Adaptive Hierarchical Weighted Fair Queueing

Scheduling Algorithm for WiMAX Waleed K. AI-Ghaneml,2, Mohammad I1yas2, Imad Mahgoub2.

lCollege of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia 2College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, FL, USA 33431

{walghan, ilyas, mahgoubi}@fau.edu

Abstract-In this paper we propose Adaptive Hierarchical

Weighted Fair Queuing scheduling algorithm for WiMAX

networks. We compare the performance of the proposed

algorithm with the Weighted Fair Queuing scheduling

algorithm. Comparison is done by simulating the traffic in a

WIMAX network and by measuring Quality of Service aspects.

Simulation results indicate that the proposed scheduling

algorithm has better QoS aspects with all traffic loads.

I. INTRODUCTION

One of the main objectives of WiMAX is to manage

bandwidth resources at the radio interface in an efficient

manner, while ensuring that Quality of Service (QoS) levels, negotiated at the time of connection setup, are met in an

appropriate way. The most important component for QoS

provisioning is the Packet Scheduler. Scheduling refers to the

mechanism for serving the queued resource requests of

various users. A scheduling discipline has two components:

deciding the order of servicing the users' requests and management of the service queues. Scheduling is important

in both best effort and QoS networks. In the former case, the

fair allocation of network bandwidth among a wide variety of

network users is a prime objective.

In WiMAX networks, both uplink and downlink resource

allocation are controlled by a scheduler in the base station. Capacity is shared among multiple users on a demand basis,

using a burst TDM scheme. When using the OFDMA-PHY

mode, mUltiplexing is additionally done in the frequency

dimension, by allocating different subsets of OFDM

subcarriers to different users. Resources may be allocated in

the spatial domain as well when using the optional advanced antenna systems. The system allows for bandwidth resources

to be allocated in time, frequency, and space and has a

flexible mechanism to convey the resource allocation

information on a frame-by-frame basis [ 1].

The rest of the paper is organized as follows. In Section 2, the Adaptive Hierarchical Weighted Fair Queuing Scheduling

Algorithm is provided. Section 3 describes the simulation

framework that includes the simulation parameters, traffic

model, and the performance metrics used to evaluate the

algorithms and the results and discussion of the experiments.

In Section 4, we summarize the results.

II. ADAPTIVE HIERARCHICAL WEIGHTED FAIR QUEUING

The proposed scheduling algorithm; the Adaptive

Hierarchical Weighted Fair Queuing (AHWFQ) will follow

978-1-4673-2890-6/12/$31.00 ©2012 IEEE

the technique IEEE 802. 1 p that uses aggregate traffic­

handling mechanisms. The base station will classify jobs into

a few different generic classes putting each class in a different queue that is more scalable and reduces the

maintenance and processing. Then the base station will

handle the traffic from the separate flows. The IEEE working

group developed QoS technique; it is a 3-bit field within an

Ethernet frame header when using tagged frames on an 802. 1

network. It specifies a priority value of between 0 and 7 inclusive that can be used by QoS disciplines to differentiate

traffic [ 1]. The eight classes are Background, Best Effort,

Excellent Effort, Critical applications, Video, < 100 MS

latency, Voice, < 10 MS latency, Internetwork Control, and

Network Control. Table 1 list the traffic classes and describe

their features.

Table I: The IEEE broad recommendations

Priority Traffic Type 0 Background Not time-critical or loss-sensitive, and of

lower priority than best effort. This type includes bulk transfers and other activities that are permitted on the network but that should not impact the use of the network by other users and applications.

1 Best Effort Not time-critical or loss-sensitive. This is LAN traffic handled in the traditional fashion.

2 Excellent Effort Not time-critical but loss-sensitive. This is a best-effort type of service that an information services organization would deliver to its most important customers.

3 Critical Not time-critical but loss-sensitive, such as Applications streaming multimedia and business-critical

traffic. A typical use is for business applications subject to some form of reservation or admission control, such as capacity reservation per flow.

4 Video, < 100 MS Time-critical, characterized by less than 100 latency MS delay, such as interactive video.

S Voice, < IO MS Time-critical, characterized by less than 10 latency MS delay, such as interactive voice.

6 Internetwork Real-time traffic in a heterogeneous Control internetworking environment with IP routers,

MAC bridges, Hubs, Switched LANs etc. 7 Network Control Both time-critical and safety-critical,

consisting of traffic needed to maintain and support the network infrastructure, such as routing protocol frames.

237

2.1 Scheduling Algorithm The BS classifies the traffic into the IEEE working group

defmed 8 classes, and gives each flow different initial weight. The initial weights are proportional to the following input

data; UGS (5Mbps), rtPS ( l4.4Mbps), extend rtPS ( l2Mbps),

nrtPS ( l2.5Mbps) and BE data jobs (6.5Mbps) [2].

The scheduling algorithm will divide the resources (slots)

on the queues according to their priorities (Equation 1). If any

queue has enough resources to process all jobs in the queue it handles the jobs in fust come fust served rule.

S· = � ( 1) I Ll=oPi

Where: pi is the priority of class i where i = 1,2 . . . ,7.

S is total number of slots.

Si is the allocated number of slots for class i where i = 1,2,

... ,7.

