Intelligent Dynamic Bandwidth Allocation Algorithm in Upstream EPONs

148 J. OPT. COMMUN. NETW./VOL. 2, NO. 3 /MARCH 2010 Radzi et al.

Intelligent Dynamic BandwidthAllocation Algorithm in Upstream

EPONsNurul Asyikin Mohd Radzi, Norashidah Md. Din, Mohammed Hayder Al-Mansoori,

Intan Shafinaz Mustafa, and Sajaa Kh. Sadon

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Abstract—An upstream access scheme for Ethernetpassive optical networks, called the intelligent fuzzy-logic-based dynamic bandwidth allocation algorithm(IFLDBA) has been proposed. The algorithm providesthe allotment of bandwidth between optical networkunits (ONUs) and within ONUs. Fuzzy logic is used toimprove the bandwidth allocation within each ONU.We compare the IFLDBA algorithm with the existingbroadcast polling algorithm. The results show thatIFLDBA has bandwidth utilization of up to 21% betterand lower delay than the broadcast polling algo-rithm.

Index Terms—Ethernet passive optical network;Fuzzy logic; Dynamic bandwidth allocation; Fairness.

I. INTRODUCTION

M any types of broadband access network are of-fered nowadays. Among the broadband tech-

nologies that are widely deployed for access networksare digital subscriber line (DSL) and cable modem(CM). The coming emerging services such as two-wayvideo conference, Internet Protocol (IP) telephony,video on demand (VOD), and interactive gaming willdemand a huge amount of bandwidth. The currentbroadband solution is challenged to provide enoughbandwidth for these services. Therefore, the passiveoptical network (PON) has been introduced at the ac-cess network domain to support full-service broad-band access networks.

PONs can be subdivided into asynchronous transfermode PON (APON), broadband PON (BPON), gigabit

Manuscript received July 31, 2009; revised January 20, 2010; ac-cepted January 29, 2010; published February 26, 2010 �Doc. ID115023�.

N. A. Radzi ([email protected]), M. D. Norashidah([email protected]), M. H. Al-Mansoori ([email protected]), S. M. Intan, and K. S. Sajaa are with the Center forCommunications Service Convergence Technologies, Department ofElectronics and Communication, College of Engineering, UniversitiTenaga Nasional, 43009 Kajang, Selangor, Malaysia.

Digital Object Identifier 10.1364/JOCN.2.000148

1943-0620/10/030148-11/$15.00 ©

ON (GPON), Ethernet PON (EPON), and 10 Gbitthernet PON (10GEPON). Recently, EPON has be-ome an emerging access network technology thatrovides a low-cost method of deploying optical accessines between a carrier’s central office and a customerite [1].

EPON is a point-to-multipoint optical network,here the term passive in “Ethernet passive opticaletwork” means that it employs only passive opticalomponents in the transmission path from source toestination. The deployment topology of EPON canake different shapes such as bus, ring, and tree [2].owever, the most popular EPON topology is the tree-ased architecture, where transmission occurs be-ween an optical line terminal (OLT) and optical net-ork units (ONUs) connected to each other by means

f a 1:N optical splitter or coupler [3].

Because of the directional properties of the opticalplitter or coupler, the OLT is able to broadcast datao all ONUs in the downstream direction. In the up-tream direction, ONUs are able to communicate onlyith the OLT; they cannot communicate directly withne another [4]. Since in the upstream direction allNUs share the transmission medium, several ap-roaches, such as wavelength division multiplexingWDM) and time division multiplexing (TDM), can besed. TDM is currently considered as more cost-ffective than WDM [5].

In TDM, upstream data transmission can be sched-led by using either static bandwidth allocation (SBA)r dynamic bandwidth allocation (DBA). In SBA, onceandwidth is assigned to a subscriber, it will be un-vailable to other subscribers on the network. There-ore, a lot of unutilized bandwidth will be wasted. Tovercome this limitation, DBA has been introduced,hich has the ability to quickly reapportion band-idth on EPONs based on current traffic require-ents [6]. The DBA scheme can provide more efficient

andwidth allocation for each ONU to share networkesources and offer better quality of service (QoS) fornd users.

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Radzi et al. VOL. 2, NO. 3 /MARCH 2010/J. OPT. COMMUN. NETW. 149

Recently, many DBA algorithms have been pre-sented. Most of the proposed DBA algorithms dis-cussed the bandwidth allocation only between differ-ent ONUs (inter-ONU) [3,7–13]. However, in thiswork, we address the issue of allocating bandwidthnot only between different ONUs, but also within eachONU (intra-ONU). More specifically, this paper pro-poses for the first time, to the best of our knowledge, anovel DBA algorithm that we call the intelligentfuzzy-logic DBA (IFLDBA) algorithm, which can allo-cate the bandwidth within each ONU (intra-ONU) toeach traffic class, expedited forwarding (EF), assuredforwarding (AF), and best effort (BE), by using fuzzylogic.

