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Optical Switching and Networking

Optical Switching and Networking 11 (2014) 113–118

1573-42http://d

n CorrE-m

luca.val(D. Phapiero.ca

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

Introducing cognition in TDM PONs with cooperative cyclicsleep through runtime sleep time determination

Francesco Zanini a, Luca Valcarenghi a,n, Dung Pham Van a, Michele Chincoli b,Piero Castoldi a

a Scuola Superiore Sant'Anna, Pisa, Italyb CNIT, Pisa, Italy

a r t i c l e i n f o

Available online 16 September 2013

Keywords:Cyclic sleepEnergy efficiencyPONsCognitiveQoS

77/$ - see front matter & 2013 Elsevier B.V.x.doi.org/10.1016/j.osn.2013.08.007

esponding author. Tel.: þ39 0505492138; faail addresses: francesco.zanini@sssup.it (F. Zacarenghi@sssup.it (L. Valcarenghi), d.phamvam Van), michele.chincoli@cnit.it (M. Chincostoldi@sssup.it (P. Castoldi).

a b s t r a c t

In this paper cognition is exploited to maximize energy efficiency while guaranteeingquality of service (QoS) constraints in TDM PONs with time-variable network conditions.In the proposed solution the previously introduced cooperative cyclic sleep schemeexploits runtime computation and adaptation to network conditions of the sleeptime. Simulation results show the potentials of the proposed approach in maximizingenergy savings while guaranteeing average delay constraints when applied to limitedbuffer ONUs.

& 2013 Elsevier B.V. All rights reserved.

1. Introduction

The term ‘cognitive’ when applied to networks has beendefined as the capability of a network to “…perceive currentnetwork conditions, and then plan, decide and act on thoseconditions … taking into account end-to-end goals …” [1].Cognitive networks were first introduced in the wirelessworld to adapt operating parameters to changing require-ments and radio channel conditions [1]. This approach isexpected, for example, to better utilize a wireless spectrumwhile guaranteeing the Quality of Service (QoS) requested bythe users.

Cognitive networks are starting to be proposed for opticalnetworks as well [2]. For example, in [2] it is reviewedhow cognition can be combined with an intelligent General-ized Multi-Protocol Label Switching (GMPLS) control planeand reconfigurable optical devices (e.g., gridless Reconfig-urable Optical Add/Drop Multiplexer—ROADM—and flexible

All rights reserved.

x: þ39 0505492194.nini),n@sssup.itli),

Orthogonal Frequency Division Multiplexing—OFDM) to pro-vide flexible bandwidth services (e.g., optical and packetseamless integration) in core optical networks. In particular,it details a transponder and a switch architecture based onoptical OFDM for cognitive optical networks. In [3] it is statedthat “when an optical network is aware, adaptive and learns itbecomes cognitive”. In the same paper a holistic framework,i.e. the COGNITION architecture, is defined for optical net-works. In particular a Requirement Layer (RL) is introduced.The RL acts as a cognitive broker between the users andcognitive elements present in each architectural layer (i.e.,application layer, service plane, control plane, managementplane, medium access control layer, and physical layer).

Currently, energy efficiency has become an importantrequirement and goal when implementing and managingcommunications networks. Both network providers andend-users are focusing their attention on energy efficiencynot only for decreasing Green House Gas (GHG) emissionsbut also for decreasing their operational expenses andenergy bills. In the access segment of a network, PassiveOptical Networks (PONs) are foreseen as the fixed accesstechnology of the future because they are capable ofproviding huge capacity with relatively low power con-sumption. However, among the fixed optical networks,

Fig. 1. Cognitive PON principle.

F. Zanini et al. / Optical Switching and Networking 11 (2014) 113–118114

PONs are currently one of the largest energy per bitconsumers because of the large number of subscribers andtheir low utilization. In particular, PON customer premisesequipments (i.e., Optical Network Units—ONUs) are contri-buting with over 65% to the overall PON power consumption[4]. Therefore, they are currently considered as the mainresearch target for improving PON energy efficiency.

