978-1-4244-4547-9/09/$26.00 ©2009 IEEE TENCON 2009

New Dynamic Bandwidth Allocation Algorithm Based Fuzzy-Logic for Ethernet PON

Nurul Asyikin Mohd Radzi, Norashidah Md. Din, Intan Shafinaz Mustafa, Sajaa Kh. Sadon and

Mohammed Hayder Al-Mansoori Communications Service Convergence Technologies Centre Department of Electronics and Communication Engineering

College of Engineering, Universiti Tenaga Nasional, Kajang, Malaysia e-mail: asyikin@uniten.edu.my, norashidah@uniten.edu.my, mansoori@uniten.edu.my

Abstract— We proposed a fuzzy-logic-based dynamic bandwidth allocation (FLDBA) algorithm for upstream Ethernet passive optical network (EPON). In the proposed DBA algorithm, the optical line terminal allocates bandwidth to the optical network units in proportion to the priority category of the traffic class. Simulations were performed to prove the accuracy of the FLDBA algorithm by comparing the algorithm with a previous DBA algorithm, Min’s DBA algorithm that is an enhancement from interleaved polling with adaptive cycle time. The results show significant performance, as high as 20% improvements in terms of the bandwidth utilization, delay and the fairness.

Keywords- Ethernet Passive Optical Network; fuzzy logic; dynamic bandwidth allocation; fairness.

I. INTRODUCTION Recently, the rapid development of communication

industries in recent years has caused the bottleneck problem in the access network. Due to the convergence of low-cost Ethernet equipment and low-cost fiber infrastructure, Ethernet passive optical network (EPON) is being considered as a promising solution for next generation broadband access network [1]. EPON is a point-to-multipoint optical network, where the term ‘passive’ in EPON means that it employs only passive optical components in the transmission path from source to destination. EPON topology is tree-based architecture where transmission occurs between an optical line terminal (OLT) and optical network units (ONUs) and is connected to each other by means of a 1:N optical splitter or coupler [2]. An EPON carries all data encapsulated in Ethernet frames and is defined by the IEEE 802.3ah standard. In downstream transmission, EPON uses broadcasting, where the OLT will broadcast frames and ONUs will selectively receive frames addressed to them. Due to the directional properties of the optical splitter or coupler, ONUs are able to communicate only to the OLT in the upstream direction.

They cannot communicate directly with one another [3]. Since in the upstream direction, all ONUs share the transmission medium, several approaches such as static bandwidth allocation (SBA) or dynamic bandwidth allocation (DBA) can be used. In SBA, once bandwidth is assigned to a subscriber, it will be unavailable to other subscribers on the network. To overcome this limitation, DBA has been introduced, where it has the ability to quickly reapportion

bandwidth on EPON based on current traffic requirements [4]. The DBA scheme can provide more efficient bandwidth allocation for each ONU to share network resources and offer better quality-of-service (QoS) for end users.

Many DBA schemes [5]-[11] have been developed over the years to cope with the challenges of high bandwidth utilization and Quality of Service (QoS) provisioning. The overview of some of the algorithm can be found in [5]. Among the most famous algorithm is interleaved polling with adaptive cycle time (IPACT). With IPACT [6], [7] and [8], idle time is eliminated by sending grant messages for succeeding ONUs while receiving transmission from previous granted ONUs. It requires OLT to poll ONUs in a round robin fashion and dynamically assign them before transmission. However IPACT is not suitable for delay, jitter and sensitive services or service level agreements (SLAs).

Strict priority scheduling is used in [9] as an enhancement of IPACT. It can increase the effectiveness of existing bandwidth and efficiently manage various types of user traffic by categorizing them into three categories; high priority for fixed bandwidth, medium priority for assured bandwidth and low priority for best effort traffic class. However, with this algorithm, best effort cannot be achieved and if certain ONU continuously generates a great deal of traffic, it will cause starvation of other ONUs. Request/permit mechanism is used in [10] and is similar as [9] but is improvised to accomplish best-effort service. A request-counter (RC) is used to control each ONU’s queue state. With this technique, it can reduce the queue length and can supply priority between ONUs and ONU can receive grant fairly.