To achieve DBA, the multipoint control protocol(MPCP) is used. The two MPCP control messagesused in IFLDBA are REPORT and GATE. The RE-PORT message informs the OLT about the state ofqueues at the ONU. When receiving a REPORT mes-sage, the OLT relies on the bandwidth allocation algo-rithm for determining the upstream transmissionschedule. After the execution of the algorithm, theGATE message is used to issue transmission length.The throughput and delay will be improved by usingthis algorithm, while the fairness is being maintained.We conduct detailed simulation experiments usingMATLAB to study the performance of the proposed al-gorithm and validate its effectiveness. The resultsshow that IFLDBA has bandwidth utilization of up to21% better and achieves lower delay than the broad-cast polling algorithm.

The remainder of this paper is organized as follows.In Section II, we review the related work. In SectionIII, we introduce our proposed IFLDBA algorithmwith theoretical explanations. Our simulation setupand examination of the impact of traffic load with thethroughput and delay is described in Section IV. Wesummarize our conclusions in Section V.

II. RELATED WORK

A. Critical Review

To date, a lot of DBA schemes [7–23] have been de-veloped over the years to cope with the challenges ofhigh bandwidth utilization and QoS provisioning. Anoverview of some of the algorithms can be found in[14]. Among the most famous algorithms is inter-leaved polling with adaptive cycle time (IPACT) [9].With IPACT [9,10], idle time is eliminated by sendinggrant messages for succeeding ONUs while receivingtransmission from previous granted ONUs. It re-quires OLT to poll ONUs in a round robin fashion anddynamically assign them before transmission. How-ever, IPACT is not suitable for delay, jitter, and sensi-tive services or service level agreements (SLAs) [15].

esides that, IPACT does not consider the multiser-ice needs of subscribers [24,25]. IPACT with MPCP,ntroduced by Zahr and Gagnaire [16], can supporthree priority categories, i.e., EF, AF, and BE, accord-ng to the two-stage open Jackson’s queuing network.

Excess bandwidth distribution for EPONs wasriginally proposed in [17] as an improvement overhe limited allocation approach of IPACT. The advan-age of this algorithm is that it can overcome the lightoad punishment problem while achieving higher uti-ization due to the allocation of the excessive band-idth resulting from lightly loaded ONUs to highly

oaded ONUs. However, with this algorithm, ONUsan receive more bandwidth than what is requested.hus, Bai et al. [18] proposes a weight-based DBAcheme called weighted-DBA (W-DBA) in which thexcess bandwidth from lightly loaded ONUs can beistributed appropriately to highly loaded ONUs ac-ording to the weight of the buffer. Therefore, the fair-ess index can be maintained at the highest value.he detailed quantitative comparison between IPACT-

imited and excess bandwidth distribution techniquess presented in [11,12]. The priority categories in bothlgorithms are according to the arrival of packets; fornstance, packets that arrive before the time sendinghe REPORT are given high priority for transmission.his will cause a problem for delay-sensitive classes of

raffic such as EF. Therefore, other alternatives to al-ocate bandwidth for intra-ONU are proposed in18–23], where the services are classified into thehree priority categories of EF, AF, and BE. However,ll of these algorithms [18–23] are based on hierarchi-al scheduling that does not support global prioritynd fairness between multiple queues in a singlePON. Recently, Chen et al. [26] proposed a hierarchi-

al algorithm that can support global priority inGbit/s and 10 Gbits/s EPON. Global priority is sup-

orted in this algorithm by letting the OLT be inharge of the priorities while ONUs handle the up-tream traffic destined to different service providers.

In [15], a broadcast polling (BP) algorithm that sup-orts inter-ONU bandwidth allocation was proposed.ith the BP algorithm, the OLT has all known ONU

andwidth requirements before bandwidth is allo-ated to the ONUs in every cycle. The algorithm di-ides the ONUs into three classes denoted classes 1, 2,nd 3. However, the classifications are not specified inetail. Apart from that, there is no bandwidth limita-ion for class 1; thus light load punishment is mostikely to occur with the BP algorithm.

