-
ew
iore
ordi Ga 29,126 N
Article history:Received 30 September 2011
the ecological footprint of the network elements (NEs)
isexplicitly taken into account. In this scenario, governmentsand
society are endorsing the development of greenrenewable energy
sources (such as solar panels, wind
Recent studies [1] conrm that the use of opticaltechnology in
high-capacity switches and routers is moreenergy-efcient than
electronic technology and that cir-cuit-switched architectures
consume signicantly lessthan their packet-switched counterparts.
However, despitethe recent efforts in improving the energy-efciency
of theinvolved technological components [2], the amount ofpower to
be spent worldwide for powering network
1389-1286/$ - see front matter 2012 Elsevier B.V. All rights
reserved.
Corresponding author.E-mail addresses: [email protected] (S.
Ricciardi), [email protected]
(F. Palmieri), [email protected] (U. Fiore).
Computer Networks 56 (2012) 24202442
Contents lists available at SciVerse ScienceDirect
Computer N
.e lshttp://dx.doi.org/10.1016/j.comnet.2012.03.0161.
Introduction
The containment of power consumption and the reduc-tion of the
associated green house gases (GHG, mainly CO2)emissions are
emerging as new challenges for telecommu-nication operators. In
fact, the rising energy costs due tothe scarcity of fossil fuels,
the increasingly rigid environ-mental standards and the growing
power requirementsof modern high-performance networking devices
areimposing new constraints, further stressing the require-ments
towards an energy-aware business model in which
turbines, and geothermal plants) for powering NEs. Greenenergy
sources are preferable with respect to the tradi-tional dirty ones
(e.g. coal, fuel, gas) since they do notemit GHG in the atmosphere
while producing electricalenergy. Nonetheless, green energy sources
(e.g. wind,sun, tide) are not always available at all sites and
arevariable with time; for this reason, the NEs that are pow-ered
by green energy sources are also provided with thelegacy, dirty
sources. At the occurrence, the smart gridpower distribution system
switches to the dirty powersupply without any energy
interruption.Received in revised form 23 February 2012Accepted 20
March 2012Available online 29 March 2012
Keywords:Green networksCross-layer optimizationsEnergy
consumptionGHG emissionsPower demand in networking equipment is
expected to become a main limiting factor andhence a fundamental
challenge to ensure bandwidth scaling in the next generation
Inter-net. Environmental effects of human activities, such as CO2
emissions and the consequentglobal warming have risen as one of the
major issue for the ICT sector and for the society.Therefore, it is
not surprising that telecom operators are devoting much of their
efforts tothe reduction of energy consumption and of the related
CO2 emissions of their networkinfrastructures. In this work, we
present a novel integrated routing and wavelength assign-ment
framework that, while addressing the traditional network management
objectives,introduces energy-awareness in its decision process to
contain the power consumptionof the underlying network
infrastructure and make use of green energy sources
whereverpossible. This approach results in direct power, cost and
CO2 emissions savings in the shortterm, as demonstrated by our
extensive simulation studies.
2012 Elsevier B.V. All rights reserved.a r t i c l e i n f o a b
s t r a c tAn energy-aware dynamic RWA framwavelength-routed
networks
Sergio Ricciardi a,, Francesco Palmieri b, Ugo FJosep Sol-Pareta
a
aDepartment of Computer Architecture, Technical University of
Catalonia, c. JbDepartment of Information Engineering, Second
University of Naples, v. RomcCenter of Information Services,
University of Naples Federico II, v. Cinthia, 80
journal homepage: wwwork for next-generation
c, Davide Careglio a, Germn Santos-Boada a,
irona 1-3, 08034 Barcelona, Spain81031 Aversa, Italyaples,
Italy
etworks
evier .com/locate /comnet
-
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2421infrastructures can be globally quantied in the order oftens of
gigawatts, corresponding to more than 1% of theworldwide
electricity consumption [2] (to give an idea,the equivalent of 22
nuclear reactors are needed to gener-ate such a huge power demand).
Thus, limiting power con-sumption in network infrastructures can
bring greatbenets and reduce their overall ecological footprint,
sothat the need for a greener, energy-aware Internet is rap-idly
becoming a fundamental political, social and commer-cial issue.
Furthermore, with the ever increasing demandfor bandwidth,
connection quality and end-to-end interac-tivity, computer networks
are requiring more and moresophisticated and power-hungry devices,
such as high-end routers, signal regenerators, optical ampliers,
recon-gurable add-and-drop multiplexers and very fast
(digitalsignal) processing units. These components tend to
in-crease the energy needs of global communication expo-nentially
so that power consumption is becoming asignicant limiting factor
for the overall scalability ofnext-generation high-capacity
telecommunication net-works. In the next years, large-scale optical
transport infra-structures will no longer be constrained mainly by
theircapacity, but rather by their energy consumption costsand
environmental effects [3].
As a consequence, it is necessary to envisage how
thenext-generation network architectures and protocols canbe modied
to meet the purpose of energy-efciency.Unfortunately, the rush for
achieving energy-efciencyresulted in the fact that many of the
solutions proposedto-date (e.g. [4,5]) tend to minimize only the
energy con-sumption of the networks while disregarding the
tradi-tional network management goals such as the overallnetwork
load-balancing. It is instead mandatory to guaran-tee that the
above modications will not adversely affectthe fundamental
operators optimization objectives ofkeeping the resource usage
fairly balanced, to save on eachavailable link sufcient free
capacity for demands that mayreasonably emerge in the
infrastructure operating lifetime,and minimizing the network usage
costs, considered as astatic way of expressing operator preference
to choosesome favorite link resources. In the ideal case, new
solu-tions should not only lower the ecological footprint, butalso
increase the offered quality-of-service such as theconnection
blocking probability.
Starting from the above considerations, we introduce[6]
energy-awareness into control plane protocols whosegoal is to
properly condition the route/path selectionmechanisms on relatively
coarse time scales by privilegingthe use of green energy sources
and energy-efcient links/switching devices, simultaneously taking
advantage fromthe different users demands across the interested
networkinfrastructures. The selected paths are likely not to be
theshortest or best ones, but the resulting power and GHGsavings
are substantial, and possible losses on the otheroptimization
objectives (i.e. number of blocked connectionrequests) are taken
into account and kept as low as possi-ble. In such a way, the
overall power consumption andGHG emissions can be minimized while
the traditionaloptimization objectives (such as load-balancing) are
notdisrupted. In doing this, we combine all the notable fea-tures
that a comprehensive energy-aware network modelshould have and put
them together into a general routingand wavelength assignment (RWA)
framework.
The RWA problem is known to be NP-complete [7] andin the dynamic
case no optimality is possible since there isno previous knowledge
of the connection requests that willbe handled by the network.
Therefore, we introduce a newheuristic method for efciently
calculating (in polynomialtime) the routing information subject to
power consump-tion constraints, taking into account also the specic
kindof energy source (dirty or green) used for powering
thetraversed NEs. In order to evaluate the performance ofour
approach, we compared our approach with well-known RWA algorithms
in the literature. The proposedapproach, that in the following will
be referred to as Green-Spark, introduces energy-awareness into the
Spark frame-work [8], which is a two-stage integrated RWA
schemestructured in a pre-selection phase where a number of
kcandidate paths satisfying the connection constraints arefound and
a nal selection stage where the optimum pathamong the candidates
determined in the previous phase ischosen according to a properly
crafted heuristic. Green-Spark is a simple and effective two-stage
on-line RWAscheme providing wavelength routing as well as
groomingcapabilities in the state-of-the-art hybrid
electric-opticalnetwork infrastructure. In its rst stage, this
enhancedRWA scheme nds, for each new connection request, aset of
feasible lightpaths satisfying both the users specicend-to-end
demands (QoS, bandwidth, etc.) and traditionaloptimization
objectives, while in the second stage it basesits nal choice on the
aforementioned power and GHG con-tainment requirements. In the end,
it nally achieves anoptimal trade-off between energy optimization
and net-work/users requirements in an affordable computationaltime.
GreenSpark differs from Spark for the second stage,which has been
here introduced to meet the energy-re-lated criteria. Furthermore,
Spark used a special parameter(kHop) to explicitly limit the length
of lightpaths, whilst inGreenSpark this is not needed anymore due
to the additivenature of the energy consumption function: longer
pathswill have higher energy consumption and, thus, will havelower
probability to be chosen for the routing of theconnections.
GreenSpark is based on a totally exible network modelsupporting
heterogeneous equipment, in which the num-ber and type of lambdas
can vary on each link or node,together with the associated power
consumption, and pro-vides a fully dynamic path selection scheme in
which thegrooming policy is not predetermined but may vary,
alongwith the evolution of the network trafc. We explicitlyconsider
the inuence of trafc on power consumptionby using realistic data
for trafc demands, network topol-ogies, link costs, and energy
requirements of the NEs.
