Photon Netw Commun (2013) 25:120–134DOI 10.1007/s11107-013-0396-0

A midterm DBA algorithm for quality of service on aggregationlayer EPON networks

Andreas Bontozoglou · Kun Yang · Ken Guild

Received: 28 May 2012 / Accepted: 1 February 2013 / Published online: 19 February 2013© Springer Science+Business Media New York 2013

Abstract This work presents a new approach on dynamicbandwidth allocation (DBA) for Ethernet Passive OpticalNetworks (EPONs). A brief introduction to the DBA areaand major term definitions are given. The related researchand standardization efforts are presented. Justification thatEPONs can be used on the aggregation network is pro-vided, based on their evolution and related research pro-posals. Focus is given to the Long Reach-Passive OpticalNetworks (LR-PONs) and their limitations which show theneed for a non-polling, midterm DBA scheme for next-generation EPONs. The challenges arising, because of thisnew approach, are discussed along with possible solutions.Finally, this work proposes the EMDBA algorithm which isable to overcome the discussed issues. The correct opera-tion of this algorithm is confirmed by a set of simulationsusing OMNet++ framework, and the outcome results showthat EMDBA performance is satisfactory in terms of delayand service differentiation.

Keywords Dynamic bandwidth allocation ·Queue management · Ethernet Passive OpticalNetwork (EPON) ·MPCP ·Multi-service environments

1 Introduction

The appearance of new services in today’s broadbandaccess networks has led to a huge increase in bandwidth

A. Bontozoglou (B) · K. Yang · K. GuildUniversity of Essex, Wivenhoe Park, Colchester, UKe-mail: abodoz@essex.ac.uk

K. Yange-mail: kunyang@essex.ac.uk

K. Guilde-mail: kguild@essex.ac.uk

requirements for aggregation and core networks. Mostproviders nowadays prefer to use scalable and cost-effectivesolutions to support their access networks. One of the fastemerging technologies, that is already being deployed, isEthernet Passive Optical Network (EPON), also known as802.3ah [1].

Ethernet Passive Optical Network networks utilize thehigh capacity provided by optical fiber infrastructure (1G for802.3ah and 10G for 802.3av [2]). The overall EPON archi-tecture consists of a single Optical Line Termination (OLT)and multiple Optical Network Units (ONUs) as shown inFig. 1. All the components in between are passive (require nopower to operate) making them robust, cheap to deploy andeasy to maintain. Due to this design, the downstream direc-tion is Point-to-Multi-Point (PtMP), while on the upstreamdirection the bandwidth has to be shared between the ONUsattached on the network. A variety of methods have beenproposed for the upstream bandwidth sharing includingTime Division Multiplexing (TDM) and Wavelength Divi-sion Multiplexing (WDM) from which the first seems to bepreferred due to simplicity and cost-effectiveness.

The OLT device is the one responsible for sharing/splittingthe upstream bandwidth between the attached ONUs. Per-forming Dynamic Bandwidth Allocation (DBA), rather thanstatically assigning bandwidth to the ONUs, uses more effi-ciently the available capacity of the uplink in the majority ofcases. Specifically for the EPON networks, Multi-Point Con-trol Protocol (MPCP) is included in the 802.3ah standard tosupport DBA signaling, providing the Request (REPORTs)and Allocation (GATE) messages. In general, DBA algo-rithms can be categorized as:

– Predictive or Reactive: based on how they operate. Apredictive algorithm—as the name suggests—will try topredict the required resources and allocate them, while a

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Fig. 1 Overall EPON architecture

reactive one will detect the need for more bandwidth andtry to satisfy it [3].

– Queue- or Rate-based: based on the state informationused from the algorithm in order to decide on the amountof bandwidth needed to be allocated.

– Short-term, Midterm or Long-term: based on howoften (in which granularity/interval) the DBA algorithmis applied [4,5]. Depending on the execution interval, var-ious problems arise and historical data have to be evalu-ated in a different way.

Additionally in modern networks, usually more than oneservice is provided over the same line, with the most commoncombination to be the triple-play voice, video and data ser-vices. EPON technology enables this type of services usingmultiple Logical Link IDs (LLIDs) between one ONU andthe OLT. In this case the ONUs differentiate traffic based onthe service class it belongs to and use priority queues to trans-mit it. Thus, bandwidth allocation has to be performed notonly between ONUs but also between service queues in eachONU. The first is known as inter-ONU allocation and thesecond as intra-ONU allocation. A variety of schemes havebeen proposed in the literature, falling into two categories:

1. OLT is responsible for both intra- and inter-ONU band-width allocation. This scheme simplifies the operationalrequirements of the ONUs but adds complexity to theOLT scheduler.

2. Joint allocation, found in the articles [6] and [7], wherethe OLT performs only the intra-ONU bandwidth allo-cation and the ONUs are responsible for the inter-ONUallocation. This scheme, contrary to the previous one,increases the complexity on the ONU devices while itrelaxes the OLT.

As mentioned before, the huge line rate that can beachieved from EPONs makes them a flexible technologyapplicable to many levels in a network. The network levels (orsub-networks) assumed in this work are Access, Aggregation(also referred as metro [8]) and Core. The major differencesbetween these levels lie on the bandwidth requirements andthe received traffic characteristics. Numerous proposals existin the literature for using EPON on different layers includingFiber-To-The-Home (FTTH) as access network and Fiber-To-The-Curb (FTTC) as aggregation network.

The major novelty of this work is the proposal of a newmidterm, non-polling, DBA approach for EPONs, whichenables more ONUs to be attached to the network (fol-lowing LR-PONs trends) by reducing signaling overhead inthe downstream direction. The contribution of this work istwofold: (a) New issues arising from the proposed approach(explained in Sect. 4) are identified, and (b) a QoS-awareDBA algorithm that addresses these issues, following theproposed non-polling approach, is presented.

The rest of the paper is organized as follows: Sect. 2presents the major related work in this area. Section 3

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explains in detail the motivation behind this work along withits contributions. Section 4 identifies challenges in the contextof this work. Next, Sect. 5 presents the proposed algorithm,followed by the simulation design and the result discussionin Sect. 6.

2 Related research

Research relative to DBA on PON networks is presented inthis section. Articles are listed in a logical order, showing dif-ferent problems found in the area and the proposed solutions.