Standardization authorities, industries and researchersare proposing several methods for decreasing ONU energyconsumption (see [5] and references therein), including amethod based on cycles of sleep and awake periods, calledcyclic sleep or fast sleep. While decreasing energy con-sumption, cyclic sleep, as well as the other proposedmethods, might cause an increase in the delay experiencedby the transmitted data, which can be critical for theQuality of Service (QoS) requested by the users. On theother hand the QoS degradation can be minimized by anoptimal scheduling of sleeping periods according to thesupported services. With this aim, the concept of service-based variable sleep period was introduced in [6] to saveenergy in ONUs while avoiding service degradation in thepresence of traffic with demanding requirements (e.g.,average delay). According to this approach, each class ofservice (CoS) is assigned a specific sleep period. If multipleCoS are received by one ONU, the sleep period of the mostdemanding class is selected.

This paper exploits cognition in Passive Optical Net-works (PONs), referred to as cognitive PONs for short, toachieve the goal of energy efficiency while guaranteeingend-user service QoS. The paper is based on the previouswork published in [7] where cognition in Passive OpticalNetworks was introduced. In this paper, cognition isutilized for the first time for the runtime computationand adaptation of the sleep time in the cooperative cyclicsleep scheme. The cooperative cyclic sleep scheme deter-mines when an ONU is switched to sleep, for how long theONU can sleep, and whether to wake up the ONU duringits sleep state based on traffic and buffer size of bothdownstream (DS) and upstream (US) transmission tomaximize energy saving while guaranteeing both DS andUS QoS constraints. The scenario considered in this papertakes also into account realistic situations such as thepresence of limited buffers at the ONUs and at the OLT.Results show that the proposed methodology minimizesthe energy while guaranteeing QoS constraints withdynamic traffic and QoS requirements.

2. Cognitive PONs

Current PONs, i.e. Ethernet-based PON (EPON) andGigabit Capable PON (GPON) and their evolution possessalready some degree of cognition in their managementprotocols (i.e., GPON ONU management and control inter-face layer (OMCI), GPON Transmission Convergence (TC)layer, EPON Multi Point Control Protocol (MPCP)). Indeed,by exploiting such protocols the Optical Line Terminal(OLT) is capable of setting parameters for fault manage-ment and performance monitoring at the ONU (by meansof OMCI) and to dynamically allocate upstream bandwidthto ONUs based upon their requests (by means of TC layerand MPCP). However, for example, dynamic bandwidth

allocation is not autonomic but it is based on ONU bufferstatus only, without accounting for experienced QoS.

For what concerns energy efficiency, sleep time-basedsolutions are not autonomic as well. Indeed ONU sleeptime is often pre-determined and set during the ONUregistration and the exchange of OAM messages betweenthe OLT and the ONU. As proposed in ITU-T G.Sup45 onlythe command of switching to sleep mode (i.e., sleeptriggering) is provided during the normal network work-ing conditions by utilizing, for example, modified dyinggasp PLOAM signaling, PLOAM-based signaling, or OMCI-based signaling. In this scenario it can happen that, due toa change in the services subscribed by the users connectedto an ONU, a longer sleep time would be allowed withoutviolating the end-to-end QoS constraints. Thus, higherenergy efficiency can be achieved while guaranteeingQoS constraints.

This paper proposes to utilize cognition based onEtherType value (in EPON) or deep packet inspection(i.e., looking not only at the Ethernet header in EPON butalso at parts of the payload) to dynamically adjust the ONUsleep time based on the services subscribed by the ONU tomaximize energy efficiency while satisfying the serviceQoS (e.g., delay) constraints. This concept is depicted inFig. 1 by considering downstream frames. By means ofpacket inspection, each ONU is capable of determiningwhich services are received by the end users connected toit. Based upon the knowledge of the service QoS para-meters (e.g., loaded into a table in the ONU memory), theONU is capable of computing the sleep time Ts as afunction of them. Each time the set of services, whichthe packets belong to, changes the Ts value is recomputedby the ONU. The main challenge in the implementation ofcognitive ONUs is the coordination of the sleep time ofdifferent ONUs connected to the same OLT. Indeed, if theirsleep times change independently, collisions in theupstream transmissions may occur. A possible solution tothis issue is to perform inspection and sleep time compu-tation for each ONU at the OLT, thus making the OLT thecognitive element. ONUs can also be equipped withcognition to detect user behaviors. For example, if all theusers attached to an ONU are inactive for a long period oftime the ONU can automatically switch itself off. Such

Fig. 2. Cooperative cyclic sleep protocol.