This approach is also based on deeper network engi-neering
considerations that make it behave very differentlyfrom the other
already existing energy-aware networkingapproaches, mainly based on
the concept of temporarilyswitching off entire devices or
subsystems (the least usedones) in order to minimize energy
consumption by rerout-ing the involved trafc. Such approaches,
often referred assleep mode [9], may be unpractical, especially for
large andhighly connected switching nodes, since many very
-
sented by Idzikowski et al. [5]. They analyzed the effects
of
Most of these approaches are characterized by a limited
grooming capability is required on opto-electronic routers
2422 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442reconguring routing, at the different layers, by
assumingcomplete wavelength conversion capability in each node.In
[20], Chiaraviglio et al. have proposed and evaluatedsome greedy
heuristics based on the ranking of nodesand links with respect to
the amount of trafc that theywould carry in the context of an
energy-agnostic congu-ration. Silvestri et al. [21] combined trafc
grooming andtransmission optimization techniques to limit energy
con-sumption in the WDM layer. Trafc grooming shifts trafcfrom some
links to other ones in order to switch emptyones off, and
transmission optimization adjusts dispersionmanagement and pulse
duration, which decreases theneed for using in-line 3R
regenerators. The power savingsthat can be achieved by dynamically
adapting the networktopology to the trafc volume are investigated
in [22],expensive transmission links become unused, hence leav-ing
signicant capital investments (CAPEX) unproductivefor the entire
duration of the sleep interval. Furthermore,sleep mode drastically
reduces the overall meshing degree,by limiting the network
reliability, and partially negatesthe possibility of balancing the
load on multiple availablelinks/paths [10]. Finally, results in
[11] show that sleepmode is achievable just for very few nodes and
only at verylow loads. Conversely, in our model, energy-aware
archi-tectures allow the NEs power consumption to scale withtrafc
load, as in [1014]; such architectures are stronglyadvocated by
current efforts from standardization bodiesand governmental
programs [15] and can be made upusing off-the-shelf standard
technologies [16,17].
2. Related work
Greening the network is an active subject of recentresearch.
Several papers have concentrated on the reduc-tion of power
consumption. In [13] Gupta and Singh wereamong the rst researchers
to envision the idea of energyconservation in Internet-based
infrastructures. Shen andTucker [4] developed mixed integer linear
programming(MILP) methods and heuristics to optimize the energy
con-sumption of a IP over WDM transport network. In detail,their
objective was minimizing power consumption ofthe network by
switching off router ports, transponders,and optical ampliers; they
proposed two heuristics (di-rect bypass and multi-hop bypass).
Another approachfocusing on a MILP-based formalization of the
power-aware routing and wavelength assignment has been
alsopresented by Wu et al. [18]. In their work, energy savingscan
be achieved by switching off optical cross connects(OXC) and
optical ampliers according to three differentalgorithms and
criteria. In [19,10], ILP mathematical for-mulations are presented
with the double objective ofreducing both the energy consumption
and the GHG emis-sions of network infrastructure. Since the ILP
solves at theoptimum the ofine RWA problem, these works give
anupper bound for energy and GHG savings. However, usingILP for
real-world networks with dynamic trafc is unprac-tical due to its
intractable computational complexity. En-ergy saving by dynamically
switching off idle IP routerline cards in low-demand hours was also
the approach pre-operating on the network edge. Accordingly, all
the con-nection requests, which share the same trafc ow
charac-teristics and involve signicantly lower capacities thanthose
of the underlying wavelength channels, can be ef-ciently
multiplexed or groomed onto the same wave-length/lightpath via
simultaneous time and spaceswitching. Similarly, different trafc
streams can bedemultiplexed from a single lambda-path.
3.2. Power requirements in network devices
The fundamental cause of energy consumption in elec-tronic
equipment is the effect of loss during the transfer ofelectric
charges, which in turn is caused by imperfectconductors and
electrical isolators. Here, the consumptiondynamism, and hence are
not easily applicable in a fullyadaptive online scenario or use
power containment tech-niques based on switching off of inactive
elements. In ourfully dynamic on-line approach, no switching off is
as-sumed to be feasible (as explained in the previous section)and
so, in this, it is completely different and not directlycomparable,
in term of both performance and effective-ness, with all the
previous ones.
3. Backgrounds
3.1. Wavelength routed networks
A wavelength-routed network, sometime also referredto as an
optical circuit switched (OCS) network, is basicallycomposed of
several OXC devices and opto-electronic edgerouters connected by a
set of ber links. TheWDM technol-ogy is used to carve up the huge
bandwidth available onthe optical bers into lower-capacity
wavelengths (opticalchannels), which may be independently used to
carryinformation across the same physical links. Circuitswitched
connections, usually with high bandwidth andQoS-on-demand, are
typically implemented by dynami-cally creating and tearing
downmulti-hop optical channelsbetween client sub-networks according
to a specic RWAstrategy. The above connections, called lightpaths,
trans-parently traverse the ber network without being con-verted
into an electrical signal. In some cases, they maypass through an
optical/electrical/optical (O/E/O) conver-sion for regeneration,
wavelength conversion or add/droppurposes. At the state of the art,
there is still a large gapbetween the available capacity of an
optical channel andthe much lower bandwidth requirements of a
typical con-nection, but, on the other side, the number of
wavelengthchannels (lambdas) available in most of the networks
ofpractical size is much lower than the number of sourcedestination
connections that need be made. Hence, trafcwhere a linear
programming approach is proposed that isable to identify optimal
topologies for given trafc loadsand generic network topology. In
[23], various power-efcient grooming strategies, combined with
lightpathextension and lightpath dropping, are evaluated in
WDMnetworks where nodes have the tap-or-pass capability.
-
associated with a specic end-to-end circuit or the type
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2423rate depends on the transition frequency and the numberof gates
involved, together with fabrication features (suchas architecture,
degree of parallelism, and operatingvoltage). In pure transparent
optical equipment, the mainenergy-hungry devices are the lasers,
since the optical sig-nal has to reach the other end of the ber
with sufcientquality in spite of the signal attenuation,
dispersionand non-linear optical phenomena. Besides, the
powerconsumption is also conditioned by sophisticated elec-tronic
devices for coping with the technological complexityof the photonic
environment. For example, when the in-volved ber strands need to
cover long distances, severalintermediate electrical signal
re-generators (3R) or opticalampliers (OA) are necessary
(typically, an OA is neededevery 80100 km and a 3R every 5001000
km) to ensurethat the signal power and quality will be sufcient to
reachthe other end of the ber with acceptable optical signal
tonoise ratio (OSNR). Such OA and 3R have a not negligibleenergy
cost that has to be taken into account when settingup the lightpath
requests.
3.3. Energy-aware RWA
Introducing energy-awareness in RWA is based on theconcept of
placing network trafc over a specic set ofpaths (sequences of nodes
and communication links) sothat the overall network power demand
and/or GHG emis-sions are minimized, while end-to-end connection
require-ments are still satised. Typical infrastructures are
denselymeshed, with many redundant interconnections amongnodes, so
that many available paths can provide multiplereachability options
between geographically distant sites.On such a mesh, wavelength
routing is used to set-up alogical topology, which is then used at
the IP layer for rout-ing. Every time a lightpath is established
between any twonodes, the trafc of the lightpath will be handled as
asingle IP hop by creating the abstraction of a virtual net-work
topology on top of the physical one. This overlayapproach is based
on the full separation of the routingfunctions at each layer, i.e.
the connection routing/groom-ing at the IP layer is independent
from the routing of wave-lengths at the optical layer. One of the
key features of theabove model is rearrangeability, i.e. the
ability to dynami-cally optimize the network as a consequence of
the inde-pendence between the virtual and the physical topology.The
above architectural exibility in building logical topol-ogies,
together with physical connection redundancy andover-provisioning,
provide fertile grounds for saving en-ergy, since a large number of
available trafc routing anddevice management options can be
exploited to optimizeenergy and carbon footprints network wide.
Hence, en-ergy-aware logical network topologies, explicitly
con-ceived to decrease power consumption in the operationalphase,
can be dynamically built by minimizing the numberof energy-hungry
devices traversed by the existing light-paths. In doing this, it is
desirable to nd a good balancebetween the competing needs to avoid
as many electricallypowered hops as possible (to reduce the power
consump-tion at intermediate switching nodes, optical ampliersand
regenerators) and avoid data transmission overexcessively long
stretches, since moving data is quiteof energy source currently
used by a network element[6]. Analogously, the same information has
to be handledby control plane signaling protocols used for the
reserva-tion and establishment of paths minimizing the use ofdirty
power sources, as well as the overall energyconsumption, across the
network.