Min et al. [11] has improved the algorithm in [10] by supporting priority queues and fairness among ONUs over upstream EPON. However, since Min’s algorithm is ratio-based, bandwidth is not fully utilized in the case where the requested bandwidth is higher than the limitation bandwidth. Therefore, in order to resolve this problem, we propose for the first time, to the best of our knowledge, a new enhanced DBA with fairness scheme by using fuzzy logic. The algorithm is called as fuzzy logic DBA (FLDBA) algorithm and the main focus of the algorithm is to divide bandwidth fairly between different ONUs and within each ONU. Fuzzy logic is used to divide the bandwidth within each ONU (intra-scheduling). The

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algorithm supports priority queues and fairness among ONUs over EPON. Priority queues are important since we are dealing with QoS that involves the allotment of voice, video and data services. Without priority queues, all types of services will be served equally. It will create various kinds of problem since voice is sensitive to delay. Fairness is important so that bandwidth will be divided to each ONU equally.

The remainder of this paper is organized as follows. In Section 2, we provide the proposed FLDBA algorithm. Our simulation setup and examination of the impact of traffic load with the bandwidth utilization is described in Section 3. We summarize our conclusions in Section 4.

II. PROPOSED FLDBA ALGORITHM

In this section, we present a new enhanced intelligent DBA scheme using fuzzy logic that we called as FLDBA algorithm. The FLDBA algorithm will be compared to Min’s DBA algorithm [11]. Both of these algorithms study the bandwidth allotment between different ONUs and within each ONU. In Min’s DBA algorithm, business is divided into three different types of business according to the time postpone sensitivity; TDM bandwidth, video bandwidth and data bandwidth. Between different ONUs, the received bandwidth is achieved using the formula below [11]:

Gmin , 0=Ri

iB = Gmax , GRi max> GRR ii max, ≤ (1)

where, Bi (bits) represents the granted window size for

ONU i and Ri (bits) is the requested window. We set the biggest transfer window as Gmax and the minimum transfer window as Gmin for the ONU bandwidth allotment.

In each ONU, bandwidth allotment will follow the priority of the business. First, each ONU reserves a fixed bandwidth for TDM business, using this formula [11]:

Btotal - ∑ = +Ni

TDMniR1 1, RTDM

ni 1, + < STDMi

Bavail = Btotal - ∑ =

Ni

TDMiS1 RTDM

ni 1, + ≥ STDMi (2)

where Rx

ni 1, + represents the bandwidth that business x requested in the n+1 timeslot and S x

i represents the limitation bandwidth for business x. Bavail is the available bandwidth and Btotal is the total bandwidth. Then, ONU allocates the bandwidth for the other two kinds of business as follows:

If ( )∑ +=Ni

Datani

Videoni BB1 ,, ≤ Bavail , then the allotment bandwidth

will be according to their requirement. Otherwise, the bandwidth allocation follows the SLA below [11]:

=+BVideoni 1, Bavail ⎥

⎦

⎤⎢⎣

⎡+ SS

SDatai

Videoi

Videoi (3)

=+BDatani 1, Bavail ⎥

⎦

⎤⎢⎣

⎡+ SS

SDatai

Videoi

Datai (4)

If there is a surplus bandwidth, it would be allocated

between each ONU again using the above algorithm. The surplus is checked using this formula[11]:

( )∑ ++=

Ni

TDMni

Datani

Videoni BBB1 ,,, ≤ Btotal (5)

However, this algorithm has its own disadvantage when the bandwidth requested is higher than the limitation bandwidth. FLDBA is proposed to overcome this problem and hence fully utilized the bandwidth. The concrete analysis of the FLDBA algorithm will be explained here.

In our proposed FLDBA algorithm, the traffic is divided into three priority categories; high priority, medium priority and low priority. The high priority is expedited forwarding (EF) bandwidth which supports voice traffic that requires bounded end-to-end delay and jitter specifications. Whereas medium priority is the assured forwarding (AF) bandwidth that supports video traffic that is not delay sensitive but require bandwidth guarantees. Finally, the low priority is best effort (BE) bandwidth that supports data traffic and is not sensitive to end-to-end delay or jitter.