Therefore, to resolve the above-mentioned problemsnd enhance the bandwidth utilization, we propose aew enhanced DBA with a fairness scheme, employ-

ng a fuzzy-logic approach. The algorithm is called IF-DBA, and the main focus of the algorithm is to divideandwidth fairly between different ONUs. We will

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distinguish this advantage by comparing our new al-gorithm with the BP algorithm. The reason for com-paring IFLDBA with BP is that IFLDBA can supportthe same number of classes and sophisticated SLA asthe BP algorithm. For comparison, we have used threetypes of priorities and the same requested bandwidthfor all three types of traffic in both the IFLDBA andBP DBA algorithms. All other parameters that havebeen used, such as maximum transfer window, maxi-mum cycle time, and guard time are also the same.

B. BP Algorithm

In the BP algorithm [15], the OLT has all knownONU bandwidth requirements before bandwidth is al-located to ONUs in every cycle. With this characteris-tic, the BP scheme is able to support a sophisticatedbandwidth allocation algorithm with the abilities tosupport the SLA. The maximum window size, Wmax,each ONU can get if the OLT allocates the total band-width to each ONU equally according to maximumcycle time, Tmax, can be obtained by using the follow-ing formula [9,15]:

Wmax = �Tmax

N− Bg�RT, �1�

where N is the number of ONUs, Bg is the guard time,and RT is the total upstream bandwidth. The defini-tion of Wmin is similar to that of Wmax as shown in Eq.(2) below, where Tmin is the minimum value that canensure that the local time is bigger than the initialtime [15]:

Wmin = �Tmin

N− Bg�RT. �2�

After receiving all the REPORT messages, the OLTfirst calculates the total requested bandwidth �Ri� ofall ONUs. If Ri�NWmin, the OLT will allocate eachONU bandwidth equal to its request. A certain win-dow size will be added by the OLT to the ONUs inturn until all the surplus bandwidth, NWmin−Ri, isused up. However, if Ri�NWmin, the OLT will first al-locate the bandwidth to the ONU of class 1. The ONUof class 1 will get the window size equal to its requestby using the following formula [15]:

W1 = R1, �3�

where Ri is the requested window size of class i. Then,the OLT allocates bandwidth to the ONUs of class 2according to the following formula [15]:

W2 = �R2 R2 � NWi,min

1.5Wmax R2 � NWi,min�. �4�

After allocating the bandwidth for classes 1 and 2, theOLT calculates the bandwidth available after allot-

ent to class 1 and class 2. If it is less than the re-uested bandwidth of class 3, the ONUs of class 3 willet the window size equal to its request. Otherwise,he allotment will follow the formula of class 3 [15]:

W3 = �R3 R3 � NWi,min

NWmax − Gc1 − Gc2

Nc3R3 � NWi,min�, �5�

here Gci is the total window size granted to theNUs that belong to class i and Nci is the number ofNUs in class i. For comparison later, we assumed

hat class 1 is for EF, class 2 for AF, and class 3 for BEraffic.

III. PROPOSED DBA ALGORITHM

. Allocation for Inter- and Intra-ONU

In this section, we present a new enhanced intelli-ent DBA scheme using fuzzy logic that we call the IF-DBA algorithm. The algorithm supports priorityueues and fairness among ONUs over EPON. Prior-ty queues are important, since we are dealing withoS that involves the allotment of voice, video, andata services. Without priority queues, all types ofervices will be served equally, which will create vari-us kinds of problems, since voice is sensitive to delay.airness is important so that bandwidth will be di-ided to every ONU equally.

The proposed IFLDBA algorithm uses a hierarchi-al scheduling, in which a top-level scheduler in theLT assigns an aggregated slot to an ONU, and this

lot is further subdivided between multiple queues bylow-level scheduler in the ONU. In the top-level

cheduler, the allocation for inter-ONU is done by us-ng an enhancement of the limited-IPACT algorithm17], where the excessive bandwidth is being ex-loited. On the other hand, for the low-level scheduler,he allocation for intra-ONU is using a fuzzy-logicegulator to allocate the bandwidth to each queue inhe ONU. The IFLDBA algorithm is explained in de-ail below. We shall start with the allocation for inter-NU followed by the allocation for intra-ONU.