4. The energy-aware network model
Dening a sustainable and effective network model tak-ing into
account power consumption as well as energysource considerations is
the essential prerequisite forintroducing energy-awareness within
the wavelengthrouting context. A broad variety of NEs contribute to
poweradsorption in a network: regenerators, ampliers,
opto-electronic and totally optical routers and switches. Eachof
these devices draws power in a specic way, which de-pends on their
internal components and structures, on thetrafc load and on the
relationship between the devices. Inaddition, some nodes may be
powered by green energysources, while others may use traditional
dirty energyplants; therefore, a differentiation between energy
sourcesis required. NEs powered by green energy sources will
notcontribute to the CO2 emissions but only to increase theoverall
network energy consumption. In the dynamicRWA problem, the routing
of connection requests is doneon a local optimality basis, i.e.
considering the currentinformation available at the connection
setup time. Thepotential of such an approach should not be
underesti-mated; the conditions that determined such optimalitymay
change, but the RWA strategies keeps its effectivenessas far as it
is able to foresee the future network evolutions,both in terms of
resources and energy utilization.energy-expensive. Energy
consumption can be drasticallyreduced by maximizing the reuse of
low-power transmis-sion links and highly connected devices,
especially whenpowered by green sources, instead of obliviously
spreadingthe trafc on the available routing/switching devices
andcommunication resources. In other words, since a logicalnetwork
topology is described by its constituent light-paths, a logical
topology that minimizes the overall energyrequirement and the
associated carbon footprint is one inwhich the choice of each
individual lightpath, whilesatisfying the traditional RWA
objectives and constraints,is driven by the above energy-efciency
optimizationcriteria.
In order to support all the above behaviors, energy-related
information associated with devices, interfacesand links need to be
introduced as additional constraints(together with delay,
bandwidth, physical impairments,etc.) in the formulations of
dynamic RWA algorithms. Suchinformation must also be handled as new
status features ineach network element that have to be considered
in all therouting and trafc engineering decisions, and conveyed
toall the various network devices within the same energy-management
domain. This clearly requires modicationsto the current routing
protocols by properly extendingthem to include energy-related
information in their infor-mation exchange messages, such as the
power demand
-
The above energy-related information and conceptsassociated with
devices and links must be abstracted anddened in a formal and
concise way into a comprehensivemodel that needs not to delve into
unneeded details, butshould only describe the essential aspects
needed to drivein an energy-conscious way the RWA algorithms
andstrategies developed upon it. Therefore, we modeled thenetwork
from a high-level perspective in an attempt tokeep the reference
scenario as general as possible focusingon the effectiveness and
energy-efciency of our ap-proach; the issues raised by modulation
techniques,spectrum-sliced elastic networks, and other
technologicalbreakthroughs, although interesting, fall outside the
scopeof this paper, which is to provide an energy-awaredynamic RWA
schema to route as many connections aspossible.
In detail, at the basis of our model we consider a mult-igraph G
= (V,E) representing the network (Fig. 1), where Vis the set of
nodes and E the set of edges, jVj = n, jEj =m. Nospecic assumption
is made on the number of wavelengthsper ber link and on the number
of bers on each link: anytwo nodes u, v 2 V may be connected by
several edges(thus, multigraph). Each ber link (u,v) 2 E is
characterizedby its physical length lu,v, together with the number
ofavailable wavelengths wu,v. There can be more than one -
available on the physical circuit. Each tagged link (u,v)k,is
characterized by its static global capacity au;vk and dy-namic
residual capacity ru;vk . Clearly, for each link (u,v)k,its current
load is given by au;vk ru;vk . Provided that asingle established
lightpath or a chain of lightpaths be-tween the source and
destination nodes has sufcientavailable capacity, each connection
request can be routedonto that lightpath or chain. Also, a new
lightpath maybe dynamically established, as the result of
groomingdecisions.
The nodes of the graph model the routing and switchingdevices
deployed in the network. We consider two types ofnodes: LERs
(Lambda Edge Routers) and LSRs (LambdaSwitching Routers). LER nodes
have both the electronicand optical interfaces, and have the
capability to insert/extract traditional electronic trafc into/from
the network.LSR nodes are OXC or recongurable optical add and
dropmultiplexers (ROADMs) that are capable of switching thetrafc at
wavelength level (since we model optical circuitswitched networks)
and may be equipped or not withwavelength converters. Whenever an
optical signal is con-verted into the electronic domain, it is
implicitly assumedthat it is possible to apply 3R regeneration as
well as wave-length conversion and add/drop at sub-wavelength
granu-larity (grooming). Consequently, network trafc may be of
presen
2424 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442ber connecting the same pair of nodes and, for
simplicitysake, we assume that all the bers are of the same
type(e.g. NZ-DSF ITU-T G.655/656), requiring an
intermediateamplication or regeneration stage every K units of
dis-tance. Typically K can assume the values KOA = 80 km fornative
optical amplication systems and K3R = 5001000 km for 3R electric
regeneration devices. On each berlink (u,v) there can be multiple
wavelength links (u,v)k,modeled on the graph G as an additional
virtual taggedlinks, where the tag k can be any of the
wavelengths
Fig. 1. A network topology with two wavelengths per link (a) and
its rerequest from node 1 to node 3) (c).two types: electronic time
division-multiplexed (TDM)trafc (i.e. trafc that undergoes
electronic processing)and pure optical trafc (i.e. WDM trafc
entirely managedin the optical domain) with or without optical
wavelengthconversion. Electronic routers have the ability to
add/droptrafc into/from the network, to make electronic
WC(Wavelength Conversion) and to regenerate the signal inthe
electronic domain (3R regeneration). Optical routerssupport optical
trafc with or without all-optical WC. Thatis, they may deect
wavelengths through an electronic 3R
tations as multigraph (b) and as layered graph (assuming a
connection
-
regenerator if the OSNR is too degraded and then switch
wanted uctuation in the green-biased node selection
WC and the latter consumes more power than optical
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2425function due to the temporary unavailability of the
specicsource (e.g. sun, wind or tide), since the hosting sites
areusually equipped with battery systems, ensuring the
avail-ability of the accumulated green energy also during thesource
off-times (e.g. the night hours for solar panels).Anyway, the
devices powered by green energy should bealways preferred also for
cost containment reasons, sincethe energy costs in the hosting
sites/installations are regu-lated by signicantly advantageous
contractual conditions.In fact most of the electricity providers
and supplying util-ities apply some balancing policies for sites
producing theirown energy from renewable source and placing the
energyproduced in excess in the public electric grid (by reduc-ing
the carbon footprint on a more global scale), so that,even in the
case in which the required energy would becurrently extracted from
a dirty source, its cost will be sig-nicantly lower when compared
with other dirty-onlypowered sites.
4.1. Per-node power requirements
In order to characterize in a realistic and quantiableway the
energy requirements of a specic network path(needed to accomplish
our optimization goals within theRWA context), we need to estimate
the power consump-tion of all the traversed NE (devices on the
nodes andtransmission links) as a function of the involved
trafctype. In doing this, we essentially consider two main
trafctypes:
1. Electronic trafc, comprising add/drop, electronic WC,3R
regeneration.
2. Optical trafc, with or without optical WC.
The above trafc types are characterized by a
considerablydifferent power consumption when traversing a NE:
elec-tronic trafc requires more power than optical trafc withthe
wavelength through the corresponding output port[2]. In our network
model, connections are bidirectionaland unsplittable, i.e. a trafc
demand is routed over a sin-gle lightpath, and LER nodes can be
source or destination ofa connection.
As for the energy, we derived a properly crafted per-node,
per-link and per-lightpath energy model and powercost function,
basing our estimation on the literature[13,14,15] and on the
manufacturers technical sheets[24,25], with the aim of tting with
the future energy-aware technologies that will adapt their
power-consump-tion with their load [1014].
We distinguish between green and dirty energy sources,i.e.
carbon-emitting and zero-carbon plants. Each noden 2 V has a
statically associated attribute sn representingthe type of energy
source (green or dirty) powering thecorresponding device, and we
assume that green and dirtyenergy sources are heterogeneously
distributed in thenetwork. This attribute has been kept static to
avoid un-trafc without WC, due to the different devices
involved[1,2].
Therefore, the power consumption of a specic light-path depends
on:
1. The type of devices traversed along its route fromsource to
destination node, e.g. router, switch, signalamplier/regenerator,
etc.
2. The device class, in terms of hardware architectureand
aggregated switching performance of the networkelement itself. More
precisely, modular switching nodescapable to handle higher
throughputs consume lessenergy per bit that smaller ones [26,27]
since they aremore optimized and tend to be located in the centerof
the network where the trafc is more aggregated,and opaque nodes
equipped with electronic switch-ing matrices are more energy-hungry
that their trans-parent photonic counterparts.
3. The type of trafc that it transports through each net-work
element, i.e. electronic, optical with WC and opti-cal without
WC.