One of the first DBA algorithms to be presented for EPONnetworks was IPACT (Interleaved Polling with AdaptiveCycle Time) [9], and because of this, it is often used inthe literature for comparison purposes. IPACT is a polling-based, short-term, reactive DBA and includes three basic dis-ciplines: Fixed Service (FS) where the ONUs always get thesame bandwidth (requests are ignored), Gated Service (GS)where ONUs get all the requested bandwidth and LimitedService (LS) in which the ONUs will be allocated what theyrequested but only up to a limit. An analytical model for eachof the above IPACT services was presented in [10]. The mod-els were based on the assumption that the incoming packetrate follows a Poisson distribution and that all the packetshave fixed length. It was proved that breaking these assump-tions leads to a very complex system that probably cannotbe mathematically analyzed. Finally, a simulation using ns-2was performed and showed that the analytical model fits itsresults.

Additionally, the IPACT algorithm suffers from the light-load penalty explained in [11] for cases of multi-servicescenarios, where lower-priority packets may suffer fromhuge delay. This happens (as the name suggests) only whenthe network load is low. The authors of the above arti-cle presented two optimization techniques and their advan-tages/disadvantages. The first one utilized two-stage buffersin the ONUs, while the second one tried to make the algo-rithm predictive. In order for the prediction to be accurate, theauthors assumed they have knowledge about the low-prioritytraffic behavior. Another proposal for addressing the aboveproblem is proposed in [12], where the authors presentedthe DBA1 algorithm, which is light-load penalty free. DBA1has to wait, though, for all ONUs to report before executing,which results in some idle time. The same authors presentedDBA2 to improve upon this.

The COPS (Class-of-service Oriented Packet Scheduling)algorithm [7] is implemented in the OLT while the ONUs areonly responsible for the REPORT messages. The algorithmworks with credits/tokens, assigned to each ONU, and theONU priority is defined by a background running process.Based on the priority, a Round Robin (RR) loop is applied onthe ONUs, and the higher-priority queue is considered first.

The algorithm is also based on polling intervals and has avariable time cycle (Tcycle). In addition, a rate-based opti-mization was presented in order to avoid the grant delay forthe high-priority queue. This delay is caused by the packetsthat arrive to the ONU in the interval between the REPORTtransmission and the GATE arrival, which is also causingthe light-load problem mentioned above. The specific tech-nique tries to predict the arrival rate (and thus a more accu-rate queue length) for the first-priority queue, based on thehistorical data. Finally, a custom simulator was developed(C++), and various scenarios were tested and compared withthe IPACT-LS algorithm. The results included average delay,link utilization, packet loss and maximum delay. Note thatthe rate optimization was also applied to the IPACT algo-rithm, but still the COPS seems to outperform the IPACTalgorithm in most of the cases.

In [13], a burst-polling, queue-based DBA algorithm ispresented. The authors explain that the IPACT algorithm isnot sufficient or suitable for delay and jitter-sensitive ser-vices because of its variable polling cycle. Thus, this paperpresented an algorithm, which is based on burst polling andMPCP protocol’s GATE and REPORT messages. Three basictraffic classes were assumed: Expedited Forwarding (EF),Assured Forwarding (AF) and Best Effort (BE). The algo-rithm is running on the OLT listening for REPORT mes-sages from the ONUs (reporting the status of each queue).The results of the DBA are announced to the ONUs with aburst of GATE messages. The algorithm itself is allocatingjust-enough bandwidth on the non-congested case, and thesurplus bandwidth is distributed fairly to the ONUs that haveto transmit more than the minimum bandwidth. In a con-gested situation, the algorithm is giving priority to the EFclass without changing the other classes. Overall, the algo-rithm is triggered in regular interval (cycle time).

A fuzzy logic-based DBA is proposed in [14]. The pro-posed algorithm had two main parts: (1) OLT side is doinginter-ONU allocation using a scheme based on IPACT limitedservice, and (2) ONU is applying the intra-ONU fuzzy algo-rithm, called IFLDBA, in order to assign bandwidth to eachqueue separately. For validation, the authors assumed a three-class scenario (EF, AF and BE classes) and compared theirresults with the BP algorithm [13] because of the commonparameters and the same capabilities on distinguishing ser-vices. The results of the simulation showed that the IFLDBAhas better performance when it comes to contention (and thesame performance in other cases). Furthermore, the IFLDBAalgorithm seems to react better in light-load scenarios.

A different approach is found in [15], where a MACprotocol for EPON, called BGP (Bandwidth GuaranteedPolling), is proposed in order to handle strict QoS require-ments between the ONUs. The algorithm is based on RoundRobin (RR) polling. RR is applied on an Entry Table ofONUs, which includes the RTT of the device. Each entry

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represents a portion of bandwidth/time, and thus, one ONUcan be included multiple times in this table. Additionally, themathematical analysis of the proposed scheme is presented,and a simulation is performed to validate BGP in compar-ison with IPACT. The advantage of such a method is thatit respects SLAs (Service Level Agreements) and supportsthem into the MAC layer.

A survey paper [8] was found focusing on Long Reach-Passive Optical Networks (LR-PONs) which can reach dis-tances up to 135 km and support up to 16,384 users. Theauthors, after explaining the research challenges in this area,listed projects/demonstrations from universities and compa-nies showing different LR-PON designs. The parameters andcapabilities of these were summarized. At the end, this paperaddressed control plane and DBA problems that arise fromthe addition of more users in the network and the increasedcoverage distance. The major concern was the Round-TripTime (RTT) that for LR-PONs is increased up to 1ms for100 km, compared to the 0.1 ms for a 10 km PON, which incombination with the increased number of users results indelays on polling and DBA functions of the PON network.Finally, two feasible solutions proposed in the literature werepresented: (1) The Multi-Thread Polling (MT) and (2) a Two-State DBA (TSD) approach.

Focusing also on the RTT increase, the authors of [16]proposed a DBA method that is able to handle coexistingshort- and long-range ONUs attached on the same network.The short-range ONUs are handled using the conventionalREPORT/GATE mechanism, while the long-range ONUs arehandled by a predictive DBA which is able to allocate up totwo cycles ahead. This gives the opportunity to the long-range ONUs to transmit data in cycles that are shorter thantheir RTT. The proposed DBA method was tested on a real-machine environment where it was shown that it achievesless latency than the conventional DBAs for the long-rangeONUs (1,300µs) while it does not affect the short-rangeONUs (1,000µs).

