 12:00.We assume the transmission power loss is 15% per 1000

km [26], and the percentage of the power of wind farms al-located to data centers is assumed to be 0.3%. As mentionedin Section I, the power consumption of a typical data center is150–200W/ft . Assuming a 3500 ft data center, the total powerconsumed in such a (typical) data center for cooling is 700 kWand the computing power consumption in a data center is as-sumed to be 300 kW, which is typical for this data center size.The power allocated by a wind farm to a data center is knownand is assumed here to be 1.4 MW. This corresponds to a powerusage efficiency (PUE) of 2, which is typical for a data center[27]. The renewable energy available to a data center is a func-tion of the transmission losses, and these are location dependent.Furthermore, the network topology, traffic, and components’power consumption also play an important role in determiningthe optimum data center location as in Section II. Therefore, theLP model in this section takes into account the Section II trade-offs as well as the tradeoffs introduced by the losses associatedwith the transmission of renewable energy to the data centerlocation. These losses reduce the renewable power available


to a node for communications purposes; hence, the LP modelidentifies the optimum data center locations by minimizing thenon-renewable power consumption of the network.We run the LP model with five data centers under

the same assumptions as in Section II-B. The optimal locationsof data centers obtained from the LP model are as follows (4, 5,6, 7, 8) where data centers 4 and 5 are powered by WF1, datacenter 6 and 7 are powered byWF2, and data center 8 is poweredby WF3. The LP model results are such that all the data cen-ters are located in the center of the network. Unlike the resultsin Section II-B, where the data centers selection was only dic-tated by the number of hops and distance between data centersand nodes, in the presence of renewable energy and transmis-sion losses, the selection of data centers locations is also con-trolled by the utilization of the renewable energy resources, asthe amount of energy available from wind farms and solar cellsat regular nodes is limited. Therefore, nodes at the center of thenetwork are selected to serve as data centers so that they can uti-lize the wind farm renewable power more efficiently and, con-sequently, result in higher reductions in the total non-renewablepower consumption compared to the selection of nodes at theedge.Fig. 21 shows the non-renewable power consumption under

the optimal data center locations obtained from the LP modeland simulations under the multi-hop bypass heuristic. This iscompared to the case where random nodes (2, 5, 9, 13 and 14)are selected to serve as data centers, powered by the nearestwind farm. The non-renewable power consumption obtainedfrom the LP model under the optimal locations represents alower bound. Compared with the random locations, the non-re-newable power consumption under the optimal locations hason average reduced the non-renewable power consumption by26.2% for the LP model and by 4.9% for the multi-hop bypasswith shortest path routing (simulation results). The random lo-cations result in larger hop numbers and distances between datacenters and nodes and, consequently, higher power consump-tion. In addition, the random locations reduce the utilization ofthe renewable energy from wind farms and solar cells and, con-sequently, increase the non-renewable power consumption. Thedifference between the LP model and the multi-hop bypass withshortest path routing (simulation results) is due to the same rea-sons discussed in Section II-B.The LPmodel was used to determine the power consumption,

assuming that the wind farms are located, as shown in Fig. 19in red. The optimal locations of data centers obtained from theLP model in this case are as follows (1, 3, 4, 12, 13), wheredata centers 1 and 3 are powered by WF1, data center 4 is pow-ered by WF2, and data centers 12 and 13 are powered by WF3.It can be observed that as with the first scenario the optimumdata centers locations are next to or near wind farms. In Fig. 22,we evaluated the non-renewable power consumption under theoptimal locations (1, 3, 4, 12, 13). Compared with random loca-tions, the non-renewable power consumption under the optimallocations has been reduced by an average of 20.8% under theLP model solution and by an average of 2.8% for the multi-hopbypass with shortest path routing (simulation results).Fig. 23(a) shows that under the optimal data center locations

for the first wind farms location scenario, introducing renewable

Fig. 21. Nonrenewable power consumption of the IP over WDM network withdifferent locations of data centers under the multi-hop bypass heuristic in a 24hour period.