The power consumption of real electronic and opticalswitching
nodes with and without WC are reported in[1,2], where it can be
observed that the electronic trafcgrows quickly with respect to the
optical one and that,within the optical trafc context, the WC is
the main factorinternal to the switching device requiring a not
negligiblequantity of energy. In [14] it is shown that the base
systemof an idle network device consumes approximately half ofthe
total power drained by the device, while the other halfis consumed
when the router is in its maximum congura-tion, i.e. maximum number
of line cards/modules installedand operating at their full load.
These power consumptionsrefer to commercially available devices
whose architec-tures are not energy-aware: their power
consumptionsonly slightly depend (23%) on the current trafc
load,but strongly depend on the number of line cards
installed[14,28]. Next-generation energy-aware
routing/switchingnodes, designed with energy-efciency in mind and
allow-ing dynamical adjustment of their power consumptionwith the
variation of the trafc load by selectively puttinginto sleep or
low-power mode some interfaces, line cards,and subsystems, will be
characterized by a signicantlydominating load-dependent energy
consumption compo-nent. However, by estimating the power demands
usedin our model from the available quantitative data gatheredfrom
the current devices implicitly forces the model tooperate in a
worst-case situation making the achievedresults more comforting
(since they will be greatlyimproved with the introduction of next
generationenergy-aware devices). Therefore, even if actual
routerarchitectures are not energy-aware, in the sense that
theyconsume the same amount of power regardless of the traf-c load,
here we consider future energy-aware architec-tures that scale
their power consumption with theircurrent trafc load, thus giving
rise to optimization[1,10,27].
-
Consequently to [14,28], we assume that the powerconsumption of
a network element, modeled by a nodeor link, can be divided into
two equal parts: xed andvariable power absorption. The xed power
consump-tion is always present and is needed just for the deviceto
stay on, while the variable power consumption de-pends on the
actual trafc load that the device is currentlysupporting. The case
in which the xed power consump-
trafc as the most power hungry devices. To this end, we
carefully balance the power consumption of all the possi-
In more formal terms, we dene for all the routing and
2/n lneBn
Bn x xBn ; 2
is the equation of the logarithmic function passing throughthe
points (0,/) and (Bn,2/), modeling the best per-bit en-ergy
consumption (i.e. optical trafc w/o WC in Fig. 3a)and:
and load (in Gbps) for different types of nodes (linear
case).
of the Power consumption (y) as function of the load (x)assuming
half xed (/) and half variable (m x)y = 1.5x + /y = 0.031x + /y =
0.01x + /
2426 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442tion is signicantly greater or lower than the variable
partwould affect more or less proportionally the optimizationgains.
In particular, for the current energy-unaware de-vices, the xed
part is much greater than the variable part(which only represents
23% of the total power consump-tion), leaving almost no space for
optimization. In theopposite situation, if the xed power
consumption wasmuch lower than the variable part, the likely
outcomewould be that the optimization margins will increase alot;
in this sense, our approach of assuming 50% for xedand 50% for
variable power consumption can be consideredas conservative.
The previous considerations can be used to build a suf-ciently
general per-node power consumption model.Starting from the power
consumptions of the networkrouting devices (in Watts) as function
of their aggregatedbandwidth (in Gbps) [1,2], we obtained the
linear powerconsumption equations [10] reported in Table 1, whichwe
used to calculate the real maximum power consump-tion of any kind
of network node given its aggregatedbandwidth. In such a linear
model, a slope ofmmeans thatfor each unit of trafc (Gbps) the
router consumes m unitsof power (W). For example, an electronic
router with anaggregated bandwidth of 10 Tbps is characterized by
amaximum power absorption of 30 kW. An optical switchwith the same
aggregated bandwidth consumes 0.62 kWwith WC and 0.2 kW without WC,
which totally agree withthe values reported in [2,14].
Starting from such maximum power consumption val-ues, we obtain
the curves in Fig. 2, in which the minimumpower consumption
associated with the network device nin the idle state is given only
by the xed power consump-tion /n of its base. The maximum power
consumption 2/nis achieved when the node is fully loaded, i.e. when
thecurrent load x is equal to the maximum aggregated band-width Bn
of the node n. How the power consumption scalesbetween these two
values has been studied carefully in[10]. In this work we observed
that the power consump-tion associated with electronic trafc is
higher than theone associated with optical trafc (Fig. 3a optical
nodepower consumption not in scale; see the peak power con-sumption
of optical nodes in Fig. 3b for in-scale values).Furthermore, we
also observed that the power consump-tion of smaller nodes follows
a worse trend with respect
Table 1Power consumption (in Watts) dependency laws on
aggregated bandwidth
Node type Power consumption (y) as functionaggregated bandwidth
(x)
Electronic y = 3xOptical w/ WC y = 0.062xOptical w/o WC y =
0.02xswitching nodes, a power consumption function Wn(x)expressing
the power requirements of a node n character-ized by device-specic
static consumption /n and perfor-mance class (aggregated bandwidth)
Bn, variablyconditioned by a traversing trafc load x. The
functionWn(x) can be viewed as a linear combination of the
loga-rithmic function hn(x) and the line function #n(x) weightedby
the parameter an(x):
Wnx anx hnx 1 anx #nx 1
where
hnx /n lne/n
Bn Bn x xBn
|{z}
variable power consumption
/n|{z}fixed powerconsumption
/n ble combinations between these two extremes.studied different
power consumption functions, both pres-ent in literature and
analytically conceived, to describe andto bigger ones, in which the
per bit energy consumptionis lower (Fig. 3b). Therefore, we can
delineate two ex-tremes: big nodes transporting optical trafc as
the leastpower consumers, and small nodes transporting
electronicFig. 2. Minimum and maximum power consumption of a
network node.
-
of real routers from Table 1, taken in such a way to
Fig. 3. Power consumption functions of current, ideal and
state-of-the-art for electronic and optical nodes (a) and for
different sizes of nodes (b).
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2427#nx /nBn x|{z}variable powerconsumption
/n|{z}fixed powerconsumption
;
3
is the equation of the line function passing through thepoints
(0,/) and (Bn,2/), modeling the worst per-bitenergy consumption
(i.e. electronic trafc in Fig. 3a).
Bn is the capacity (aggregated bandwidth) of the routern (its
performance class), and an(x) is the weighting param-eter between
hn(x) and #n(x) depending on the class/perfor-mance of the router n
and on the parameter b(x) whichaccounts for the specic type of
trafc associated withthe load x that is actually passing through
the node n:
Bnanx maxfBn;8n 2 Vg bx; 0 6 a 6 1; 4
Table 2Notation used in the energy model.
Parameter Energy model
/n Fixed power consumption of node nWn(x) Overall power
consumption (xed + variable) of node n withhn(x) Logarithmic
function#n(x) Line functionan(x) Linear combination weighting
functione Eulers number (base of the natural logarithms)Bn
Performance class of node n (aggregated bandwidth of all intb(x)
Weighting function on the type of trafcm Slope of the power
consumption functions (linear case)wu,v Number of
wavelengths/channels crossing ber (u,v)Wu;vk x Power consumption of
the link (u,v) on the wavelength k wilu,v Length of the ber (u,v)
(km)guu;vk x Power consumption of the interface on the node u
associatedQu,v Power consumption associated with the individual
amplicaKOA Maximum allowed length of a link without need of optical
aK3R Maximum allowed length of a link without need of 3R regenR(x)
Power consumption associated with the individual 3R regeneWp(x)
Power consumption of lightpath p with trafc load xlp Cumulative
length of path p (km)penalize the more power consuming devices and
trafctypes; e.g. for the electronic trafc, bx moptic w=o
wcmelectronic 0:011:5 0:006.
2. an(x) weights one Eq. (2) or the other Eq. (3)
functionaccording to the device class and trafc characteristicsbx 1
if the traffic x is optical w=o WC0:323 if the traffic x is optical
w=WC0:006 if the traffic x is electronic
8>:
0 6 b 2 f1;0:323;0:006g 6 1: 5
Note that:
1. The values of b(x) have been obtained using the valuesof the
involved NE.
trafc load x
erfaces)
th trafc load x
with the wavelength k on the ber (u,v) supporting the trafc load
xtion device on link (u,v)mplication (km)eration (km)ration of the
trafc load x (one for each wavelength)
-
bandwidth capacity) between two network nodes. In such
2428 S. Ricciardi et al. / Computer Networks 56 (2012)
242024423. The xed power consumptions /n of nodes are obtainedfrom
[1,2,14].
For an explanation of the symbols used in the notationrefer to
Table 2.
4.2. Per-link power requirements
End-to-end transmission links are characterized by apower
consumption depending not only on the specic de-mand associated
with the hardware interfaces located inboth the endpoints, but also
on the impact introduced bythe optical amplication and regeneration
devices neededby the signal to reach the endpoints with an
acceptablequality, and thus, on the length of the traversed
berstrands.