In the same area is [17] which focuses mainly on the DBAschemes proposed in the recent literature in order to enhancethe performance of LR-PONs. After an introduction to con-ventional PON types (EPONs, GPONs and WDM-PONs),the authors presented the challenges and architecture of LR-PONs. An extensive survey of 19 DBA algorithms followed,categorizing them into QoS aware and QoS unaware. Mostof these are extensions of the MT and TSD approaches (like[18] and [19]), enhancing the performance in terms of meanpacket delay, Packet Delay Variation (PDV), network uti-lization and packet loss. Additionally, the majority of thepresented algorithms take into consideration SLAs and run-time overheads.

In [20] the authors identified three distinct componentsof DBA algorithms that are usually examined individually.These are (1) the grant scheduling framework—which event

triggers the DBA, (2) the grant sizing policy and (3) the grantscheduling policy. Recent literature techniques were pre-sented for each of the above DBA sub-components focusingmainly on single-thread polling mechanisms. Additionally,the authors proposed a new grant scheduling policy for excessbandwidth allocation (Excess:Share), enhancing the limitedwith excess distribution scheme. This scheme is designed tobe used with the double-phase polling (DPP) grant frame-work presented in [21]. The proposed algorithm was testedagainst various combinations of the above sub-componentsin a simulation environment using EPON with 10, 50 and100km span. The simulations identified how each componentaffects the whole system’s performance. The results showthat the proposed scheme performs better in terms of packetdelay and channel utilization.

An interesting finding is presented in [22], where theauthors give the definitions of offline/online scheduling andexplain the different pre-transmission delays that occur inpolling processes. Later on, they compare the online Inter-leaved Polling (like IPACT) with three offline polling mech-anisms: using one, two and three threads in MT fashion. Forthe simulation, 16 ONUs are placed randomly at the last5km of the 100km PON span. The results show that the MTapproach achieves lower reporting and queuing delays but IPhas lower grant delays, and thus, the overall pre-transmissiondelay in IP is lower. This, as the authors explain, happensbecause more bandwidth is wasted on guard intervals andreports message when three threads are used. Finally, IPACTalso achieves higher utilization.

To date, most reported literature focuses on short-termbandwidth allocation algorithms. A variety of problems havebeen identified in this area, and different solutions havebeen proposed to overcome problems like light-load penalty,underutilization of the network and QoS support. Most ofthe schemes focus on QoS performance and network utiliza-tion. In contrast, this work focuses on the signaling overheadcaused from the introduction of more ONUs in EPONs. Themotivation behind the work presented in this paper and itscontribution are discussed in the next section.

3 Motivation and contribution of this work

3.1 Motivation

The work presented here considers the DBA problem inthe framework of recent evolution of EPON networks. The10G EPON standardization committee has already publiclyreleased the 802.3av, making EPON a high-capacity network.This, in conjunction with the supported features of LR-PONspresented in the previous section, leads to the conclusion thatEPONs are not only an access technology, but can be usedat the aggregation level to support multiple access networks

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(e.g., EPON supporting a number of DSLAMs for ADSLconnections or the FTTC application previously described).

As mentioned in Sect. 1, different network layers receivetraffic with different characteristics. Access network traffic isusually more bursty, since it is produced by a small numberof flows/applications and has low bandwidth requirements.In aggregation networks, many flows are combined togetherthat not only increase the bandwidth requirements, but alsomake the traffic smoother. For example, if an ONU requires a100 Mbps rate on the uplink, individual application’s spikesare not going to affect this figure. Thus, short-term, queuestate–based DBA algorithms can be more efficient/effectivefor access networks (bursty traffic) but not for aggregationnetworks.

Additionally, the constant polling in too short interval(Tcycle < 12 ms), applied from a short-term DBA, resultsin wasted bandwidth. This is illustrated in Fig. 2 whereMPCP overhead is plotted against Tcycle and the numberof ONUs in the network. The MPCP packet size is setto 84 Bytes, which is the minimum packet size including:8(pre) + 12(MAC addrs.) + 2(Type/Length) + 46(data) +

2 4 6 8 10 12 14 16 18 20 0

100 200

300 400

500 600

700 800

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0

20

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Ove

rHea

d (%

)

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ONUs

0 10 20 30 40 50 60 70 80 90 100

2 4 6 8 10 12 14 16 18 20 0 2000

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8000 10000

12000 14000

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)

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0 10 20 30 40 50 60 70 80 90 100

(a)

(b)

Fig. 2 MPCP polling overhead (downstream) with increased numberof ONUs. a 1G EPONs, b 10G EPONs

4(FCS) + 12(IFG). Furthermore, it is assumed that at leasttwo GATE messages need to be sent in the downstream direc-tion, one to trigger a REPORT message and one to grantbandwidth to the ONU. The overhead calculated ignores anylaser on/off times or idle problems and represents only thebandwidth used in the downstream direction. The equationfrom which it is derived is as follows:

O = 103 ∗ 2 ∗ M ∗ 100 ∗ N

Tc ∗ L(1)

where M is the MPCP packet size (bits), N the number ofONUs, Tc the cycle time (ms) and L the line rate (bps).The number of ONUs used is based on LR-PON propos-als reviewed in [8] and summarized in Table 1 along with thepotential overhead.

It is shown that the MPCP overhead is increased dramat-ically with the number of ONUs, leaving two options forLR-PONs: (1) Increase the Tcycle which will increase packetdelay or (2) employ a non-polling transmission and DBAmechanism.

The above-described characteristics of short-term, polling-based DBA lead to the conclusion that a different methodshould be applied when focusing on the aggregation layer ofa network. As mentioned in Sect. 1, DBA algorithms can becategorized as short-term, midterm and long-term based onhow often they are applied. In this work these are defined asfollows:

– Short-term: Algorithms that are applied in a very smallinterval and do not use any historical data. Thus, this typeof algorithm is mainly based on queue status information,and they are reactive. This category is a synonym withthe term Scheduler.

– Long-term: Algorithms that are mainly based on histor-ical data and learning. These would include algorithmsthat run per hour or daily and try to predict the requiredtraffic based on their knowledge/training.

– Midterm: Any algorithms that, unlike schedulers, do notrun in very short intervals. Instead, they coordinate band-width allocation on a per-second or per-minute basis.