F. Zanini et al. / Optical Switching and Networking 11 (2014) 113–118 115

activity detection can be based again on deep packet inspec-tion of the content of the report messages sent by the ONU tothe OLT.

Cognition might not be limited, however, to deeppacket inspection. For example the utilization of flexibleOFDM/OFDMA bandwidth allocation would permit toimplement adaptive link rate without the utilization ofmultiple interfaces as proposed in [8]. By increasing thetransmission rate (either by utilizing more complex mod-ulation formats or more subcarriers) when more capacityis needed it will be possible to maintain the same sleeptime. However, an analysis needs to be conducted toevaluate whether the increase in power consumption, ifany, due the utilization of more complex modulationformats can be overcome by the energy saved during sleeptime. Moreover, in optical OFDM PONs, if several sourcesare utilized to generate the subcarriers they can bedynamically turned on–off or subcarriers can be inter-leaved [9] to save energy while assuring the transmissioncapacity needs. Similarly in WDM PONs or hybrid WDM/TDM PONs (also called TWDM PONs), wavelength or linecards at the OLT can be turned on–off dynamically basedon the capacity needs. This approach can also be combinedwith sleep mode based approaches.

The introduction of cognition appears therefore as ameans to implement energy proportional PONs: whenbetter performance (e.g., bandwidth or QoS) is requested,more resources, and thus energy, will be utilized.

3. Cooperative cyclic sleep scheme

The authors in [10] introduced the cooperative cyclic sleep(CCS) scheme that determines sleep triggering (i.e., when anONU is switched to sleep), sleep time computation (i.e., forhow long the ONU can sleep), and wake-up triggering orLocal Wake-up Indicator (LWI) (i.e., whether to wake up theONU during its sleep state) based on traffic and buffer size ofboth DS and US transmission to maximize energy savingwhile guaranteeing both DS and US QoS constraints. Thepossibility of utilizing OLT-ONU cooperation for sleep controlis also mentioned in the IEEE 1904.1 “Service Interoperabilityin Ethernet Passive Optical Networks (SIEPON)” workinggroup standard drafts [11].

The cooperative sleep scheme is illustrated in Fig. 2where the handshake protocol between the OLT and ONU,their corresponding states together with a timeline of theprotocol are depicted.

A sleep cycle is defined as the interval of time between thedispatch/reception of two consecutive ACK messages by theONU/OLT. A sleep triggering is generated by the OLT with aSleep req containing a desired sleep time (Ts) when the DSbuffer is empty. After sending the request, the OLT transits totheWAIT_FOR_ACK state and it starts buffering DS traffic. Uponrequest reception, the ONU can decide not to sleep, if the USbuffer is not empty, by sending a NACK and to continuetransmitting US traffic. In case the US buffer is empty, the ONUsends an ACK message, transits to the SLEEP state and startsbuffering US traffic. At the OLT, the reception of a NACK

indicates that the ONU cannot sleep, hence the OLT transitsback to ACTIVE state for data transmission. In case of ACK

reception, the OLT transits to ONU_SLEEPING state.

The ONU can sleep for whole Ts or part of it dependingon the occurrence of the LWI during SLEEP state. In thispaper, the LWI is triggered when the US buffer is almost-full, namely the available US buffer space is not enough forstoring the estimated US data coming within Toh duration(Fig. 2). Toh is the time needed for the ONU to switch “on”its transceiver, perform clock recovery and frame re-synchronization procedure. When the ONU wakes up,the ONU transits from SLEEP to POST_SLEEP state andremains there for a time equal to Toh. Upon expiration ofToh, the ONU sends all buffered US traffic (in Td time)followed, if the ONU buffer is empty, by a Confirm to askfor another sleep period. Otherwise the ONU remains inthe ACTIVE state. Upon reception of the first US data packetduring the ONU_SLEEPING state, the OLT transits to theACTIVE state and transmits the buffered DS traffic.If the Confirm arrives to the OLT when the DS buffer isnot empty, the OLT replies with an NACK to force the ONUto stay awake for receiving traffic. Otherwise, anotherSleep req is generated and sent to the ONU for startinga new sleep cycle.