Service discipline that used in this algorithm is limited service since the cycle time of this approach is variable, but it will not surpass a certain limit. Service discipline is a way for the OLT to determine the granted window size for ONU i depending on the requested window. In this algorithm, the allotment of bandwidth between different ONUs will be done in the same manner as the Min’s algorithm with the MATLAB pseudo code as follows:

Bi = Gmin if Ri = 0 Else Bi = Gmax if Ri > Gmax Else Bi = Ri if Ri <= Gmax (6)

Within each ONU, the bandwidth allotment will be done according to the flow chart in Fig. 1. First of all, we allocate the bandwidth for voice traffic. Then, the remaining bandwidth will be allocated to video, then data bandwidth.

The fuzzy logic regulator is used to allocate bandwidth in each ONU and is triggered when there is contention for bandwidth between classes. The regulator comprises of three input parameters and one output parameter as shown in Fig. 2. The input parameters are requested voice (rvoice), requested video (rvideo), and requested data (rdata). The output value is the allocation decision whether to adjust voice, adjust video or adjust data. The input membership functions are shown in figures below. The trapezoidal functions are used to represent the linguistic truth values, i.e low and high. The fuzzy rules are shown in table below. There are seven rules that relate the three inputs with the fuzzy output.

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New requestedbandwidth

Ri,voice

Si, voice

Yes

NoRi,video + R i,data

Btotal - B voice

FLRFuzzy logicRegulator (FLR)

FLR 1.5

B i, voice= Ri, voice

Bi, video = Ri, video ;

B i, data = Ri, data

Yes

No

FLR < 2.5

Adjust Video

Yes No

Adjust Data

FLR ≥

Adjust Data Adjust Video

Yes No

Yes No

Adjust Voice

Check surplus bandwidth

Figure 1. Flow chart of the FLDBA algorithm within each ONU

Figure 2. Fuzzy Logic Regulator block diagram

All the rules use “AND” connectives. The construction of the rules is based on logical reasoning of how the system should behave in allocating bandwidth. The rules are shown in table below.

The membership function for rvoice shows that bandwidth is ‘low’ if the requested voice bandwidth is less than the limitation bandwidth for voice, which is 12.5 Mbps in this case. For rvideo and rdata, bandwidths are ‘low’ when requested video and requested data are less than their limitations, which are 25 Mbps.

Adjust voice bandwidth is done by first allocating bandwidth for voice since voice is delay sensitive. Voice bandwidth will be adjusted according to the formula below:

⎟⎟⎠

⎞⎜⎜⎝

⎛+

=+SS

SBB Videoi

Voicei

Voicei

totalVoice

ni 1, (7)

0 10 20 30 40 50 60

0

0.2

0.4

0.6

0.8

1

rvoice

Deg

ree

of m

emb

ersh

ip

low high

Figure 3a. The rvoice membership function

0 10 20 30 40 50 60

0

0.2

0.4

0.6

0.8

1

rvideo

Deg

ree

of m

emb

ersh

ip

low high

Figure 3b. The rvideo membership function

0 10 20 30 40 50 60

0

0.2

0.4

0.6

0.8

1

rdata

Deg

ree

of m

embe

rsh

ip

low high

Figure 3c. The rdata membership function

TABLE I. FUZZY RULES FOR THE FUZZY REGULATOR

Rule rvoice rvideo rdata Decision 1 Low Low High Adjust data 2 Low High Low Adjust video 3 Low High High Adjust video 4 High Low Low Adjust voice 5 High Low High Adjust voice 6 High High Low Adjust voice 7 High High High Adjust voice

Then, if requested video is less than limitation, ONU will

receive all the remaining bandwidth. Otherwise the allotment will be done according to the formula below:

⎟⎟⎠

⎞⎜⎜⎝

⎛+

=+SS

SBB Datai

Videoi

Videoi

availVideo

ni 1, (8)

As for data bandwidth, if requested is less than the

remaining bandwidth, ONU will receive all the requested video bandwidth. Otherwise, ONU will receive up until the remaining bandwidth after allocation to voice and video bandwidth.