3.2 Contribution

As mentioned before, the major novelty of this work is theintroduction of a new midterm DBA approach for EPONs,which enables more ONUs to be attached to the network (asresearch on LR-PONs suggests) while it reduces signalingoverhead in the downstream direction. In this DBA algo-rithm/scheme, ONUs are not continuously polled; instead,the DBA can be triggered on pre-configured regular inter-val or on an event detection basis. Advantages of such anapproach include the following:

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Table 1 Example LR-PONdemonstrations[8] Project Base Reach (km) λ D/U(Gbps) ONUs Overhead (%)

ACTS-PLANET [23–28] APON 100 1 2.5/0.311 2,048 22

B.T. [29–31] GPON 135 40 2.5/1.25 2,560 27.5

WDM-TDM [32,33] – 100 17 10/10 4,352 11.7

PIEMAN [34] – 100 32 10/10 16,384 44

WE-PON [35] G&EPON 100 16 2.5/2.5 512 5.5

SARDANA[36,37] G&EPON 100 32 10/2.5 2,560 6.8

1. REPORT and GATE messages are decoupled since theDBA can be triggered only if an extreme event is detected.

2. The DBA execution interval (or granularity) is a parame-ter that varies depending on the traffic characteristics ineach case, making it applicable to a variety of scenariosat the aggregation layer.

3. Signaling overhead is reduced, depending on the execu-tion interval, since ONUs do not need to be polled in eachcycle.

4. The algorithm can take under consideration data rate limi-tations (SLAs), which is hard for short-term DBAs, alongwith service priorities.

5. Due to the non-polling nature, it can apply stricter QoS,avoiding the unpredictable delay of variable polling cycleand overcoming the light-load penalty.

Because of this different, midterm, approach of the DBAscheme, a set of new challenges arise. Therefore, the contri-butions of this work are twofold: (a) The new problems areidentified and explained/discussed and (b) a DBA algorithmis proposed, which has the advantages listed above and at thesame time deals with the new issues. Challenges and con-siderations on this non-polling approach are presented in thenext section.

4 Challenges in midterm DBA

The first problem to be noted for a midterm DBA is on whatthe ONUs have to report. As mentioned in the introduc-tion, two DBA approaches exist: queue-based and rate-based,which are discussed below.

4.1 Queue-based approach

Most of the DBAs presented in the literature use thisapproach. Queue status is used to represent the ONU’srequested bandwidth. The fact that the queue length isreported makes these algorithms reactive, since the OLT hasto first detect the traffic before allocating.

This technique is easily applicable on polling DBAs due tothe small cycle time used. Trying to apply this on a midterm

DBA leads to various problems. First and most importantis that increasing the DBA interval, along with the higherdata rate found on aggregation networks, makes the ONU’squeues to be filled up before they get a chance to send aREPORT message. This behavior makes the reported queuestatus very unreliable to use as an input to the DBA process.An alternative solution would be to take under considerationthe queue’s drop rate, but reacting after drop rate is detectedwould collapse QoS. A more concrete approach that solvesthis problem is to use the incoming data rate of the queue,which is what is used in this work.

Data rate can be estimated based on the queue byte lengthreports using the following equation:

Ri = Ri−1 + Qi − Qi−1

I(2)

where Ri−1 is the previously allocated rate, Qi the bytes inthe queue now, Qi−1 the bytes in the queue in the previ-ous interval and I the DBA execution interval. Two prob-lems exist, though, with the above equation: (1) Drop rateis ignored when the network is congested and (2) queue sta-tus can became very unstable over long intervals, and con-trol mechanisms should be applied for stability [3]. Theseproblems have been faced in the past when midterm DBA isneeded, like in [38] where DBA and QoS provisioning oversatcom links were addressed. Specifically in the case of anEPON network, the previous allocated data rate Ri−1 maynot be fully utilized due to the lack of fragmentation of theEthernet frames.

Additionally, although this work does not consider AQM(Active Queue Management) queues, a queue status basedDBA approach cannot work in combination with AQM tech-niques. In most AQM schemes the queue decides if a newlyarrived packet should be queued or dropped [39] and thus thereported length would be inaccurate.

All described limitations of queue state–based DBAslead to the conclusion that a rate-based approach should beemployed for midterm bandwidth allocation. Furthermore,this work considers that the incoming data rate is reporteddirectly from the ONUs, rather than being estimated from

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Fig. 3 Rate-based allocation problem

eq. 2, since this does not require memory of previous alloca-tions and is more accurate under heavy congestion.

4.2 Rate-based approach

The use of incoming data rate would solve most of the queueproblems for aggregation-level networks, but introduces newones. The main feature of this approach is that it is more pre-dictive rather than reactive, since rate represents the expectedtraffic for the next period of time. The flaw/drawback is thatit does not give any indication of the current congestion levelof the queues.

A simple example to demonstrate the effect of the above-described problem is the sudden “rate departure.” Take, forexample, an ONU device that serves as a single ConstantBit Rate (CBR) flow. The queue length will rarely be zerosince the ONU has to wait for its transmission time slot. Atsome point this flow terminates and the ONU does not detectany incoming traffic, resulting in not being allocated anybandwidth. In this case the packets currently in the servicequeue are going to suffer from unpredictable delay, since theyare not going to be transmitted until a new flow arrives to thatONU.

This problem can be generalized since the DBA algo-rithm satisfies only the future incoming traffic and not thecurrently queued one, leaving the queue status virtually atthe same level. This problem gets more severe when VBR(Variable Bit Rate) traffic is used. VBR traffic leads to unpre-dictable packet delays. This is illustrated in Fig 3 where R1

is what is currently allocated to the queue and R2 is theincoming bit rate, where R1 < R2. In the next round R2

is allocated, but due to the higher rate, the queue has alreadycollected some packets. If the incoming rate increases, thepackets in the queue will be increasing. If the rate remainsconstant, the queue length will remain (virtually) the same.Finally, if the incoming rate decreases (R3 < R2), the alloca-tion will be decreased, leaving the queue at almost the samelength.

Although the decreased incoming data rate will relieve thequeue from some packets, it was shown in simulation that

it is not enough to bring the queue back to its initial state.Underutilization is caused due to the lack of fragmentationof the Ethernet frames, and thus, the allocated rate cannotalways be fully used. In such cases the queue is left withmore packets/bytes than before the R2 spike. In the longterm, this results in the queue length to constantly increaseover time with a very small increase rate, leading eventuallyto large packet delays and packet drops.