To prevent upstream channel monopolization by oneNU with high data volume, Wmax is assigned as a

imitation to every ONU. The choice of specific valuesf Wmax determines the Tmax under heavy load and itan be obtained by using Eq. (1). Making Tmax tooarge will result in an increased delay for all the pack-ts, including high-priority packets. Making Tmax toomall will result in more bandwidth being wasted byuard times. In addition to Tmax, Wmax also deter-ines the guaranteed bandwidth available to ONU i.et Bmin denote the minimum guaranteed bandwidthf ONU i, which is given by the following equation [9]:

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Bmin =Wmax

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This means that the ONU is guaranteed to be ableto send Wmax bytes in at most Tmax time. An ONU willbe restricted to its guaranteed bandwidth only if allother ONUs in the system use their available band-width. However, if one ONU requested less band-width, it will be granted a shorter transmission win-dow, thus making the cycle time shorter. When onlyone ONU has data to send, the bandwidth available tothat ONU will be the maximum guaranteed band-width, Bmax [11]:

Bmax =Wmax

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RT

. �7�

The service discipline that will be used in this algo-rithm is limited IPACT. This is because with limitedIPACT, the cycle time is variable, but it will not sur-pass a certain limit. It also shows some propertiessimilar to fixed service and others similar to gated ser-vice [7]. Service discipline is a way for the OLT to de-termine the granted window size for ONU i dependingon the requested window. However, because of thebursty nature of Ethernet traffic [27], some ONUsmight have less traffic to transmit while other ONUsrequire more than the Bmin in limited service. Thissituation results in a total excessive bandwidth that isnot exploited under the limited service. Thus, to fullyutilize the bandwidth, the excess bandwidth obtainedfrom the lightly loaded ONUs will be distributed tohighly loaded ONUs according to the formula in [17]:

Bi = �Ri Ri � Bi,min

Ri + Bi,excess Ri � Bi,min� , �8�

Bi,excess =Ri

k�K

Rk

Btotal,excess, �9�

Btotal,excess = l�M

�Bl,min − Rl�, �10�

where Btotal,excess is the total excessive bandwidthsaved by underloaded ONU nodes. Bi,excess is the cor-responding share of the total excessive bandwidth al-located to overloaded ONU i. K and M are the set ofoverloaded and underloaded ONU nodes, respectively.

In the intra-ONU IFLDBA algorithm, the ONU isdivided into three priority queues; high priority, me-dium priority, and low priority. The high priority is EFbandwidth, which supports voice traffic that requiresbounded end-to-end delay and jitter specifications.Medium priority is the AF bandwidth that supports

ideo traffic that is not delay sensitive but requiresandwidth guarantees. Finally, the low priority is BEandwidth that supports data traffic and is not sensi-ive to end-to-end delay or jitter.

For intra-ONU allocation, the bandwidth allotmentill be done by using a fuzzy-logic regulator. Fuzzy

ogic is used because of its intelligent capability to don approximation that can reflect the flexibility of hu-an judgment in order to improve the bandwidth uti-

ization. By using buffer occupancy directly for the in-ernal queues, bandwidth is always granted up toome limitation value whenever the requested band-idth is higher than its limitation [7–10,17–19]. How-ver, by using fuzzy logic, bandwidth is being grantedccording to the requested bandwidth from all threeypes of traffic. For example if requested EF and AFraffics are higher than the limitation while BE traffics lower, rather than putting some limitation on EFnd AF traffics, as normally happens when the bufferccupancy has been used, fuzzy logic can give moreandwidth to EF and AF traffics and less to BE traffic,ince the decision is done based on human reasoning.his makes the algorithm more dynamic. Therefore,

uzzy logic can utilize the bandwidth better as com-ared with the use of buffer occupancy for the internalueues.

The flowchart for bandwidth allocation for intra-NU is shown in Fig. 1. First, part of the bandwidthill be allocated to EF traffic. Then, the remainingandwidth will be allocated to AF, then BE traffic. Theuzzy-logic regulator is used to allocate bandwidth inach ONU and is triggered when there is contentionor bandwidth between classes. The regulator com-rises three input parameters and one output param-ter as shown in Fig. 2. The input parameters are re-uested EF bandwidth (rEF), requested AFandwidth (rAF), and requested BE bandwidth (rBE).he output value is the allocation decision whether todjust EF, adjust AF, or adjust BE.

The universe of parameters for the fuzzy regulators represented by the following linguistic variables:

X � {EF bandwidth, AF bandwidth, BE bandwidth,Allocation decision}

And the linguistic values are given below:S (EF bandwidth) � {L, H}S (AF bandwidth) � {L, H}S (BE bandwidth) � {L, H}S (Allocation decision) � {Adjust EF, Adjust AF, Ad-

just BE}here L is low and H is high.

The input membership functions are shown in Fig.. The membership functions used are trapezoidalunctions, which have been widely used in fuzzy con-rol design for their simplicity and also have beenound suitable for real-time operations [28]. The trap-

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ezoidal functions are used to represent the linguistictruth values, i.e., low and high.