Fig. 22. Nonrenewable power consumption under the multi-hop bypassheuristic in the IP over WDM network assuming optimal data center locationsunder different wind farm locations.

energy to the network (wind farms next to data centers and solarcells in regular nodes) has reduced the non-renewable powerconsumption under the different heuristics. Fig. 23(b) gives thereductions in the non-renewable power consumption under thedifferent heuristics and scenarios. The results are compared withthe non-bypass heuristic (with shortest path routing) without re-newable energy representing the upper bound on the nonrenew-able energy consumption. The multi-hop bypass heuristic (withshortest path routing) without renewable energy introduces av-erage savings of 46%. The figure also shows that the savingsdue to introducing renewable energy (non-bypass with renew-able energy) has achieved an average reduction of 58%. Com-bining the multi-hop bypass heuristic with renewable energy in-creases power saving to an average of 77% obtained from theLP network design and 71% obtained from the simulation. In-troducing the replication scheme increases the average savingto 73% (simulation results).Fig. 23(c) shows the total power consumption of the network

under the non-bypass and multi-hop bypass heuristics with andwithout renewable energy. It is clear that the optimal data centerlocations maintain the total power consumption of the network


Fig. 23. (a) Nonrenewable power consumption of the IP over WDM networkunder different heuristics with and without renewable power in the IP overWDM network assuming optimal data center locations. (b) Reductions in non-renewable power consumption under different heuristics with and without re-newable power in the IP overWDM network assuming optimal data center loca-tions. (c) Total power consumption under different heuristics with and withoutrenewable power in the IP over WDM network assuming optimal data centerlocations.

by minimizing the power loss due to the transmission of renew-able energy from wind farms to data centers. The total trans-mission loss throughout the day is insignificant compared to thesavings in CO emissions.

V. CONCLUSION AND DISCUSSION

This paper has investigated the power consumption of IP overWDM networks with data centers where data centers create ahot node scenario leading to a significant increase in the powerconsumption. We have investigated three problems. In the first

problem, we studied the optimization of the data center loca-tions with the objective of minimizing the power consumptionof the network. We have developed an LP model and carriedout simulations. The LP model and simulation results show thatthe power savings obtained by optimizing the data center lo-cations depend on three factors: the IP over WDM routing ap-proach implemented (lightpath bypass or non-bypass), regu-larity of the network topology and the number of data centersin the network. Comparing the non-bypass and the multi-hopbypass heuristics show that while power savings up to 37.5%are obtained under the non-bypass heuristic, the savings underthe multi-hop bypass heuristic are limited to 11.2%. This is dueto the fact that in networks implementing the non-bypass ap-proach where IP router ports are required at intermediate nodesoptimizing the location will result in reducing the number ofhops, and therefore, the number of router ports needed at inter-mediate nodes. On the other hand, under the multi-hop bypassheuristic the power saving is less significant as the location op-timization only reduces the number of EDFAs and transponderswhose power consumption is limited compared to router ports.The results also show that the location of data centers has sig-nificant effect on the power consumption of the network for net-works with irregular topologies and fewer number of data cen-ters as optimizing the locations of data centers has significantimpact on reducing the average hop number and distance be-tween nodes and data centers, and, consequently, the power con-sumption. Comparing the results of the irregular topology withthe result of the NSFNET topology, we have observed that thepower consumption savings have been reduced by an average of10% under the non-bypass heuristic. NSFNET network resultshave shown that while the power saving obtained as a result ofoptimizing the location of a single data center is up to 26.6%,the savings with 5 data centers are limited to 11.4%. Note thatthe power saving obtained from data center locations optimiza-tion comes at no extra cost in terms of bandwidth, delay, orstorage. In the second problem, we have studied the power sav-ings introduced by implementing a data replication scheme inthe IP overWDM network with data centers. A novel algorithm,Energy-Delay Optimal Routing (EDOR), has been proposed tominimize the power consumption under the replication schemewhile maintaining the QoS. Simulation results show that imple-menting the replication scheme under the non-bypass heuristicwith shortest distance routing has resulted in an average powersavings of 28%. This significant reduction is due to the reductionin the average number of hops and distance between data cen-ters and nodes. The power saving achieved is reduced to 4.5%under the multi-hop bypass with the EDOR algorithm. This dif-ference is due to the difference in power requirements betweenthe non-bypass multi-hop heuristics. The results also show thatwith the EDOR algorithm the increase in the propagation delayis limited to less than 8% compared to the propagation delaywith shortest distance routing. Note that the LP model for theNSFNET network utilizes a huge number of variables (about 2million). Therefore, the LP approach will not scale for networkswith a very large number of nodes and data centers. However,the results show that with a larger number of data centers thedata center location optimization is less important (savings arelimited to 4.4% and 1.7%). Therefore, the problem in this case