To conclude, it is now obvious that despite the fact thatrate-based DBA is more appropriate for midterm resourcemanagement, the queue status cannot be ignored. The raterepresents the future need of bandwidth, while the queuelength shows the current condition. Thus, we propose that acombination of the two reporting values has to be used for amidterm DBA.

4.3 Synchronization

Another noticeable and more practical problem arises withthe cancellation of the EPON’s polling mechanism. Althoughpolling results in channel underutilization, it guarantees in aneasy way that two ONUs will never transmit at the same time.Thus, polling acts as both a DBA and a collision avoidancemechanism.

This work presents a non-polling DBA method whichneeds to employ a different synchronization technique toavoid collisions in the upstream direction. Therefore, in thiswork, the Super Slot approach is assumed for the ONUsclock management [40], in which the ONUs are shifting theirupstream clocks and synchronize based on the downstreamSYNC_CODE sent between Super Slots. This is presentedin Fig. 4, where the Super Slot length is equivalent to Tcycle

of polling medium access control approaches since it is thetime required for all the ONUs/services to transmit. Thesetwo terms are going to be used interchangeably in this workfrom now on. Additionally, this time (Tcycle) is not asso-ciated with the DBA execution interval, and it can be setseparately.

Although synchronization can be achieved by the useof the Super Slot and clock shifting only, [40] proposesthe division of the Super Slot into smaller time slots. Thisenables OAM (Operations Administration Maintenance) totake place in empty time slots.

4.3.1 Minimum allocation requirement

In the last mentioned presentation [40], a number of con-siderations about Time Division Multiple Access (TDMA)on EPON networks and possible solutions were discussed.One of the problems mentioned is that this approach does notsupport jumbo frames, but this is not the only frame-relatedlimitation.

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Fig. 4 Upstream andDownstream synchronizationfor non-polling EPON TDMA.a Upstream direction, bDownstream direction [40]

(a)

(b)

Fig. 5 Min. Allocation versus Super Slot length (Tcycle)

Minimum allocation becomes an important limitationaffecting the Super Slot length. This happens because Ether-net frame cannot be fragmented, which means that each ONUmust have enough time allocated to fit a Maximum TransferUnit (MTU). For 802.3 this would be 1526 Bytes for oneframe (without any 802.1Q tagging, nor the IFG), includingthe 8 preamble bytes used from EPON to store LLIDs. Thus,the minimum rate allocation (per second) per ONU can becalculated as follows:

Rmin = MT U ∗ 8

Tcycle(3)

where Tcycle is in seconds and Rmin is bits/s. As seen inFig. 5, using very small Super Slot length will result in under-utilization because the minimum bandwidth is allocated butnot used. On the other hand, in the worst-case scenario aframe will have to wait up to Tcycle time before transmit-ting (ignoring the queuing delay). Additionally, the smallerthe Super Slot length used, the more the fragmented time isexpected.

In the case that time slots are used into the Super Slot,the time slot size can also affect the minimum allocation

requirement, since an ONU will have to occupy a full timeslot. Therefore, Tcycle is a parameter that has to be carefullytuned and introduces a trade-off between minimum allocationand mean packet delay.

5 Proposed DBA algorithm

As mentioned in Sect. 3, this work presents a midtermDBA scheme for EPON networks, called EMDBA (EPONmidterm DBA). The main concept is that the system collectsand stores status updates from the ONUs, while the DBAalgorithm is executed at regular intervals. Furthermore, amulti-service environment is considered where each servicehas a different priority. This algorithm is fairness based andtherefore tries to allocate the available resources in a fair wayboth between ONUs and service queues in each ONU. Twoapproaches exist when the DBA is applied per service on acommon channel:

– The first one is that the OLT has a committed bandwidthfigure and this should be distributed/allocated on all theservices. This way the underlying services are going tocompete for this amount since the bandwidth is shared.

– The second approach is based on a hierarchical DBAscheme, where the OLT algorithm allocates per service.It is reasonable to expect that a similar per-service alloca-tion has been performed on the higher (network) layer toassign bandwidth to the OLT. Thus, the OLT has limitedbandwidth for each service supported, and not a singletotal figure. In such a case, the services do not compete,and therefore, each one can reach its saturation pointindependently.

The EMDBA algorithm can operate in both modes. Settingthe per-service committed data rate to a very large value willmake the algorithm work the first way. The opposite will

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make it react the second way. This is going to be more clearin the pseudocode presented later.

Finally, as seen in Sect. 4, both incoming data rate andqueue status need to be taken into consideration (input to thealgorithm) in order to address DBA at regular intervals. Thus,EMDBA works in two stages on a per-service basis. Thegoal is to satisfy the incoming rate, covering the traffic that isgoing to arrive until the next interval (predictive behavior),and to ensure that the currently queued frames are going tobe transmitted in the next interval (reactive behavior).

5.1 Assumptions

The main assumptions that have been made in this work canderive logically based on the operation of the algorithm. Thefirst assumption is that there are no synchronization issuesand the technique mentioned in Sect. 4.3 is used as the under-lying MAC to avoid collisions.

The EMDBA algorithm presented in this work is bothqueue status and data rate-based. MPCP protocol does notsupport rate reports; thus, it is assumed that there is an over-lay protocol handling the signaling (REPORT and GATE)messages per service. In this work OLT has two tables inits memory: (1) an ONU table with all the LLIDs and (2)a per ONU/service table. The second one is updated by theREPORT messages of the overlay protocol and used as inputto the algorithm.

The priority queues used in this work are not sophisti-cated enough to perform AQM, but they support countingand reporting the incoming data rate, drops and length (inbytes). Additionally, they can be data rate limited, in ordernot to exceed the bandwidth allocated to them.

Finally, in most of the cases the ONU device is a bridgebetween the PON network and a copper 100Mbps EthernetLAN, since the 1Gbps line rate is shared between ONUs.Consequently, in order to utilize the full capacity, at least 10ONUs should be attached. In this work it is assumed that theONU’s access interface is not data rate nor medium (copperor fiber) limited, as long as it is an 802.3 Ethernet inter-face. On the one hand, this way there is no minimum num-ber of ONU requirement to fully utilize the PON network.On the other hand, possible bottleneck situations have to beaddressed from the OLT’s DBA algorithm.

5.2 Problem formulation

This work considers an EPON network that supports multipleONUs, so the basic notation used is the following: G forvariables referring to the OLT (Gateway) or totals, N thenumber of attached ONUs (nodes) indexed by i and S thenumber of provided services indexed by j . More detailednotation is listed in Table 2.