The fuzzy rules are shown in Table I below. Thereare seven rules that relate the three inputs with thefuzzy output. All the rules use AND connectives. Theconstruction of the rules is based on logical reasoningof how the system should behave in allocating band-width. Rules 2 and 3 are the classes where rEF band-width is low and rAF bandwidth is high and rBEbandwidth is either low or high. For these rules, theaction is to adjust AF bandwidth. Adjusting AF band-width is done by first allocating bandwidth for EF,since EF is delay sensitive. If requested EF bandwidthis less than the limitation, the ONU will receive what-ever it is requesting. Otherwise, it will be restricted toits limitation bandwidth.

Then, we calculate the sum of requested bandwidthfor both AF and BE. If the summation of both trafficclasses is less than the available bandwidth after al-locating bandwidth to EF, the ONU will receive all therequested AF and BE bandwidth. Otherwise, the al-lotment will be done according to the SLA as in thefollowing formula:

Fig. 1. Flowchart for

Bi,n+1AF = Bavail Si

AF

SiAF + Si

BE�. �11�

As for BE bandwidth, if requested bandwidth is lesshan the remaining bandwidth, the ONU will receivell the requested BE bandwidth. Otherwise, the ONUill receive up to the amount of the bandwidth re-aining after allocation to EF and AF bandwidth.

On the other hand, rules 4 and 5 are for the caseshen rEF bandwidth is high and rAF bandwidth is

ow and rBE bandwidth is either high or low. Rules 6nd 7 are when rEF and rAF are high but rBE is ei-

Fig. 2. Fuzzy logic regulator block diagram.

ocation within ONU.

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ther low or high. For these rules, the allocation deci-sion is to adjust EF bandwidth. EF bandwidth will beadjusted according to the following formula:

Bi,n+1EF = Rtotal Si

EF

SiEF + Si

AF�. �12�

Then, if rAF is less than the limitation, the ONUwill receive all the requested bandwidth. Otherwise itwill follow Eq. (10). Again, for BE, if requested band-width is less than the remaining bandwidth, the ONUwill receive all the requested bandwidth. Otherwise,the ONU will receive up to the amount of the band-width remaining after allocation to EF and AF band-width.

Rule 1 is when rEF bandwidth is low, rAF band-width is low, and rBE bandwidth is high. For this in-stance, BE bandwidth will be adjusted by first allocat-ing bandwidth for EF, then AF. The ONU will receiveall the rEF and rAF bandwidth. For BE bandwidth, ifrBE is less than the remaining bandwidth, the ONUwill receive all the requested bandwidth. Otherwise,the ONU will receive up to the amount of the band-width remaining after allocation to EF and AF band-width.

One other possible rule is when all the inputs arelow. This rule has been eliminated, since under this

Fig. 3. (Color online) Membership function for (a) rEF, (b) rAF, and(c) rBE.

ondition the bandwidth allocation decision will beulfilled, and the process flow would not enter theuzzy regulator. The same set of rules would be trig-ered every time the fuzzy regulator is invoked,hereby an appropriate consequence will be executed.

To obtain a single crisp solution for the output vari-ble, the Sugeno inference process is used, since it isomputationally efficient and works well with optimi-ation and adaptive techniques. It also has guaran-eed continuity of the output surface and it is welluited to mathematical analysis [28]. Figure 4 showshe output singletons based on Sugeno’s method,here 1 is to adjust EF bandwidth, 2 is to adjust AFandwidth, and 3 to adjust BE bandwidth. For de-uzzification, the weighted average (WA) of the single-ons is obtained by using Eq. (13) [28]:

WA =��k1�k1 + ��k2�k2 + ��k3�k3

�k1 + k2 + k3 , �13�

here ��k1� is the weight associated with each rule,hereas k1 is the membership of the output of each

ule. The fuzzy output range and the correspondingllocation decision are depicted in Table II.

. Delay Analysis

According to [13], components of packet delay con-ist of polling delay, dpoll; granting delay, dgrant; andueuing delay, dqueue. The dpoll is the time between theacket arrival and the next Request sent by thatNU, whereas the dgrant is the time interval from the

TABLE IFUZZY RULES FOR THE FUZZY REGULATOR

Rule rEF rAF rBE Decision

1 Low Low High Adjust BE2 Low High Low Adjust AF3 Low High High Adjust AF4 High Low Low Adjust EF5 High Low High Adjust EF6 High High Low Adjust EF7 High High High Adjust EF

Fig. 4. Output singletons based on Sugeno’s method.