Accordingly, the power absorption of a transmissionlink realized
on the wavelength k between the nodes uand v can be entirely
described by the specic power de-mand characterizing the involved
end-to-end interfacesplus the power required for powering all the
possible inter-mediate regenerators or optical ampliers, if any. If
theber between u and v is currently crossed by wu,v wave-lengths,
we consider that the power requirements due tothe intermediate
devices will be shared between the wu,vchannels simultaneously;
typically, as for the OA, the en-tire frequency band (e.g. C-band)
is amplied as a whole,without per wavelength granularity, whilst as
for 3Rregeneration, per wavelength 3R is required. Thus thepower
consumption Wu;vk x, associated with a link onthe wavelength k with
load x traversing the ber (u,v),whose length is lu,v, can be
described as:
Wu;vk x guu;vk x gvu;vk x
lu;vKOA
Qu;vwu;v
lu;vK3R
Rx;
6where
1. guu;vk x is the power consumption of the interface onthe node
u associated with the wavelength k on theber (u,v) when operating
at the minimum speedallowing to support a trafc load x without any
loss ordelay increase. Here we do not take into account thevariable
effect on power consumption of the dynamictrafc traversing the
interface, whose impact is alreadyconsidered in the per-node
consumption, and onlymodel the specic per-interface power demand
accord-ing to its specic static hardware features (type of
laser,its power, etc.) and to a multiple threshold scale
charac-terizing its consumption depending on current operat-ing
speed (implicitly dependent from the load x). Theabove guu;vk x
function can be modeled as in [27].
2. Qu,v is the power consumption associated with the indi-vidual
amplication device on link (u,v) (between 3 and15W) operating
throughout the ber link (one for theentire frequency band); for
simplicity, we assume thatall the amplication devices operating on
the same linkhave the same power consumption.
3. R(x), dened in the sameway as #n(x) (i.e. electronic traf-c
in a node), is the power consumption associated withthe individual
3R regeneration device of the trafc load xa dynamic scenario,
connection requests have to be servedas soon as possible when they
arrive; thus, we designedGreenSpark with simplicity in mind, which
was consideredas a necessary requisite when developing the
dynamicRWA scheme to keep as low as possible the
computationalcomplexity. According to the typical assumptions in
OCSnetworks, each connection is considered to be bidirectionaland
consists of a specic set of trafc ows that cannot besplit between
multiple paths. A connection can be routedon one or more (possibly
chained) existing lightpaths be-tween the source and the
destination nodes with sufcientavailable capacity or on a new
lightpath dynamically builton the network upon the existing optical
links. Connectionrouting and grooming decisions are taken
instantaneouslyreecting an highly adaptive strategy that
dynamicallytries to fulll the network resource utilization and
connec-tion serviceability objectives together with minimizing
theoverall power consumption by privileging cheaper (interms of
power demands) chains of nodes/links and, be-tween them, trying to
maximize the usage of devices pow-ered by green energy sources.
Without loss of generality, we route connection re-quests with
only a constraint on the required bandwidth,and rely on the
incorporation of other policies within thebandwidth-routing
framework to perform routing basedon several QoS and impairment
metrics such as limited la-tency, error rate, hop-count, delay, and
losses. Such con-(one for each wavelength), when present; for
simplicity,we assume that all the regenerationdevices operating
onthe same link have the same power consumption.
4.3. Per-lightpath power requirements
Given a lightpath p as a sequence of nodes and taggedlinks
(u,v)k with a trafc demand x, the power consump-tionWp(x) of p is
given by the sum of the individual powerabsorption of all the
traversed nodes and links plus the re-quired regenerations:
Wpx Pn2p
Wnx P
u;vk2pWu;vk x
lpK3R
Rx; 7
where lp is the cumulative path length. The third compo-nent in
the Eq. (7) sum is needed to cope with fully trans-parent
lightpaths whose length exceeds the maximumlength K3R that a signal
can travel without need of 3Rregeneration. In this case, since all
the intermediate de-vices do not convert the signal back and forth
into electri-cal/optical form and only introduce impairments, a
3Rregeneration stage is required in correspondence with at
least lpK3R
j kintermediate nodes. If the lightpath p is fully
transparent (without wavelength conversion), the tag k isthe
same on all the wavelength links.
5. The two-stage RWA scheme
The proposed energy-aware RWA scheme operates on-line, running
at each request of a dedicated connectionwith specic service
requirements (typically QoS on the
-
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2429straints can be incorporated into SLAs by converting
theserequirements into a bandwidth requirement as shown in[29].
Impairments are accounted for by modeling 3R regen-eration into the
framework, supported by the study in [30]in which
impairment-awareness is included into theregeneration placement for
WDM networks, and optoelec-tronic signal regeneration is employed
to address the sig-nal quality of lightpaths that are found to be
impairedwithout compromising the signal quality of any of
thelightpaths.
The apparently conicting goals of minimizing cost andlength of
designed paths while keeping the network re-source usage fairly
balanced, and optimizing the overallpower consumption by reusing,
as possible, energy-efcient paths across the network, give origin
to a multi-variate and multi-objective optimization problem thatcan
be solved according to a divide-et-impera strategy, setup of
two-stages in which each stage separately handlesa specic objective
by using properly crafted heuristics.Specically, in the rst stage
(pre-selection phase), the goalis to determine an ordered list
(whose length is dened bya parametric value k) of feasible minimum
cost paths thatfully satisfy the connection demands, trying to
leave oneach link of these paths sufcient room to satisfy
furtherrequests as much as possible. Such strategy clearly
impliesbalancing the load on all the available network resources.In
the second stage (energy-aware decision phase), theproposed scheme
analyzes, for each path found in the stageone, its power
requirement as given by the aforementionedenergy model, and then
selects the best available solutionaccording to several heuristic
criteria based on nding agood compromise between the traditional
carriers objec-tives and the new green requirements (i.e. limiting
powerconsumption or using green energy sources to reduceGHG
emissions).
Towards this goal, we explicitly dened and studied twodifferent
green optimization objectives: the rst one, aim-ing at reducing the
power consumption throughout thenetwork, and hence its operating
expenditures; the secondone, oriented to minimize the network
carbon footprint onthe environment by minimizing the GHG
emissions.
5.1. Prerequisite control plane facilities
The proposed scheme also requires several forms ofcooperation
between the nodes concurring to the RWAproblem solution. This
implies that every node needs torun distributed control-plane
services (such as those pro-vided by the GMPLS framework) keeping
up-to-date infor-mation about the complete network topology,
resourceusage and power demand attributes, as well as taking careof
resource reservation, allocation, and release.
More precisely, a periodic link-state advertisement(LSA)
schememust convey all the link and node state infor-mation
(including energy related ones) to every node inthe network,
ensuring the complete synchronization be-tween all the nodes
network status views. Since theamount of per-link state information
is very small, anyappropriate enhanced link state scheme like those
em-ployed by OSPF can be adequate for this purpose, like theone
developed in [6].The Dijkstra-based path selection scheme of stage
oneshould meet certain conditions:
1. A link may not reserve more trafc than it has
capacityfor.
2. Shorter paths should be preferred when they consumefewer
network and energy resources.
3. Critical resources, e.g. residual bandwidth in
bottlenecklinks, should be preserved for future demands.
The last two conditions reect that what we really seek isto keep
the connection blocking probability (or, in otherwords, the
rejection ratio) as low as possible, or equiva-lently to increase
as much as possible the networkutilization.
In addition, an extended signaling/reservation protocol,such as
RSVP-TE, can be used to setup and release pathsand lightpaths and
handle all the bandwidth, ber orwavelength resources reservation
and allocation/dealloca-tion operations required during such
activities. In detail, asa new request arrives, the control plane
on each node,starting from the originating one, runs our
source-basedlocalized RWA algorithm, calculates the new overlay
net-work topology and triggers the proper path setup actionsby
sending a reservation request toward the destinationand
provisionally reserving bandwidth resources. TheRWA scheme,
operating according to a two-layer model(i.e. an underlying pure
optical wavelength routed networkcore and an opto-electronic time
division multiplexedlayer built over it) should determine if the
request can berouted on one of the already available lightpaths,
bytime-division multiplexing it together with other
alreadyestablished connections, or a new lightpath is needed onthe
optical transport core to join the terminating (edge)nodes. In
presence of multiple options between new feasi-ble and already
established lightpaths, the link weightingand path selection
functions of the two stages, applied onthe existing lightpaths and
to the wavelength links thatcan be used to set up new lightpaths,
together with the en-ergy costs, dynamically determine the best
compromise(between network and energy costs) routing solution
forthe request, starting from the current network status.
Forexample, if two lightpaths between source and destinationexist,
both with sufcient available capacity, if the differ-ence in
network cost between them falls below a specicacceptability
threshold, the tie is resolved in favor of thegreener lightpath.
Such policy guarantees maximum light-path utilization and
automatically achieves, as long as pos-sible, effective dynamic
grooming and power usage,assuming that the topology (link state)
database is prop-erly updated.