Table 2 Basic variable notation for the DBA algorithm

Variable Explanation

I DBA execution interval (in seconds)

Tcycle The Super Slot Length

s The time slot length, or step (in bits per second)

Ri, j The detected incoming rate of node i for service j

Li, j The maximum data rate limitation of node i for service j ,taken from configuration

Bremj The bandwidth remaining for service j

Qi, j Queue length (Bytes) of node i for service j

Ai, j The allocated bandwidth for node i , service j

N hmi The maximum hardware throughput supported from node i

R The minimum reserved bandwidth percent (%) for eachservice

Gcj The committed bandwidth to the OLT for service j

Ghm The maximum throughput of the OLT device

5.2.1 Fairness

As mentioned before, the EMDBA is a fairness-based DBAalgorithm. In scenario where the PON is congested, the ratioof allocation/request will be the same for every ONU andservice queue. Defining Brem

j the bandwidth remaining forservice j , fairness for the incoming data rate is defined asfollows:

Ari, j = Ri, j ∗

Bremj

∑Ni=0 Ri, j

(4)

and in the same way for queue length definition, where Bremqj

is in Bytes per Super Slot available for service j :

Aqi, j = Qi, j ∗

Bremqj

∑Ni=0 Qi, j

(5)

The final allocation Ai, j is going to be the summation ofthese two values after the Aq

i, j is converted from Bytes tobits per second (rate). Since the allocated bytes have to betransmitted into the next execution interval, the conversionis as follows:

Aqri, j =

Aqi, j

I(6)

5.2.2 Rate constrains for SLAs

As mentioned in Sect. 5.1, no data rate limitation is assumedfor the access side interface of the ONUs. Therefore, dif-ferent types of ONUs may be present in the same EPONnetwork, and they include the N hm

i value in the REPORTmessages, making the EMDBA algorithm generic enough tosupport a range of scenarios. Additionally, this parameter canbe overridden from the configuration on the OLT, artificiallylimiting the nodes upstream. This behavior makes EMDBA

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Photon Netw Commun (2013) 25:120–134 129

able to support SLAs on inter-ONU allocation, constrainingthe decision of the fairness Eqs. 4 and 5.

In the same way, the Li, j is defined in order to limit intra-ONU allocation between service queues. In any case the fol-lowing constraints should be enforced:

Ai, j ≤ N hmi ≤

S∑

j=0

Li, j

5.3 EMDBA

The EMDBA is a two-stage algorithm. One stage takes intoconsideration the incoming rate, and the other stage triesto relax the queues. The logic/algorithm of incoming ratestage is presented in Algorithm 1. The functions used in thealgorithm are as follows:

– MIN: The minimum between two numbers,– ceilToStep: It will round up its argument so that it

becomes multiple of the time slot size (arg%s = 0) and– floorToStep: The same as above but rounding down.

The full algorithm in its final form applies the presentedlogic, for both rate and queue length, before moving on to thenext-priority service queue. As shown in the next section, thisis the most efficient way, as compared to two other differentapproaches tested.

6 Simulation and performance evaluation

Simulation results, focused mainly on the application of theEMDBA algorithm and its correct operation, are presentedin this section. OMNet++ and its INET/ MANET frameworkare used for all the simulations performed. The experimentsperformed fall into two categories. First is on how the incom-ing rate and queue length can be combined and used from thealgorithm. The second set focuses on validating the algorithmin multi-service scenarios and priority queues.

6.1 Algorithm application order

As mentioned before, the logic presented in Sect. 5.3 canbe applied in different ways in the algorithm implementa-tion. The first approach was to allocate bandwidth based onthe queue status of the ONUs for all the services and all thenodes. On the second stage, the algorithm was consideringthe incoming rate requirements. The exact opposite proce-dure can also be applied, satisfying first the incoming rateand then the queue status. Finally, the last alternative is tosatisfy both incoming rate and queue lengths for each ser-vice, before moving on to the next one (priority based).

Algorithm 1 EMDBA single pass1: zeroAllAllocations()2: BT ←MIN(Ghm ,

∑Sj=0 Gc

j )3:4: // For each service, starting with the higher priority5: for j ∈ S do6: Brem

j ←MIN(BT − R ∗ BT ∗ (S − j − 1), Gcomj )

7: R j ←∑Ni=0 Ri, j

8:9: cong← false10: if R j > Srem

j then11: cong← true12: end if13:14: for i ∈ N do15: // Node Limit16: Brem

i ← N hmi −

∑Sj=0 Ai, j

17: Atmpi, j = Ri, j

18:19: if ! cong then20: Atmp

i, j = ceilToStep(Ri, j )21: // Check Min. Allocation Requirement22: if Atmp

i, j < Rmin then

23: Atmpi, j ← Rmin

24: end if25: else26: // Service is Congested - Fair Allocation27: Atmp

i, j ← Ri, j ∗ Bremj /R j

28: Atmpi, j = floorToStep(Atmp

i, j )29: end if30:31: // Check Constraints32: N max

i ←MIN(Bremi , Li, j )

33: if Atmpi, j > N max

i then

34: Atmpi, j ← N max

i35: end if36:37: // Store Allocation38: Ai, j ← Atmp

i, j39: BT ← BT − Ai, j40: end for41: end for

6.1.1 Parameters

The network used for the simulations is an EPON networkwith 8 ONUs, each one being the back-haul of a WiMaxBase Station (BS). Each ONU is connected to a BS using anEthernet interface, following the “Independent Architecture”described in [41]. Twenty-four Service Stations (SS) are usedto generate traffic in three services: voice (real-time), video(streaming) and data (background). Furthermore, the SS areimplementing random way point mobility and have the abil-ity to handover to different BSs. More detailed parametersare presented in Table 3.