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ONU’s request for a transmission window for thepacket until the Grant from the OLT is received.Moreover, dqueue is the queuing delay after the corre-sponding Grant from the OLT arrives at the ONU.The packet delay, d, is equal to [13]

d = dpoll + dgrant + dqueue, �14�

where on average the time between the packet arrivaland the next Request sent by that ONU is given by[13]

dpoll =T

2. �15�

Depending on how many packets are in the queueat the time of the new packet’s arrival, dgrant may spanmultiple polling cycles; dgrant can be calculated by us-ing the formula [13]

dgrant = Tq − Wi,p

Wmax� , �16�

where q is the queue size (including the new packetsize) at the moment of new packet arrival and Wi,p isthe pending Grant size. As for the dqueue, this delay isinsignificant compared with the previous two [13]:

dqueue = �q

RTq � Wi,p

�q − WI,P�modWmax

RTq � Wi,p

�. �17�

IV. PERFORMANCE EVALUATION

In this section, we present simulation results toverify our analysis and demonstrate the performanceof the proposed IFLDBA algorithm. We compare theresult obtained from the IFLDBA algorithm with theBP algorithm proposed by Xiong and Cao [15]. We areusing the same basis for the IFLDBA and BP algo-rithms, where we have three different priorities andthe bandwidths requested for both of the algorithmsare the same for comparison purposes.

The simulation was done using MATLAB. Table IIIshows the simulation parameters for both algorithms.

TABLE IIRELATIONSHIP BETWEEN WEIGHTED AVERAGE (WA) WITH AL-

LOCATION DECISION FOR THE FUZZY REGULATOR

Range of WA Allocation Decision

WA�1.5 Adjust EF1.5�WA�2.5 Adjust AF

WA�2.5 Adjust BE

n EPON, upstream line rates are 1.25 Gbits/s, butecause of the 8B/10B line encoding, the bit rate forata transmission is 1 Gbit/s [19].

An efficient DBA algorithm strives to achieve asigh bandwidth utilization as possible. The startingoint for our comparative study is to look at how theffered load affects the throughput of the two men-ioned algorithms. The variation is done in every pos-ible condition, and the result is shown in Fig. 5 below.fter simulating the two algorithms, we can see thatoth of the algorithms can perform efficient band-idth assignments. However, the IFLDBA algorithm

s proved to perform better than the BP algorithm, asan be seen in Fig. 5. The throughput for both algo-ithms is about the same when the offered load is lesshan 40%. This is because with low loads both algo-ithms can perform efficiently.

As the offered load increases to more than 40%, IF-DBA starts to show better performance than the BPlgorithm. When the offered load increases to morehan 80%, the throughput of IFLDBA starts to in-reases to as high as 87%, whereas the BP algorithmas maintained at 72%. The improvement made by

FLDBA is due to the usage of fuzzy logic for intra-NU, where the amount of bandwidth wasted can be

TABLE IIISIMULATION PARAMETERS

Parameter Valuea

No. of ONUs �i� 16Upstream bandwidth, RT 1 Gbit/s

Maximum transfer window, Wmax 15,500 bytesMaximum cycle time, Tmax 2 ms

Limitation bandwidth for EF, SEF 20%�Bi

Limitation bandwidth for AF, SAF 40%�Bi

imitation bandwidth for BE, SBE 40%�Bi

Guard time, Bg 5 �sBuffer size 10 Mbytes

aBi is the bandwidth received for ONU i.

ig. 5. (Color online) Throughput versus offered load in everyariation for both the IFLDBA and BP algorithms.

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reduced, since bandwidth will be allocated more prop-erly to each type of traffic as compared with the BPalgorithm. Besides, IFLDBA makes almost full use ofthe excessive bandwidth for both inter- and intra-ONU as compared with the BP algorithm.

The value of average improved percentage P of uti-lized bandwidth in IFLDBA is defined as

P =�AvBwIFLDBA

utilized − AvBwBPutilized�

AvBwIFLDBAutilized , �18�

where AvBwzutilized is the average bandwidth utilized

for the z type of algorithm.