The signaling scheme for triggering the new lightpathset-up and
reserving the required bandwidth, ber orwavelength resources along
the path is very similar tothe RSVP-TE protocol used by GMPLS. To
make a reserva-tion request, the source node needs the path and the
band-width that it is trying to reserve. The request is sent by
thesource along with path information. At every hop, the
nodedetermines if adequate bandwidth is available in the on-ward
link. If the available bandwidth is inadequate, thenode rejects the
requests and sends a response back to
-
ler (low global capacity au;vk ) and more congested (low
2430 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442residual capacity ru;vk ) links. Note that every two
linkswith the same residual/maximum capacity ratio but differ-ent
residual or maximum capacity values will have differ-ent associated
weights. This avoids assigning the sameweight to two links with the
same saturation ratio but withdifferent residual or global
capacity. Therefore, we choosethe weighting function (8), which
satises all the desiredproperties discussed before. Note also that
the rst stageis exclusively based on load-balancing criteria, and
no en-ergy consideration is present at all; this guarantees thatthe
k paths selected in this stage are the best balancedones, thus
giving priority to the traditional network opti-mization criteria
of minimizing the connections blockingthe source. If the bandwidth
is available, it is provisionallyreserved, and the request packet
is forwarded onto thenext hop in the path. If the request packet
successfullyreaches the destination, the destination acknowledges
itby sending a reservation packet back along the same path.As each
node in the path sees the reservation packet, itconrms the
provisional reservation of bandwidth. In addi-tion, it also
performs the required conguration needed tosupport the incoming
trafc such as setting up labels in aGMPLS label switching node, or
reconguring the lambdaswitching internal devices (such as MEMS) in
a transparentoptical wavelength switching system.
5.2. The rst stage: selecting the candidate paths
The rst stage of the GreenSpark RWA schema com-putes a list of k
feasible cycle-free paths, in increasing or-der of cost, between
the source and the destinationnodes of the connection to be routed,
constrained by itsQoS requirements. Here k is a congurable
parameter thatcan be used to limit the number of feasible paths
thatshould be considered in the following step, thus control-ling
the depth and granularity of the analysis processaccording to a
performance/precision compromise. TheK-SPF (k-shortest paths rst)
algorithm used has beenexplicitly modied to meet the specied
bandwidthrequirements of each new request and to enforce
thewavelength continuity constraint so that, when
traversingconverter nodes, we are totally free in selecting any
outgo-ing link of the multigraph (i.e. any wavelength), whereaswith
all the other nodes we can only select an outgoinglink
corresponding to the same wavelength associatedwith the incoming
one. The above pre-selection processis driven by a link weighting
function x((u,v)k) taking intoaccount, for each link (u,v)k, the
(static) global capacityau;vk and the current (dynamic) residual
capacity ru;vk stillavailable on the link. Intuitively, a good
weighting functionshould be inversely proportional to both the
residual andthe maximum capacities, but the contribution of
thesetwo factors need not be the same. Following the analysisin
[8], the link weighting function is dened as:
x : E ! R;xu;vk ru;vk logau;vk1 8Such a function exhibits the
desirable property of lead-
ing to a good load-balancing over the network, since it triesto
avoid bottleneck link by assigning higher costs to smal-ratio.
Energy-awareness is introduced only in the secondstage, where the
greenest path among the k best-balancedcandidate paths is nally
selected.
5.3. Second stage: choosing the best path
The k minimum cost paths found by the K-SPF algo-rithm in the
rst stage are the k best paths as for networksblocking probability
(the percentage of rejected connectionrequests), since the
weighting function x((u,v)k) tends tobalance as much as possible
the use of the network re-sources. Among these k best-balanced
paths, we now haveto choose the optimal path among them according
to ourenergy-aware selection criteria, aiming at minimizing
thepower consumption or the carbon footprint. For this pur-pose we
need to introduce a properly crafted heuristicworking as a path
scoring function, to differentiate amongthe available preselected
paths and choose the most en-ergy-efcient one. The scoring function
fS is dened onthe set of all the possible paths P and will evaluate
thepower consumption and carbon footprint of the k pathsK = {pi, i
= 1,2, . . . ,k} obtained from the rst step:
fSp : P! R: 9The (total) power consumption Wp(x) of a path p
de-
ned in eq. (7) can be decomposed as the sum of the
powerconsumption of the traversed devices that are powered bygreen
WGpx and dirty WDpx energy sources:
Wpx WGpx WDpx: 10Note that the carbon footprint of a lightpath
is only gi-
ven by the power consumption of the involved NEs that arepowered
by dirty energy sources, as the NEs powered bygreen energy sources
do not contribute to GHG emissions.
Therefore, if our primary objective is to minimize theGHG
emissions (GreenSpark MinGas), we have to choosethe path p which
has the lowest carbon footprint WDpx(primary objective) and, among
paths with the same min-imum carbon footprint (if any), we choose
the path thatminimizes the total power consumption Wp(x)
(secondaryobjective):
fSp WDpx logWpx: 11Analogously, if our main goal is reducing the
overall
power consumption and, thus, the network operating en-ergy costs
(GreenSpark MinPower), we need to use anobjective function
privileging the paths with minimal totalpower consumptionWp(x) and,
among them, choosing theone with the minimum carbon footprint
WDpx:
fSp Wpx logWDpx: 12The computation of the scoring function is
done for
each of the kminimum cost paths, and the path p eventu-ally
chosen is the one with the lowest fS(p) value:
p argminffSpjp 2 Kg: 13If more than one such lightpaths exist
(i.e. with the
lowest fS (p) value), the one with the minimum i indexin the set
of lightpaths K is selected (to maximizeload-balancing).
-
The path p is the best path between the best load-balanced paths
that minimizes the carbon footprint oroverall power consumption
according to the proposed en-ergy model, and it will be used to
route the connectionrequest.
Note that the rst stage cost function is dened over theset of
edges (8), whereas the energy-aware scoring func-tion is dened over
paths (9) to reect our intent of achiev-ing an acceptable
compromise between the traditionalnetwork optimization objectives,
typically based only onspecic link properties, and the
energy-related ones thatneed to take into account more complex
considerationsto be done on the higher layer concepts of
lightpath/chan-nel, interface and node role and wavelength
processingpractice such as optical amplication and 3R
regeneration.That is why we structure the decision process into
twoindependent phases and select among the k candidatepaths the one
with the minimum carbon footprint orpower consumption according
respectively to the func-tions (11) and (12), instead of simply
selecting the mini-mum cost path based only on the traditional
costfunction (8).
The generic GreenSpark algorithm is sketched in Fig. 4.
6. Time and space complexity analysis
In the rst stage, the computing of the K-SPF for ndingthe k
feasible paths for a specied sourcedestinationpair requires in the
worst case a time complexity ofO(k (m + n logn)) [31]. The second
stage computes theobjective function fS for each of the k paths
found. The func-tion calculation requires the computation of the
power con-sumption or GHG emissions for each network element inthe
path. The maximum length of a cycle-free path in agraph with n
nodes is n 1, thus the second stage requiresO(k n). Hence, since
the K-SPF complexity is thedominating factor between the two
stages, the worst caseruntime is given by the polynomial time
complexityO(k (m + n logn)). Therefore, GreenSpark belongs to
thesame polynomial complexity class of the fastest SPF im-proved by
using a priority queue with a Fibonacci heap inthe implementation,
O(m + n logn) [32]. GreenSpark com-plexity is also lower than the
quadratic complexity of theoriginal SPF algorithm, O(n2), and
signicantly lower thanthe cubic complexity of nave MIRA O(n3m log
(n2/m))optimized with the Goldberg max-ow algorithm [33].
As for space complexity, our multigraph network repre-
enSpa
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2431The algorithm takes as input the current network state G,the
connection request q = (s,d,b) between node s and dwith QoS
bandwidth requirement b, the k parameter ofthe K-SPF and the
objective function fS. In the rst stage(lines 12), the K-SPF
algorithm nds the k minimum costpaths with sufcient free bandwidth
connecting the sourceand destination nodes. The k paths are the
best onesaccording to the load-balancing cost function x((u,v)k)
ofEq. (8). In the second stage (lines 37), the chosen
objectivefunction fS is evaluated for the k minimum cost paths
andthe best path p is eventually chosen to route the connec-tion
request q. Finally, the new x((u,v)k) costs are updatedonly for the
edges of the path p, and the chosen path andnew network state are
returned.
Fig. 4. The Gresentation requires less space with respect to the
layeredgraph approach conventionally used in dynamic RWA
algo-rithms (Fig. 1). Using up to kwavelengths on each edge,
thelayered representation with C converter nodes will requirekn + 2
nodes (k layers, each dedicated to an individualwavelength, plus
two additional nodes to serve as ingressand egress) and km + 2k + C
(k 1) edges (converters canbe modeled by cross-layer edges that
connect each layerto the k adjacent layer a wavelength conversion
spanningmultiple frequencies will thus entail many such edges
insequence), whilst the equivalent multigraph representa-tion will
require only n nodes and km edges, thus notablyreducing the space
complexity. Besides, in the layeredgraph, the ingress and egress
nodes as well as the edges
rk algorithm.