4. Introducing cognition in cooperative cyclic sleep

In the absence of delay constraints, the idea at the baseof the energy consumption minimization is to make thesleep time as long as possible. If buffer size allows all thearrived packets to be stored in the buffer and then, oncethe buffer is full, to be sent in one burst. This approach isreferred to as buffer-oriented because the sleep timedepends on the buffer size only.

On the other hand, to minimize the delay, both theONU and the OLT need to be always “on”. In this way thepackets can be transmitted as soon as they arrive without

F. Zanini et al. / Optical Switching and Networking 11 (2014) 113–118116

accumulating delay while being stored in the OLT or ONUbuffer. However, in this case, no energy can be saved.Sometimes, based on the services subscribed by the ONU,a delay greater than zero but limited can be accepted. Thisvalue is determined by the required service delay con-straint. To satisfy this constraint the ONU must periodicallywake up and start the transmission of the packets that havebeen queued during the ONU sleep time. This approach isreferred to as time-oriented because the sleep time is fixedand it is computed only based on the service QoS.

This paper extends the CCS scheme presented in [10]and it proposes a novel runtime sleep time computa-tion and adaptation to maximize ONU energy efficiencywhile satisfying the traffic average delay. This approach isbased on the combination of both buffer-oriented and time-oriented methods.

4.1. Upper and lower bounds on energy per bit and delay

The average delay D is the sum of the average time that apacket must wait in the queue before being transmitted andof its average transmission time. The energy per bit Eb isdefined as the energy consumption of the ONU divided by thetotal number of bits Nbit that have been transmitted andreceived by the ONU

Eb¼ PaTaþPsTs

Nbit: ð1Þ

In Eq. (1), Pa and Ps are power consumption of ONU inactive and sleep state. Ta and Ts are ONU active and sleep time,respectively. The active time is the time the ONU stays activeand it also includes the time required by the ONU to wake-upfrom the sleep state, defined as overhead time Toh.

The minimum value of Eb for all the possible theoreticalsleep schemes is achieved when Ta is minimal and Nbit ismaximal. This happens in the ideal case when there is aninfinite buffer capable of storing all the packets that are thentransmitted in batch. In this ideal scenario the system storesall the packets while being in low power consumptionmode Ps. At the end, the device turns active and transmitsall the packets in one burst. In this scenario the device is “on”just during the transmission time and “off” otherwise. Accord-ing to previously mentioned considerations, in this scenario, itis possible to write

Ta ¼Nframe � Sframe; ð2Þwhere Nframe is the total number of transmitted packets andSframe is the time needed to transmit one packet of informa-tion. In this case the single contribution to the energyconsumption provided by the overhead time can be neglected.

Nbit is related to Nframe by the following equation:

Nbit ¼Nframe � Psize ð3Þwhere Psize is the packet size. If we define Iframe as theaverage packet inter-arrival time we have that

Ts ¼Nframe � Iframe: ð4ÞBy replacing Eqs. (2)–(4) into Eq. (1), we have that theminimum value of Eb, defined as Ebmin, results

Ebmin ¼SframePaþ IframePs

Psize: ð5Þ

In this scenario the average delay Dmax equals half ofthe total observation time (see Section 4.2 for moredetails). By combining Eqs. (2) and (4) we have

Dmax ¼Nframe � ðIframeþSframeÞ

2: ð6Þ

If we assume that Sframe5 Iframe, the packet transmissiontime can be neglected

Dmax ¼Nframe � Iframe

2: ð7Þ

The opposite scenario is when the device is always“on”. In this case the packet is transmitted immediatelyand the minimum delay Dmin, if the transmission time isneglected, results

Dmin ¼ 0: ð8ÞWhen the device is always “on”, the maximum value

of Eb, defined as Ebmax, is reached. In this case, the activetime Ta corresponds to the overall transmission timeNframe � Iframe plus the transmission time of the packetsNframe � Sframe. If we assume that Sframe5 Iframe, by combiningthe previous equations and assumptions we have

Ebmax ¼Pa � Iframe

Psize: ð9Þ

4.2. Combined time- and buffer-oriented policy

From the description presented in Section 4.1 an energy-delay trade-off must be faced. The delay can range from 0 toDmax while the energy can range from Ebmin to Ebmax.