The improved percentage versus offered load for theIFLDBA algorithm over te BP algorithm is shown inFig. 6. The improvement exceeds 5% when the trafficload increases to 40%. It continues to increase linearlyuntil the offered load increases to 80%. When the of-fered load increases to 100%, the IFLDBA algorithmcan have up to 21% improved performance better thanthe BP DBA algorithm. The increase in bandwidthutilization was due to fewer unused slot remainders inIFLDBA. This is because, with fuzzy logic, the deci-sion used in allocating bandwidth is based on re-quested bandwidth from the ONU. It thus ensuresthat the ONU will not be granted more time slots thanrequested. However, in BP DBA, some time slots areoften being wasted. In addition, idle time is reduced inIFLDBA. IFLDBA is using the mechanism as men-tioned in [29]. With this method, bandwidth is grantedinstantaneously for lightly loaded ONUs without anyidle time.

The heavily loaded ONUs are scheduled after theOLT receives all REPORT messages and performscomputation for bandwidth allocation. This can leadto less idle time in IFLDBA. On the other hand, BP isusing a broadcast polling method. With broadcastpolling, the OLT has to wait for all REPORT messagesto arrive before performing computation for band-width allocation. Thus, the idle time is longer.

Fig. 6. (Color online) Improved percentage versus offered load forthe IFLDBA algorithm over the BP algorithm.

Other than the bandwidth utilization, we also studyhe fairness and the delay of both the IFLDBA and BPlgorithms. The fairness index is the measure of close-ess of the bandwidth allocation to the desired goal. Iteasures the equality of the bandwidth allocated. If

he bandwidth allocated is not equal, it tells how farhe allotment is from equality.

Since fairness is achieved between different ONUsn IFLDBA, it can be compared with the BP algo-ithm, since the BP algorithm also supports fairnessetween different ONUs. The fairness among differ-nt ONUs over EPON, f, is defined as [30]

f =

�i=1

N

xi�2

N�i=1

N

xi2�

2, �19�

here N is the number of ONUs and xi is the through-ut achieved by ONU i.

Figure 7 shows the fairness index between differentNUs of the IFLDBA and BP algorithms. It can be ob-

erved that both of the algorithms show excellent per-ormances in terms of fairness. This indicates that thepstream bandwidth is fairly shared by all uploadingNUs. A very slight variation occurs in the BP algo-

ithm due to the obvious existence of highly loadednd lightly loaded ONUs. Highly loaded ONUs anductuated traffic can decrease the fairness index.

Figure 8 compares the packet delays versus trafficoad between the proposed algorithms, IFLDBA andhe BP algorithm, for all three types of traffic, EF, AF,nd BE. As mentioned above, IFLDBA supports localriority. As for priority between ONUs, lightly loadedNUs will be granted first, followed by highly loadedNUs. Since the BP algorithm supports global prior-

ty, the comparison of the delay performance betweenFLDBA that supports only local priority and the BPlgorithm that supports global priority can be ob-erved.

ig. 7. (Color online) Fairness index versus offered load for bothhe IFLDBA and BP algorithms.

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The simulation results show that for all three typesof traffic, the proposed IFLDBA performs better thanthe BP algorithm, especially when the traffic load ishigh. Figure 8(a) shows the comparison of IFLDBAand BP algorithms for EF traffic. When the offeredload is more than 20%, IFLDBA shows lower delaythan the BP algorithm. This is because the EF trafficin lightly loaded ONUs in IFLDBA does not have towait for all REPORT messages to arrive before it canperform the bandwidth computation. Since the delayis short in EF lightly loaded ONUs, delay in EF highlyloaded ONUs can also be shortened. As the offeredload increases above 80%, the delay for IFLDBA ismaintained at 2.4 ms. This situation happens be-cause, as the offered load is high, there are morehighly loaded ONUs and fewer lightly loaded ONUs inthe system. When lightly loaded ONUs are fewer, thedelay for EF traffic in the lightly loaded ONUs also de-creases, and the delay of EF traffic in highly loadedONUs will also be less compared with when we havemore lightly loaded ONUs. For this reason, eventuallythe delay performance is somehow maintained. When

Fig. 8. (Color online) Packet delay versus offered load between theIFLDBA and BP algorithms for (a) EF traffic, (b) AF traffic, and (c)BE traffic.

he offered load is 100%, IFLDBA shows an improve-ent on the delay as high as 29.4% over the BP algo-

ithm.

In Figure 8(b), it can be observed that the delay ofF traffic in IFLDBA is shorter than in the BP algo-ithm. This is because there are fewer unused slot re-ainders in AF traffic in IFLDBA, since IFLDBA

rants as much bandwidth as requested up to a cer-ain limit and never more than the requested band-idth.