-
connecting them to the network have to be built each timea new
connection arrives, whilst in the multigraph ap-proach this
preprocessing phase is not necessary thanksto its compact
representation. Note that, even in absenceof wavelength conversion,
all the layers of the layeredgraph have to be explored, since the
(rst) wavelength ofthe lightpath may be any, which compensates the
addi-tional check needed in the multigraph approach to enforcethe
wavelength continuity constraint. Furthermore, thehigher number of
nodes and edges required by the layeredgraph with respect to the
multigraph approach increasesthe time complexity which strictly
depends on the n andm parameters.
The low computational and space complexity requiredby the
GreenSpark algorithm with the multigraph networkrepresentation
helps lightening the computational burdenof path computing elements
and serving the connectionswith lower delay with respect to more
complexapproaches.
7. Performance evaluation and results analysis
In order to evaluate the effectiveness of the
GreenSparkenergy-aware RWA framework and its impact on thepower
consumption and carbon footprint of telecommuni-cation networks, we
conducted an extensive simulation
study on the network topology modeled as undirectedgraphs in
which each link has a non-negative capacityand a specic power
demand depending on both its phys-ical and technological features.
All the nodes in the graphare characterized, apart from the
traditional network-levelcapabilities such as wavelength conversion
and add-and-drop capability, by their power absorption and type of
en-ergy source (i.e. green or dirty), as dened in the energymodel
of Section 4.
To improve the signicance of the obtained results andmake them
more easily comparable with the other experi-ences available in
literature, we spent a signicant efforton the use of realistic data
in all our experiments (networktopology, trafc demands, costs, and
power consumptionmodels). Accordingly, we used in our simulations
thewell-known network topology Geant2 [34] of Fig. 5 withthe
bandwidths for the links ranging from OC-1 to OC-768 bandwidth
units. Here, trafc demands have beenmodeled by using different
randomly generated or staticpredened [35,36] trafc matrices. In the
latter case, thetrafc volumes have been scaled proportionally to
the re-ported trafc distributions. The energy model has beenfed
with the realistic power consumption values associatedwith nodes
and links taken from [2,10,37]. Recall that, sinceno per-node sleep
mode is assumed to be possible, thenetwork elements are always
powered on and thereforethe GreenSpark algorithm bases its
decisions exclusively
rk top
2432 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442Fig. 5. Geant2: real netwo ology used in simulations.
-
on the variable power consumption part, which is the onlyone
that can vary and thus be optimized. Power consump-tion results
refer thus only to the variable powerconsumption.
Each connection request was characterized by a band-width demand
ranging from OC-1 to OC-192 units (i.e.from 50 Mbps up to 10 Gbps).
As the network load grows,that is, the number of busy connection
resources increasesmore and more with respect to the free/released
ones, wecontinuously monitored the overall network power de-mand,
the percentage of green energy used compared tothe maximum
available, and the network efciency ex-
minimum interference routing algorithm (MIRA) [40], al-ready
implemented in several commercial solutions, givesus a real
portrait of the power and GHG savings that will beconsequent to the
introduction of the proposed schemawithin real world
infrastructures, and, at the same time,demonstrates the absence of
signicant performance bur-dens in traditional network management
objectives(increasing blocking probability, reduced
load-balancing,etc.) due to the new energy optimization goals.
The behavior of the algorithm varying the k parameterhas been
extensively studied (Section 7.2) and, for the con-sidered network
topology, an optimal value of k = 3 was
probability. However, starting from 500 connection re-
e Gea
ing fr, 12, 25
m, 100ing ac, MIRked co
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2433pressed by the rejection ratio/blocking factor. All the
sim-ulation experience has been conducted in a properlycrafted
optical network simulation environment [38] thatallows the creation
of network topologies along with thespecication of simulation
parameters and congurationles. All the results have been determined
with a 95% con-dence interval not exceeding 6% of the indicated
values,estimated by using the batch means method with at least25
batches. All the runs have been performed on an Intel
Core i7-950 CPU @ 3.07 GHz with 16 GB RAM and 64 bitoperating
system server running Sun Java Runtime Envi-ronment v.1.6. In all
the experiments, we used a dynamictrafc model in which connection
requests, dened by aPoisson process, arrive with a parametric rate
of c re-quests/s and the session-holding time is exponentially
dis-tributed. The connections are distributed on the
availablenetwork nodes according to the above random-generatedor
predened trafc matrices, as summarized in Table 3.
In our lambda-switched optical framework, the re-sources
occupied by the routed connections are countedas the sum of the
ratio between the free and the busybandwidths along the edges.
Resources are thus repre-sented as the sum of the bandwidths on all
the networkedges, while the trafc volume is represented by the
quan-tity of the utilized bandwidth in a certain time.
In all the experiments, GreenSpark has not been com-pared with
other analogous power-containment solutionsknown in literature
because, at the state-of-the-art, almostall the available schemes
achieve their savings by power-ing off interfaces or entire nodes
(practice avoided in realnetwork as already mentioned in the
introduction), so thatthe comparison would be misleading since
shutting downan entire device would cancel its xed power
consumptionwhich, in our always-on approach, is present all the
time.Conversely, the use of provably efcient and publicly
avail-able algorithms such as min hop algorithm (MHA) [39] and
Table 3Parameters used in the simulations.
Simulation parameters Dant
Number of connections VaryRandom generated bandwidths {1,
3GreenSpark k 1, 3,Spark k, kHop 3, 20KOA,K3R 80 kSource,
destination VaryRWA algorithms MHAMeasurements Blocquests, its
blocking probability grows at quite a fast pace.All the algorithms
belonging to the Spark family (Spark,GreenSpark MinPower and
GreenSparkMinGas) performsensibly better than the other ones, and
all their versionsachieve similar and very satisfactory results in
terms ofthe connection blocking probability.
In Fig. 7 we compared the total power consumption(green and
dirty) obtained by the different algorithms ver-sus the connection
requests. MIRA reveals to be the highestpower consumer, followed by
MHA which, in contrast with
nt2 network
om 0 to 3000 with different resolutions4, 48, 192} OC-units with
different distribution probability
0 kmcording to a Poisson process, duration times exponentially
distributedA, Spark, GreenSpark MinPower, GreenSpark
MinGasnnections, power consumptions, green energy percentageschosen
as the best compromise between the different opti-mization
objectives of the two stages (load-balancing andgreenness) and time
performance (recall from Section 6that the complexity depends on
k). However, for clearnesssake, we rst show the results of the
comparative simula-tions with the other RWA algorithms (Section
7.1) and,then, show how the biasing of the k parameter affectsthe
performance (in terms of the two stage objective)and the time
complexity of GreenSpark for the given net-work topology.
7.1. Comparative simulation study
In the rst set of simulation shows, we report the com-parison of
the GreenSpark framework with other well-known RWA algorithms. In
these tests, the k parameterof GreenSpark has been tuned to an
optimal value (k = 3)for the considered network topology, as a
result of theextensive simulation study reported in Section
7.2.
In Fig. 6 we plotted the connection blocking probabilityversus
the generated connection requests. We can observehow MHA exhibits
the highest blocking probability, essen-tially due to the
congestion of the communication linksassociated with the shortest
paths. MIRA [40] improvesthe performance of MHA, and achieves lower
blocking
-
the previous graph, performs better than MIRA. This is due the
connections (and, thus, statistically introduces less
Fig. 6. Connection blocking probability versus connection
requests.
2434 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442to the longer paths chosen by MIRA with respect to MHAthat,
in turn, always chooses the shortest paths to routeFig. 7. Total
power versuspower consumption). In this graphic, we can also
observethe rst big difference inside the Spark family:
theconnection requests.
-
GreenSpark algorithms have lower total power consump-tion with
respect to the energy-unaware Spark. Besides,we note that the two
GreenSpark algorithms, MinPower
and MinGas, perform almost the same as for the totalpower
consumption, with MinPower doing slightly better,as expected.
Anyway, the fact that the two GreenSpark
Fig. 8. Green power (percentage) versus load (routed
connections).
ection
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2435Fig. 9. Load (routed conn s) versus power budget.
-
algorithms have almost the same total power consumptiondoes not
mean that their carbon footprint is the same. Thisleads us to the
next graphic of Fig. 8, in which the greencomponent of the power
consumption has been reported.