To determine the sleep time, we focus on energy mini-mization first by applying the buffer-oriented approach. In thisapproach, when the ONU is in the sleep state, the OLT storesincoming packets into a buffer of size N until the ONU wakesup. The ONU is allowed to sleep as long as the OLT buffer isnot full. The time needed to wake-up from the sleep state (Toh)and the round-trip time (RTT) must be included in the sleeptime because the OLT must buffer packets while the ONU isregaining synchronization and while the OLT is sending theSleep req and waiting to receive the Confirm messages (asshown in Fig. 2). The sleep time Ts can be therefore expressedas

Ts ¼Nλ�Toh�RTT ; ð10Þ

where λ¼ 1=Iframe.The delay each packet experiences inside the queue

depends on its arrival time. If we assume a Poisson arrivalof the packets with an Iframe average interarrival time, thewaiting time of packet number N, arriving when the ONUwakes up, is null. The packet N�1, that arrived Iframe timeunits before packet N, waits Iframe inside the queue and so on.The packet average delay D equals

D¼ 1λ

∑Ni ¼ 0iN

¼ 1λ

NðN�1Þ=2N

; ð11Þ

where i is the ID of the queued packet.If Nb1, we merge Eqs. (10) and (11) and we substitute

both the buffer size N with the limited buffer constraint Band the average delay D with the maximum average delay

SPW

Iframedecreasing

Buffersizeincreasing

Cooperative

C

BA

Fig. 3. Energy per bit–delay tradeoff for both the cooperative and the SPWscheme [8]. DS rates vary from 10 Mb/s to 100 Mb/s with steps of 10 Mb/s.OLT buffer size varies from 7 to 140 packets with steps of 7 packets.

F. Zanini et al. / Optical Switching and Networking 11 (2014) 113–118 117

constraint Dmax, we obtain the following sleep time deter-mination policy.

If

2Dmax

IframeoB ð12Þ

then

Ts ¼ 2Dmax�Toh�RTT : ð13ÞOtherwise, that is if the forecast number of arriving

packets during twice the time of the maximum allowedaverage delay is larger than the buffer size, we reduce thesleep time according to the following equation:

Ts ¼ B � Iframe�Toh�RTT : ð14ÞEqs. (13) and (14) determine the sleep time in a system

using a cooperative scheme with limited buffer size. Thismethod offers the advantage of satisfying the maximumaverage delay condition imposed by the application QoS.In addition, it minimizes the energy consumption as longas the buffer allows to store the requested packets.

5. Performance evaluation

5.1. Simulation setup

The simulation framework has been implemented insoftware by using Matlabs. The framework models thecommunication protocol described in previous sections aswell as the cooperative cyclic sleep policies utilized forruntime sleep time calculation.

The data traffic is transmitted in frames with a fixed sizeof 1250 bytes. The frame arrival process is Poisson for bothDS and US transmission directions. Round-trip transmissiondelay is 0.01 ms. The transmission rate is set to 10 Gb/s andthe overhead time is set to Toh ¼ 2 ms. The performanceparameters are the average energy per bit and the averagedelay. The former parameter represents the overall energydrained by the ONU while active and asleep divided by allthe packets sent and received by the ONU. The latterparameter the average delay experienced by all the packetsreceived by the ONU.

For all the results the confidence interval at 95% con-fidence level has been computed. The maximum measuredconfidence interval is within 72.5% of the mean value.

5.2. Energy-delay tradeoff

Fig. 3 shows the energy per bit Eb–delay D tradeoff forboth CCS and the SPW scheme [8]. The results show theimpact that the buffer size and the average packet inter-arrival time has on the average energy per bit and on theaverage delay.

Each point in the plot is obtained by simulating atransmission with a specific DS rate and OLT buffer size.The ONU buffer size is fixed to 200 packets. DS rates varyfrom 10 Mb/s to 100 Mb/s with steps of 10 Mb/s while the USinter-arrival time is set to 1 ms. The OLT buffer size variesfrom 7 to 140 packets with steps of 7 packets. Each point inthe plot corresponds to the average energy/bit and averagepacket delay pair correspondent to each (DS rate, OLT buffer

size) pair. The energy/bit is computed as in Eq. (1) while theaverage delay is computed by measuring the time fromwhen the packet arrives to the OLT and when the packet isreceived by the ONU.