Requested voice bandwidth

Requested video bandwidth

Requested data bandwidth

Fuzzy Logic

Regulator

Allocation Decision:

Adjust voice, adjust video, Adjust data

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Adjust video bandwidth is done by first allocating bandwidth for voice. If requested voice bandwidth is less than limitation, ONU will receive whatever it is requesting for. Otherwise, it will be restricted to its limitation bandwidth. Then, we calculate the sum of requested bandwidth for both video and data. If the summation of both traffic classes is less than the available bandwidth after allocating bandwidth to voice, ONU will receive all the requested video and data bandwidth. Otherwise, the allotment will be done according to (7). As for data bandwidth, if requested is less than the remaining bandwidth, ONU will receive all the requested data bandwidth. Otherwise, ONU will receive up until the remaining bandwidth after allocation to voice and video bandwidth.

Adjust data bandwidth will be done by first allocating bandwidth for voice, then video. ONU will receive all the requested voice and video bandwidth. For data bandwidth, if requested is less than the remaining bandwidth, ONU will receive the requested bandwidth. Otherwise, ONU will receive up until the remaining bandwidth after allocation to voice and video bandwidth. One other possible rule which is when all the inputs are low has been eliminated since in this condition, the bandwidth allocation decision will be fulfilled and the process flow would not enter the fuzzy regulator. The same set of rules would be triggered every time the fuzzy regulator is invoked, whereby an appropriate consequent will be executed.

In order to obtain a single crisp solution for the output variable, the Sugeno inference process is used. Sugeno inference method is used since it is computationally efficient, works well with optimization and adaptive techniques, it has guaranteed continuity of the output surface and it is well suited to mathematical analysis. The fuzzy output range and the corresponding allocation decision are depicted in Table 2.

[ ]1 1 2 2 3 3

1 2 3

µ (k )k µ(k )k µ(k )k(k k k )

WA+ +

=+ +

(9)

where µ(k1) the weight associated with each rule, whereas k1 is the membership of the output of each rule. TABLE II. RELATIONSHIP BETWEEN WEIGHTED AVERAGE (WA)

WITH ALLOCATION DECISION FOR THE FUZZY REGULATOR

Range of WA Allocation decision

WA ≤ 1.5 Adjust voice

1.5<WA<2.5 Adjust video

WA ≥ 2.5 Adjust data

III. SIMULATION RESULT In this section, we present simulation results to verify our

analysis and demonstrate the performance of the proposed FLDBA algorithm. We compare the result obtained from FLDBA algorithm with the algorithm created by Min. The simulation is done using MATLAB. Table 3 shows the simulation parameters for both algorithms. In EPON, upstream line rates are 1.25 Gb/s, but due to 8B/10B line encoding, the bit rate for data transmission is 1 Gb/s [10].

TABLE III. SIMULATION PARAMETERS

Parameter Value Number of ONUs (i) 16

Upstream bandwidth, Btotal 1 Gbps Maximum transfer window, Gmax 62.5 Mbps Minimum transfer window, Gmin 3 bps

Limitation bandwidth for voice, Svoice 20% x Gmax Limitation bandwidth for video, Svideo 40% x Gmax Limitation bandwidth for data, Sdata 40% x Gmax

An efficient DBA algorithm strives to achieve as high

bandwidth utilization as possible. The starting point for our comparative study is to look at how the offered load affects the bandwidth utilization of the two mentioned algorithms. The variation is done in every possible condition and the result is shown in Fig. 4 below. After simulating the two algorithms, we can see that both of the algorithms can perform efficient bandwidth assignments. In the case when all three types of bandwidth are low, the bandwidth of both FLDBA algorithm and Min’s DBA algorithm are fully utilized. However, when other variations are simulated, it proves that FLDBA algorithm is more dynamic than the algorithm developed by Min.