We can also observe in the figure that as the offeredoad was increased to more than 80%, the delay was

aintained at 4 ms in IFLDBA. When the offered loads 100%, IFLDBA improves on the BP algorithm inerms of AF delay by as much as 49.5%. In the case ofE traffic in Fig. 8(c), the delay of the IFLDBA and BPlgorithms is less than 2.6 ms when the offered load isess than 20%. The delay in BE is higher than the de-ay in EF and AF because of the light load punish-

ent. However, the delay is still less in IFLDBA, be-ause BE traffic in lightly loaded ONUs is beingranted instantaneously, thus reducing the delay inE traffic. Furthermore, since there are fewer unusedlot remainders in the IFLDBA algorithm, it can re-uce the packet delay by as much as 32.4% when theffered load is 100%.

V. CONCLUSION

An intelligent DBA algorithm based on fuzzy logicrinciples has been successfully demonstrated. Theroposed IFLDBA algorithm is using fuzzy logic to al-ocate the bandwidth within an ONU in order to in-rease the throughput and decrease the delay. The IF-DBA algorithm shows better performance thannother existing well-known DBA algorithm, the BPlgorithm, in terms of throughput and delay. As com-ared with the BP algorithm, the proposed IFLDBAlgorithm can improve the bandwidth utilization upo 21% because of the fewer unused slot remaindersnd lower idle time. In addition, IFLDBA can also re-uce the average packet delay by as much as 29.4%or EF traffic, 49.5% for AF traffic, and 32.4% for BEraffic. The fairness index has been maintained asigh as 1 when the proposed algorithm was employed.

This work was supported in part by the Ministry ofcience, Technology and Innovation, Malaysia, underrant 01-02-03-SF0124.

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urul Asyikin Mohd Radzi received the B.Sc. and M.Sc. degreesn electrical and electronics engineering from Universiti Tenaga Na-ional, Malaysia, in 2008 and 2010, respectively. She is currently autor at the Department of Electronics and Communication Engi-eering, College of Engineering, Universiti Tenaga Nasional, Ma-

aysia. Her research interest is in the area of EPON and QoS.

orashidah Md Din received the B.Sc. degree in electrical engi-eering from Memphis State University, USA, in 1985, and M.S.nd Ph.D. in electrical engineering from Universiti Teknologi Ma-aysia in 1989 and 2007, respectively. She joined UITM in 1990 as aecturer in the Faculty of Electrical Engineering, and in 1997–2001he joined GITN Sdn. Bhd, the Malaysian e-government networkervice provider. She then joined the Department of Electronics andommunication Engineering, College of Engineering, Universitienaga Nasional in 2001 as a Senior Lecturer. She is currently anssociate Professor and Head of the same department since 2008.er research interests include RF radiation, Internet architectures,PONs, and teletraffic engineering. She has authored and coau-

hored more than 90 research papers in journals and conferenceroceedings. Dr. Norashidah has been an executive committeeember for IEEE Malaysia Communications and Vehicular Tech-ology Joint Chapter for the past 7 years.

ohammed Hayder Al-Mansoori received the B.Sc. degree (withrst class honors) in electronic and communications engineering

rom Al-Mosul University, Al-Mosul, Iraq, in 1998, and the M.Sc.nd Ph.D. degrees from the University Putra Malaysia, Serdang,alaysia, in 2004 and 2008, respectively. In 1999, he was an In-

tructor at the Department of Electronics and Communications En-ineering, IBB University. From 2006 to 2008, he was a Lecturer athe Faculty of Engineering and Technology, Multimedia University.

http://www.telecom-paristech.fr/_data/files/docs/id_594_1149772769_271.pdf



http://www.cse.wustl.edu/~jain/atmf/ftp/af_fair.pdf

ItaUtCr


He is currently an Associate Professor and the head of unit researchand consultancy in the Department of Electronics and Communica-tion Engineering, College of Engineering, Universiti Tenaga Na-sional, Malaysia. His current research interests include optical fibercommunications, fiber lasers, fiber sensors, and EPONs. He has au-thored or coauthored more than 100 research papers in journalsand conference proceedings. Dr. Al-Mansoori is a member of the
IEEE and Optical Society of America (OSA). a
ntan Shafinaz Mustafa received the B.Sc. in electrical and elec-ronics engineering from the University of Coventry, UK, in 1996nd M.Sc. degree in communications and network engineering fromniversiti Putra Malaysia, in 2006. She is currently a Lecturer in

he Department of Electronics and Communication Engineering,ollege of Engineering, Universiti Tenaga Nasional, Malaysia. Heresearch interests include EPONs, multimedia networks, and im-
ge processing.

Documents

Intelligent Dynamic Bandwidth Allocation Algorithm in Upstream EPONs