This experiment is focusing on application layer end-to-end delay; therefore, superficially large queues are used. Thisreflects the impact of the DBA algorithm to the frame/packet

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130 Photon Netw Commun (2013) 25:120–134

Table 3 Detailed simulation parameters

Parameter Value

WiMax

Symbols/sub-carrier 48

Sub-carriers 60

Uplink/downlink 50–50 (=1,440 symbols)

Bytes/symbol 12 (=27.648 Mbps each direction)

FrameLength 5 ms

EPON

Line rate 1 Gbps

Super slot (Tcycle) 5 ms

OLT-ONU distance 15 Km

Fiber delay 5µ/km

DBA exec. interval (I ) 200 ms

Report interval 100 ms

time slot (s) Ignored (Set to very small value)

Min. reserved (R) 0.01 (1 %)

Traffic per flow

Voice 96 Kbps, CBR, pkt size:uniform(180 B,280 B)

Video 1.2 Mbps, VBR up to 2 Mbps, pkt size:uniform (200 B,500 B)

Data 1 Mbps, CBR, pkt size: 1,300 B

Global

Service Priorities Voice:0.5, Video:0.4, Data:0.1

Simulation Time 360 and 10 s

Number of SS 24

Queue size 1,000 frames

delay rather than the drop rate of the queues. Specifically, thevideo service is studied because the voice service will alwaysbe satisfied (high-priority service) and the data service is usedas background traffic to congest the network. Furthermore,the network load is defined as follows:

L = G

A ∗ 0.98− V

where G is the video-generated traffic, A is the total avail-able bandwidth, and V is the minimum required allocationfor each ONU for the higher-priority service. Since there isR(%) reserved bandwidth for lower-priority services, 2 % arereserved for data and broadcast services. In order to simulatecongestion, the OLT is data rate limited in each run, leavingthe algorithm with less available bandwidth (A) to distribute.

In all the experiments performed, statistics are gatheredafter the first 5 s. This time is allowed for the network to bein a stable condition. Operations taking place in this inter-val include Ethernet auto-negotiation, ONU registration andWiMax flow creations for each SS. The traffic generators areintroduced at t = 1 s.

10-3

10-2

10-1

100

0 20 40 60 80 100 120

Del

ay(s

)

Load (%)

Delay vs. LoadQ Video

QR Video

Fig. 6 Queue-based only versus Queue for all services and then Rate

6.1.2 Results

As mentioned before, the first approach was to satisfy thequeue status for all the services and nodes and then run thesecond stage of the algorithm to satisfy the incoming raterequests (QR). This method is compared to one satisfyingthe queue status (Q) only in Fig. 6. As shown, QR achieves30ms less mean packet delay compared to the simple queueallocation for light-load and both have the same performancefor higher load. This happens because at high load the avail-able bandwidth is fully distributed in the first stage of thealgorithm, leaving no bandwidth for the second stage andthus making both of them work in the same manner.

The same behavior is observed when the stages are appliedin the opposite order, satisfying the incoming rate first. Thisis shown in Fig. 7 where it can be seen that RQ achieves 80msless mean packet delay for light-loaded network. This differ-ence is based on the fact that the rate-based only approachignores the current queue levels. Additionally, in this figure,a second experiment has been performed, for 10 s runtime.This is demonstrating the rate-based problem discussed inSect. 4.2, where spiky (VBR) traffic is causing the queuelevels to be constantly increasing over time with very smallrate. The rate-based only approach (R) has mean packetdelay less than 100 ms for a short period (10 s—Fig. 7b) andabove 100 ms for longer simulation time (Fig. 7a). It is alsoshown that the RQ approach is not affected by the simulationtime.

As seen from the simulation results, both QR and RQapproaches improve the simple rate or queue-based ones,but still fail to achieve acceptable performance in terms ofpacket delay. The last approach is to satisfy both queue statusand incoming rate for each service before moving to the nextone. As seen in Fig. 8, this method outperforms both previ-ous ones. It avoids the queue length instabilities and the rate

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Photon Netw Commun (2013) 25:120–134 131

10-3

10-2

10-1

100

0 20 40 60 80 100 120

Del

ay(s

)

Load (%)

Delay vs. LoadR Video

RQ Video

10-3

10-2

10-1

100

0 20 40 60 80 100

Del

ay(s

)

Load (%)

Delay vs. LoadR Video

RQ Video

(a)

(b)

Fig. 7 Rate-based only versus Rate for all services and then Queue.a Simulation time: 360 s, b simulation time: 10 s

10-3

10-2

10-1

100

0 20 40 60 80 100

Del

ay(s

)

Load (%)

Delay vs. LoadQR VideoRQ Video

EMDBA Video

Fig. 8 Previous approaches versus allocation both rate- and queue-based per service

2.2e+07

2.3e+07

2.4e+07

2.5e+07

2.6e+07

2.7e+07

0 20 40 60 80 100

Load (%)

Throughput vs. Load

QR VideoRQ Video

EMDBA Video

20

30

40

50

60

70

80

90

20 40 60 80 100 120 140 160

Util

izat

ion(

%)

Load (%)

Utilization vs. LoadQRRQ

EMDBA

(a)

(b)

Thr

ough

put (

bps)

Fig. 9 Video service throughput and network utilization. a Video ser-vice throughput, b network utilization

problems mentioned in Sect. 4. Furthermore, the EMDBAachieves (constantly) less than 25 ms end-to-end mean packetdelay until the service saturation point.

Additionally, it is important for any DBA algorithm not tounderutilize the priority services and the network. EMDBAperformance is satisfactory in terms of both throughput andPON utilization. This is shown in Fig. 9 where the throughputachieved for the video service constant until 80 % load. Thisis the point the service reaches a congested state, and thegenerated traffic cannot be fully served.

At the same time the total network utilization, includingall services, is shown in 9. The network utilization is mea-sured at the optical interface of the OLT as the total number ofbits received vs. the data rate limit applied. The 20 % under-utilization appears due to the over-allocation performed forvoice service (as discussed in Sect. 4.3.1) in combination withthe very low data rate limit used in the simulation. This alsoincludes guard times between ONUs, Ethernet Inter-FrameGap (IFG) and fragmented time slots.

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132 Photon Netw Commun (2013) 25:120–134

10-3

10-2

10-1

100

101

102

103

0 20 40 60 80 100 120 140 160

Del

ay(s

)

Load (%)

Delay vs. Load

EMDBA DataEMDBA VoiceEMDBA Video

Fig. 10 End-to-end delay for all the services using EMDBA algorithm

6.2 Traffic differentiation and QoS

An important feature of the proposed algorithm is service pri-oritization and traffic differentiation. The end-to-end delayfor all the services is presented in Fig. 10, which proves thecorrect operation of the EMDBA algorithm. Service priori-ties are taken into consideration from the algorithm, and atthe same time R % is reserved for each low-priority servicequeue, avoiding blockage of BE (data in this case) service.