It can be noted that the SPW scheme delay is almostconstant. Its value is between 1 and 2 ms for all thesimulated scenarios. The reason is that, in this policy, assoon as an upstream packet arrives to the ONU, the ONUwakes up and the transmission starts on both OLT andONU sides. As a consequence, the downstream packetdelay variation is small. The energy per bit values arealways greater than or equal to the ones of the cooperativeapproach, for any data rate. This is due to the frequentswitching of the ONU between ACTIVE and SLEEP states.

On the other hand, as Fig. 3 shows, both average energyper bit and average delay in CCS vary as a function of thebuffer size and of the average DS frame interarrival time.Therefore, given a DS frame rate, it is possible to movealong curves at constant DS frame interarrival time bymodulating the buffer utilization. Moreover, if the delayconstraints are loose, the average energy per bit curvestend to the minimum limit described in Section 4.1. Thus,in the CCS it is possible to trade average delay with energyconsumption. Moreover Fig. 3 shows that the higher is thebit rate (i.e., the lower is the value of Iframe) the lower is theaverage energy per bit because the network capacity, andin turn the network energy, is more efficiently utilized.

5.3. Cognition potentials

The potentials of introducing cognition in the CCSthrough runtime computation of Ts based on the methodo-logy described by Eqs. (12)–(14) are the ability to minimizethe energy while guaranteeing the delay constraints.

Usually, in a typical network scenario, a networkstream consists of multiple streams. By assuming thatpackets in each stream arrive based on a Poisson process,

F. Zanini et al. / Optical Switching and Networking 11 (2014) 113–118118

it is possible to write

λT ¼ λ1þλ2þλ3þ⋯þλF ; ð15Þwhere λT is the overall arrival rate consisting of the sum ofF stream interarrival rates. Each stream might have differ-ent average delay requirements and the network mustguarantee the minimum of them DT. This constraint isexpressed as

DT ¼minfD1;D2;D3;…;DFg: ð16ÞEqs. (15) and (16) set a network working point in the

Eb–D plot as shown by point A in Fig. 3. Point A corre-sponds to an average delay of 20 ms and an inter-arrivaltime of 1 ms. If an additional stream is added to the formerone, the overall arrival rate changes and the delay con-straint might change as well. Indeed, assume that theadded stream has an interarrival time of 1 ms and a delayconstraint of 15 ms. Based on Eqs. (15) and (16) thecumulative stream has an interarrival time of 0.5 ms anda delay constraint of 15 ms. Therefore, by the introductionof cognition, it is possible to change the working point tothe point marked with the letter B, simply by tuning thesleep time based on Eqs. (12)–(14). This working conditionoffers the lowest energy per bit for the given constraintsand arrival rate. If, with the same streams, the delayconstraint changes to 5 ms, the new working point C couldbe reached by further adjusting the sleep time. In allpreviously mentioned points, the buffer utilization η foreach point depends on both the average arrival rate andthe sleep time and it is expressed by the followingequation:

2DT � λTB

¼ η: ð17Þ

Therefore cognition offers the advantage of adapting atruntime the working point of the system to minimize theenergy consumption while satisfying both the quality ofservice and the buffer size constraints.

6. Conclusion

This paper showed the potentials of introducing cogni-tion in the cooperative cyclic sleep scheme in TDM PONs.Through cognition the sleep time is dynamically recom-puted to adapt it to time variable network conditions (e.g.,traffic load) and QoS constraints.

Results showed that cognition is beneficial because itallows to follow the minimum energy curves withoutviolating QoS constraints and the network device physicallimitations (i.e., buffer size).

Acknowledgment

This work was funded in part by the “Novel ONUs forEnergy efficient optical access networks (NOÈ)” project,part of the joint higher education project “Providingtraining for applied and technological research (FORTEC)”,co-financed by the Region of Tuscany within the frame-work “POR CRO FSE 2007–2013 Asse IV—Capitale Umano”.

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