becomes that of optimizing the routing, and we have shown thatenergy consumption results obtained from the simple heuristicswe have developed are close to the optimal LP model results. Inthe third problem, we have evaluated the use of renewable en-ergy (wind and solar energy) to reduce the non-renewable powerconsumption, and, consequently, the CO emission of IP overWDM networks with data centers. A LP model is developed tooptimize the location of data centers taking into account the lo-cation of renewable energy sources (wind farms) and the trans-mission power losses. It was determined that following the opti-mization all the data centers are located in the center of the net-work as the selection of data centers is controlled by the utiliza-tion of the renewable energy resources in addition to the numberof hops and distance between data centers and nodes. The resultsshow that moving the data centers closer to renewable energysources maximizes the utilization of renewable energy sourcesand, consequently, reduces CO emissions. By combining themulti-hop bypass heuristic with renewable energy and the repli-cation scheme power consumption savings up to 73% have beenachieved. The results also show that the optimal data center lo-cations minimize the power losses associated with the transmis-sion of renewable energy from wind farms to data centers.

ACKNOWLEDGMENT

The authors would like to thank the University of Cambridgefor the useful discussions with their collaborators.

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Xiaowen Dong received the B.E. degree in electronic engineering from South-west Jiaotong University, Chengdu, China, in 2005, and the M.E. degree (withFirst Class Honors) in electronic engineering from the National University ofIreland, Maynooth, Ireland, in October 2008. He is currently working towardthe Ph.D. degree in the School of Electronic and Electrical Engineering, Uni-versity of Leeds, Leeds, U.K.From 2005 to 2007, he was a Wireless Communication System Engineer in

Wuhan Research Institute, Wuhan, China. His current research interests includeenergy aware optical networks, energy efficient routing method, and protocolsin optical networks.

Taisir El-Gorashi received the B.S. degree in electrical and electronic engi-neering from the University of Khartoum, Sudan, in 2004, the M.Sc. degree inphotonic and communication systems from the University of Wales, Swansea,U.K., in 2005, and the Ph.D. degree in optical networking from the Universityof Leeds, Leeds, U.K., in 2010.She is currently a Postdoctoral Research Associate in the School of Electronic

and Electrical Engineering, University of Leeds. Her research interests includenext-generation optical network architectures and green Information and Com-munication Technology.

Jaafar M. H. Elmirghani received the B.Sc. degree (first-class hons) in elec-trical engineering from the University of Khartoum, Sudan, in 1989 and thePh.D. degree from the University of Huddersfield, U.K., in 1994 for work onoptical receiver design and synchronization.He is currently the Director of the Institute of Integrated Information Systems,

School of Electronic and Electrical Engineering, University of Leeds, Leeds,U.K. He joined Leeds in 2007, and prior to that during 2000–2007 hewas a Chairin optical communications at the University of Wales, Swansea. He founded,developed, and directed the Institute of Advanced Telecommunications and theTechnium Digital (TD), a technology incubator/spin-off hub. He has providedoutstanding leadership in a number of large research projects at the IAT andTD. He is a coauthor of Photonic Switching Technology: Systems and Networks


(Wiley) and has published over 300 papers. His research interests include opticalsystems and networks and signal processing.Dr. Elmirghani is Fellow of the IET, Fellow of the Institute of Physics and Se-

nior Member of IEEE. He was Chairman of IEEE Comsoc Transmission Accessand Optical Systems technical committee and was Chairman of IEEE ComsocSignal Processing and Communications Electronics technical committee, andan editor of IEEE Communications Magazine. He was founding Chair of theAdvanced Signal Processing for Communication Symposium which started atIEEEGLOBECOM’99 and has continued since at every ICC andGLOBECOM.Dr. Elmirghani was also founding Chair of the first IEEE ICC/GLOBECOM

optical symposium at GLOBECOM’00, the Future Photonic Network Tech-nologies, Architectures and Protocols Symposium. He chaired this Symposium,which continues to date under different names. He received the IEEE Commu-nications Society Hal Sobol award, the IEEE Comsoc Chapter Achievementaward for excellence in chapter activities (both in 2005), the University ofWalesSwansea Outstanding Research Achievement Award, 2006 and the IEEE Com-munications Society Signal Processing and Communication Electronics out-standing service award, 2009.

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Green IP Over WDM Networks With Data Centers

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