As shown in Fig. 10, high-priority service (voice) delayis constantly less than 8 ms throughout both WiMax andEPON networks. This is more than satisfactory based on ITUG.114 [42] defining 75 ms end-to-end one-way delay as theacceptable for most applications. In addition, the video ser-vice delay achieved by EMDBA is 25 ms up to the saturationpoint, which is also satisfactory.

The same service differentiation ability can be seen, interms of packet drop rate, in Fig. 11. As probably expected,since the voice service bandwidth requirements are alwayssatisfied, EMDBA achieves 1 % packet loss throughout allthe simulation runs. The same behavior is observed in thevideo service, until its saturation point where the networkstarts discarding frames.

As seen from the previous two figures, EMDBA perfor-mance is acceptable for most video and voice applications, interms of end-to-end packet delay and drop rate. As mentionedbefore, EMDBA is based on regular interval rather than beingpolling-based. It is expected that polling-based systems willhave less response time. This means that in bursty trafficenvironments they will outperform the EMDBA in terms ofQoS, since the former requires at least I (exec. interval) msin order to redistribute bandwidth.

On the other hand, using EPON as an aggregation layer,it is expected that EMDBA and polling approaches willhave similar performance due to smother traffic received at

0

20

40

60

80

100

20 40 60 80 100 120 140 160

Pac

ket L

oss

(%)

Load (%)

Packet Loss vs. Load

EMDBA DataEMDBA VoiceEMDBA Video

Fig. 11 Drop rate for all the services using EMDBA algorithm

0.0001

0.001

0.01

0.1

1

10

100

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Ove

rHea

d (%

)

Number of ONUs

IPACT/MTTSD

EMDBA-100msEMDBA - 40ms

Fig. 12 Signaling overhead for 1G PONs

this layer. Polling-based approaches will still be more reac-tive, especially in the cases of call/session initiation, but theEMDBA benefits from the use of less bandwidth for signal-ing which is discussed in the next section.

6.3 Signaling overhead comparison

In this section, a brief comparison in terms of required signal-ing overhead is presented, between two polling approachesand the proposed EMDBA. From Sect. 2, two polling mech-anisms have been selected for this comparison.

The first one is IPACT. IPACT, as described in [9], sendsone MPCP GATE to each ONU. The REPORT message ispiggybacked to the data transmitted on the upstream. Sim-ilarly, Multi-Thread (MT) approaches work the same way,with each thread being responsible for polling all the ONUsin a single cycle. The second mostly used approach is thetwo-stage DBA (TSD) where a virtual cycle is used right afterthe “normal” polling cycle. In these cases the virtual cycleextends up to double (depending on the implementation) the

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Photon Netw Commun (2013) 25:120–134 133

normal cycle by polling all the ONUs for a second time.Therefore, two GATE messages are used in each full cycle.

The overhead required for each approach is presented inFig. 12 in comparison with the EMDBA using I = 40 msand 100 ms. The MPCP packet size used is the minimum 84Bytes (one GRANT per ONU—see Sect. 3), 1Gbps line rateis considered, and Tcycle is set to 4.8 ms. It is shown that theproposed approach requires a significantly less signaling (asprobably expected), making it possible for more ONUs to beattached to the network.

7 Conclusions and future work

This work started with an explanation of the basic terms inDBA, focusing mainly on EPON networks. The major lit-erature in this area was presented. The need for a midtermDBA algorithm/scheme was proven based on the standard-ization efforts and research activities on EPON networks,taking into consideration their fast evolution. A number ofchallenges, on performing midterm DBA, were identifiedand discussed along with proposed solutions. Furthermore,the proposed EMDBA algorithm and its principles were pre-sented. A number of simulations using OMNet++ have beenperformed, confirming the correct operation of the algorithm.The simulations prove that both rate and queue status shouldbe reported from the ONUs in order to accurately reflect thenetwork status.

As seen in the related work section, this midterm approachis fairly new to EPONs; therefore, future work should focuson further optimizing the algorithm presented. Addition-ally, a test bed-based implementation, using data rate–limitedqueues, can be performed to confirm the effectiveness of theEMDBA algorithm on real-life networks. Finally, since thiswork focused on presenting the new midterm DBA approachand its benefits in terms of signaling overhead, future workwill compare this algorithm with polling-based mechanismsin terms of QoS performance.

Acknowledgments This work is partially sponsored by EPSRC soci-ety and University of Essex.

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Author Biographies

Andreas Bontozoglou received the B.S.degree from the Department of Informa-tion Technology of T.E.I., Thessaloniki, GR,in 2008 and the MS degree in Telecom-munications and Information Systems fromUniversity of Essex, UK, in 2009. He iscurrently working toward the PhD degreeat the Department of Computer Scienceand Electronic Engineering of University

of Essex. His research interests include fixed-mobile network con-vergence, resource management, quality of service and networkperformance measurement.

Kun Yang received his PhD from theDepartment of Electronic and ElectricalEngineering of University College London(UCL), UK. He is currently a full profes-sor in the School of Computer Science andElectronic Engineering, University of Essex,UK. Before joining in University of Essexat 2003, he worked at UCL on several Euro-pean Union (EU) research projects for several

years. His main research interests include heterogeneous wireless net-works, fixed-mobile convergence, pervasive service engineering, futureInternet technology and network virtualization, and cloud computing.He manages research projects funded by various sources such as UKEPSRC, EU FP7 and industries. He has published 50+ journal papers.He serves on the editorial boards of both IEEE and non-IEEE journals.He is a Senior Member of IEEE and a Fellow of IET.

Ken Guild is currently founder and CTO forSmart Networked Environments Ltd and aVisiting Fellow at the University of Essex,UK. From September 2003 to June 2011, hewas a Reader within the School of ComputerScience and Electronic Engineering at theUniversity of Essex. As head of the NetworkConvergence Lab (NCL), he participated andmanaged numerous national and European

research projects and was responsible for a number of graduate andpostgraduate courses in the area of communications and networking.Prior to this, he was Director of Technology for Network Architec-ture at Marconi, Germany, and Director of Technology and co-founderof a UK startup, ilotron, developing all-optical networking systems.Between 1993 and 1997, he was a senior development engineer in theadvanced products division at Alcatel Networks, UK. He holds a PhDin all-optical networking from the University of Essex and an M.Eng.degree in electronic and electrical engineering from the University ofSurrey. His current research interests include future Internet architec-tures, fixed-mobile convergence, traffic engineering and converged net-works and services. He has published over 60 papers and holds 12patents.

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