The results, highlight that there is big difference in theuse of
green energy depending on the chosen GreenSparkoptimization goal,
as well as for the other algorithms.GreenSpark MinGas exhibits the
topmost green powerusage percentage, i.e. it prefers lightpaths
passing throughgreen-powered NEs and avoids sites powered by
dirtysources as much as possible. More than 29% of the totalpower
used by GreenSpark MinGas comes from green en-ergy sources, thus
saving considerable quantity of CO2from being emitted by the
network during its operations.The other two algorithms of the Spark
family are charac-terized by a lower green power usage, as
expected, whilekeeping the blocking probability unaffected.
Althoughbeing energy-unaware, MIRA performed quite well in ourtests
in terms of green power usage percentage, basicallydue to its
minimum interference driving criteria whichtends to balance the
usage of network resources (whoseenergy is equally distributed
among green and dirty energysources), even if it exhibits a very
high connection blockingprobability, reaching values of 54%
starting from a load ofjust 1450 connections. MHA exhibits the
worst perfor-mance both for the green power usage and for the
connec-tion blocking probability, showing its limitations incomplex
network scenarios where a number of constraints,comprising the
energy-efciency ones, have to be takeninto account. A particularly
interesting issue comes from
the observation of the pseudo-sinusoidal trend in the useof the
green resources characterizing all the algorithms ofthe Spark
family. This behavior is due to the specic costand scoring
functions associated with this family, in whichless costly/greener
paths will be chosen rst, making thegreen energy percentage rise.
As the usage of green pathsraises, however, also the dynamic cost
assigned to suchpaths increases as a consequence of their increased
load(according to the load-balancing criteria of Eq. (8),
untilalternative non-green paths will be cheaper than the greenones
and thus will be preferred for connections routing.This will make
the green energy percentage decrease,but, at the same time,
increase the cost of these alternativepaths, until it will be again
more convenient to route theincoming connections on green paths,
and so on. Therefore,the pseudo-sinusoidal trend of the Spark
family is some-how a visual proof of the efciency of the two phase
selec-tion scheme which, at rst, tries to balance the networkload
and, then, to minimize the specic scoring function,such as the
total power (GreenSpark MinPower), the totalGHG emissions
(GreenSpark MinGas) or the total cost(Spark).
Starting from the consideration that it is a commonpractice that
network operators contract a xed powerbudget with their energy
supplier and then strive to re-main within that budget since
surpassing the thresholdwill result in high penalty rates on the
overall energy costs,in Fig. 9 we plotted the load versus the power
budget re-quired to route the connections. The energy-aware
Green-Spark algorithms exhibit an almost optimal growth trend,
us dif
2436 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442Fig. 10. Total power (green + dirty) and load (routed
connections) vers ferent algorithms at a load causing a blocking
probability of 0.05 (5%).
-
showing the highest increase in the load against a xedincrease
in the power budget with respect to the otheralgorithms. We can
observe that the entire set of connec-tions can be routed in the
network keeping its power
budget below the 70 kW threshold (and the blockingprobability at
the lowest observed values). From this pointof view, Spark performs
notably well, considering that it isenergy-unaware: its power
budget is only 83 kW. It is
Fig. 11. Connection blocking probability versus connection
requests.
ersus
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2437Fig. 12. Total power v connection requests.
-
worthwhile to note that inside the power budget of theSpark
algorithms, there are much many connections (more
than 2000) with respect to the MHA and MIRA algorithms,which
only route between 1200 and 1400 connections.
Fig. 13. Green power (percentage) versus load (routed
connections).
nectio
2438 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442Fig. 14. Load (routed con ns) versus power budget.
-
Furthermore, their power budgets are sensibly higher,being 140
kW and 195 kW respectively, between two andthree times more than
GreenSpark.
The last test of this series was conducted by keeping theQoS on
the blocking probability at the constant value of 5%.We measured
both the total required power and the loadoffered by the
algorithms. The graphic in Fig. 10 clearlyshows the great
improvements introduced by the Sparkfamily both for the power
consumption and the routedconnections. In particular, we observe
that the GreenSparkalgorithms need as low as half of the power
required byMIRA and MHA and route double the number of
theirconnections, making them very attractive also for QoS
con-strained networks with strict requirements on the connec-tion
blocking probability.
As a conclusion, we observe that there is a generationgap
between the Spark family and the traditional MIRAand MHA
algorithms, both in terms of power consumptionand blocking
probability. In particular, Spark performancesare quite
satisfactory, but GreenSpark algorithms, thanksto the two stages
load-balancing and green objectives bal-anced by the k parameter,
perform much better in terms ofpower and GHG, with MinGas even
superior than MinPow-er, since it considerably lowers the GHG
emissions whilekeeping almost the same total power requirements
thanMinPower. Results showed that GreenSpark algorithmsnot only
signicantly lower the required power and GHGemissions but also
increase the connections acceptance
ratio, showing that properly crafted RWA algorithms canenable
greener networks with even better performancethan before.
7.2. Tuning the GreenSpark k parameter
The k parameter value biases the load-balancing criteriaof stage
one (Eq. (8)) and the greenness criteria of stagetwo (Eq. (11) and
Eq. (12)). Stage one restricts the set ofpossible paths to the best
balanced k paths between in-gress and egress nodes according to its
cost functionx((u,v)k); stage two selects, among such paths, the
green-est one according to its scoring function (MinGas
orMinPower). At the extreme cases, a k value of 1 wouldrestrict the
stage one to always select the minimum costpath (thus, the best
load-balanced path according toEq. (8)) and the stage two to always
select the only pathavailable from stage one, making the algorithm
totallyenergy-unaware, and therefore reducing it to a
simpleDijkstra-based weighted shortest path (that is a minimumcost
one); from the other side, a large enough k value(greater than the
maximum number of possible paths be-tween any two nodes) would make
the algorithm totallygreen, completely discarding the
load-balancing effectof stage one. Therefore, smaller values of the
k parameterbias the solution by privileging well balanced paths,
whilelarger values of the k parameter privilege energy
relatedobjectives rather than the traditional network
eenSp
S. Ricciardi et al. / Computer Networks 56 (2012) 24202442
2439Fig. 15. Total power (green + dirty) & Load (routed
connections) versus Gr(5%).ark with varying k values at a load
causing a blocking probability of 0.05
-
8. Conclusions and future work
References
2440 S. Ricciardi et al. / Computer Networks 56 (2012)
24202442management ones. A in depth study of the k parameter
istherefore interesting and it is reported here for the
Geant2network topology.
In Fig. 11 we show the blocking probability of Green-Spark with
varying values of the k parameter versus theconnection requests. As
expected, the k value affects verylittle the blocking ratio since
the actual load-balancing isdone in the rst stage, whose output are
always the k bestbalanced paths; in this sense, load-balancing is
assured bystage one.
On the contrary, if we look at the total (green + dirty)power
consumption versus the connection requests re-ported in Fig. 12, we
observe that the higher the k value,the lower the total power
consumption, as expected. Infact, with higher k values, the second
stage will have thepossibility to choose among a greater number of
alterna-tive paths to minimize ecological footprint of the
network,both for MinPower and MinGas, which indeed performvery
similar; in this sense, the green aspect is committedto stage
two.
However, if we take a look inside the total power con-sumption
at the green power percentage versus the loadreported in Fig. 13,
we see that there is a notable differencein the use of green energy
sources at high k values. Withk = 5 and k = 7, the green power
percentage of GreenSparkMinGas markedly increases (whilst the
MinPower is in theaverage as expected being GHG-unaware), showing
thatthere is still room for green optimization at the expenseof
additional computational complexity due to the calcula-tion of the
higher number of alternative paths in the stageone. This result
suggested the basis of a future work ofours, in which we will try
to reach such greener pathsthrough a one-stage algorithm with an
omni-comprehen-sive energy-aware/load-balancing cost function
employedto directly achieve such paths. We also note that the
greenpower percentage for k = 1 is quite high, showing that
theload-balancing may have positive effects on the GHG emis-sions
when the energy sources are heterogeneously dis-tributed in the
network.
In Fig. 14 we plotted the load versus the required powerbudget
for different k values. As seen in Fig. 12, with thesame k value,
MinPower and MinGas perform quite simi-larly in terms of total
power consumption, but GreenSparkwill require different power
budgets depending on the kvalue. The higher the k, the lower the
power budget butalso the higher the computational complexity
requiredfor the path calculation at each connection request
set-uptime. Anyway, it is worthwhile to note that there is
greatimprovement between k = 1 and k = 3, and only limitedgain for
greater values, meaning that already with k = 3alternative paths
the GreenSpark framework is able tosensibly reduce the power budget
in an optimal balancebetween greenness and performance.
Finally, the results of the test on the QoS on the
blockingprobability at the constant value of 5% is shown in Fig.
15.The total power required decreases with the increase of thek
parameter value but again, while from k = 1 to k = 3 thereis a
great reduction of the total power, when passing fromk = 3 to k = 5
and k = 7 there is no such a great benet. Notealso that, at such
low load (5% of blocking probability),there is no great difference
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