Fig. 4a shows the condition when 45% of the requested bandwidth comes from voice, 30% from video and 25% from data bandwidth. On the other hand, Fig. 4b shows the condition when 40% of the requested bandwidth comes from voice, 10% from video and 50% from data bandwidth. As can be seen in the figures below, when the offered load is less than 40%, the result for both Min and FLDBA is more or less the same because the bandwidth requested is low and both of the algorithm will grant all the bandwidth than ONUs are requesting for. However, from 40% offered load onwards, the bandwidth utilized for FLDBA algorithm is better than in Min’s DBA algorithm. This is because with Min’s DBA algorithm, ONU will receive the same amount of bandwidth for both video and data even though in cases where data is requesting more bandwidth than video. Since FLDBA is using fuzzy logic, the problem is overcome by giving more bandwidth to data as compared to video traffic. Even more, for Min’s DBA algorithm, the allocation for voice will always be up until its limitation voice bandwidth. However, for FLDBA algorithm, voice will receive more than limitation up until its

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allocated SLA. Thus, this will improve the bandwidth utilization in FLDBA algorithm. The improved percentage of FLDBA exceeds 9% when the traffic load is low. When the traffic load increases, FLDBA algorithm can have 20% improved percentage more than Min’s DBA algorithm. The delays for all three types of traffic are studied here. Fig. 5 shows the delay verse offered load for all three types of traffic, voice, video and data traffics. FLDBA has shorter delay for all three types of traffic compared to Min’s DBA. This proves the concept of the higher the bandwidth utilization is, the shorter the delay. We also study the fairness among multiple connections over EPON. The fairness index, f is defined as:

2

1

2

1

⎟⎠⎞⎜

⎝⎛∑

⎟⎠⎞⎜

⎝⎛∑

=

=

=

n

ii

n

ii

xn

xf (12)

Where n is the number of concurrent connections and xi is the throughput achieved by timeslot i. Fig. 6 plots the corresponding fairness indices of multiple connections for both FLDBA algorithm and Min’s DBA algorithm. From the figure, it proves that the bandwidth is more evenly shared for each timeslots in FLDBA algorithm as to be compared with Min’s DBA algorithm.

0

2

4

6

8

10

12

14

16

0 10 20 30 40 50 60 70 80 90 100 110

Offered load (%)

Del

ay (m

s)

FLDBA

Min's DBA

(a)

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90 100 110 120 130Offered load (%)

Ban

dwid

th u

tiliz

atio

n (%

)

FLDBA

Min's DBA

Figure 4a. Bandwidth utilization verse offered load at requested video bandwidth and requested data bandwidth are low but requested voice bandwidth is high.

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90 100 110 120 130Offered load (%)

Band

wid

th u

tiliz

atio

n (%

)

FLDBA

Min's DBA

Figure 4b. Bandwidth utilization verse offered load at requested voice and data bandwidth are high but requested video bandwidth is low.

0

2

4

6

8

10

12

14

16

18

20

0 10 20 30 40 50 60 70 80 90 100 110

Offered load (%)

Del

ay (m

s)

FLDBA

Min's DBA

(b)

0

5

10

15

20

25

30

35

0 10 20 30 40 50 60 70 80 90 100 110Offered load (%)

Del

ay (m

s)

FLDBA

Min's DBA

(c)

Figure 5. Delay verse offered load for (a) voice traffic, (b) video traffic and (c) data traffic.

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5Time slot

Fairn

ess

inde

x

FLDBA

Min's DBA

Figure 6. Fairness among multiple connections over EPON

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IV. CONCLUSION We have presented a fuzzy-logic-based DBA algorithm for

EPON system. We examined the bandwidth utilization and fairness performance of FLDBA algorithm and existing Min’s DBA algorithm. The simulation results show significant performance improvements; as high as 20% in terms of the bandwidth utilization. The delay and fairness has also been studied and it proves significant improvements when the proposed algorithm is employed.

ACKNOWLEDGMENT This work was partly supported by the Ministry of Science,

Technology and Innovation Malaysia and the Universiti Tenaga Nasional under research grant 01-02-03-SF0124.

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