If there are not enough resources, the scheduler allocates

the required slots to the fust jobs in the UGS queue and drops the remaining jobs. The jobs in the queues rtPS extend rtPS,

and nrtPS are assigned a weight proportional to its age in the

queue and inversely proportional to the number of slots

required to process it (Equation 2).

Wj = (CXL�: a.) (j3L�l fi) (2) J=l J

Where:

Wj is the job weight

aj is the job age in the queue

Qi is the queue length

rj is the number of slots required for job j

a, and � are two coefficients to change the relative influence

of age and size.

The scheduling algorithm sorts the jobs in the queue

according to their weights and services them in the new order. This avoids starvation by giving old jobs higher

priorities, and tries to make the queues shorter by servicing

jobs that need less resources before jobs requires more

resources. The scheduling algorithm allocates the required

slots to the first Kjobs in the queue such that:

Lf=l rj S; Si S; Lf=il rj where K S; Qi (3)

The Best Effort queue uses simple Round Robin routine to

allocate resources to the queued jobs. The base station drops

jobs when they reach a predefmed time out limit. The

scheduling algorithm revises the initial priorities of all classes based on calculating the weighted moving average of the

lengths of the queues. The objective is to allocate more slots

to the traffic flows that have higher delay time or many

dropped jobs. The moving average is calculated as follows; if

the previous average is Qit, then the new average will be:

Qit+1 = h Qit+1 + (1 - f) * Qit (4)

Where f < 1 is the smoothing factor, and Qil = Qi" The window size of the weighted moving average is

infmite, the weight of the old queue lengths decreases

exponentially with time but it never becomes zero, meaning

that all previous queue lengths are included in the

calculations of the current weighted moving average. The

new class priorities are calculated as follows:

_ Qit+1 Pi - 7 . Li=o Q1t+1

Figure (1) illustrates the scheduling algorithm.

OropJob5. te�ched tll'Ileout

(5)

conlt'oI.lnd IntcmelwCnt rint comt: Ilfn

Conu� u�rvtd MIMI"<

Figure 1: Adaptive Hierarchical Weighted Fair Queuing

III. SIMULA nON FRAMEWORK AND EXPERIMENTS

For simulations we consider uniformly distributed mobile

users within a hexagonal cell. We used the IEEE 802. 16j

standard [ 1]. The bandwidth is 10MHz and the number of

subcarriers is 200. Frame length is 5ms, 24 down link symbols and each frame has 24 adaptive and modulation

coding (AMC) channels. Simulation parameters are as in

Table 2. Each class's generated traffic types are as in Table 3

from reference [4]. Each traffic queue can buffer all incoming

jobs.

3.1 Generated traffic We have implemented traffic model to generate traffic of

self-similar characteristics with distributions as in table (2). The file size distribution is plotted in Figure (2), we notice

that the body of the distribution is Lognormal. The

distribution has heavy tails which are of Pareto distribution.

Figure (3) illustrates the distribution of references among

files. The distribution follows Zipf's law where number of

references is inversely proportional to the file rank.

238

Table 2: Simulation parameters

Parameters Values System OFDMA Downlink channel bandwidth 5 MHz Carrier frequency 3.5GHz FFT size 512

N umber of data subcarriers 384 Number of bands 12 Number of subcarriers per sub band 32 Neighboring subcarrier spacing 10.94kHz Sampling frequency 5.6MHz

Sampling factor 28/25

OFDM symbol duration 97.lus Slot period 97.1us Frame period 5ms Square cell 1.5km User distribution Uniform Mobile velocity 3km/h

Table 3: Generated Traffic Type

Service Class Data rate UGS 50 kbps rtPS 5 kbps - 2Mbps ertPS 32 kbps nrtPS 1 0 kbps - 2 Mbps BE 10 kbps - 2 Mbps

The number of files generated is lO,OOO files. 3000 files of them are HTML documents that have embedded files and

3200 are loner files. The remaining 3800 files are embedded

files that can't be requested directly. Among the documents

with embedded files the maximum number of requested files

is 142, the mean number of requested files is 2.7603 and

standard deviation is 8.3875.

0.!9<J9 0.9995 0.999

0.995 0�9

0�5 0.9

0.15

0.5

025

0.1 0.05

001 0.005

oocm 0.00(11 �

10'

,

10' 10' 10' lrf log10 Fie SIzes (Byles)

Figure 2: Generated File Size distribution

Requests are generated by linking the HTML documents

with the embedded files then the matching between the file

sizes, popularities and requests has been done. Figure (4)

shows the Cumulative Distribution Function of probability of

requesting files.

3.2 Simulation results Six scenarios are simulated. The number of available time

frame slots is the same in all of them. Number of subscribers are 750, lOOO, 1250, 1500, 1750, and 2000 subscribers. The

purpose is to check the performance of the AHWFQ

scheduling algorithm, and compare it with the Weighted Fair

Queuing (WFQ) scheduling algorithm under different traffic

loads.

QoS measures are calculated for the 6 scenarios using the proposed AHWFQ, and the WFQ. Results are in Tables 4,

and 5.

Po�a.ty dSOiW»llor file'

45110

4000

1000

5IJO

w w � • 511 � ro M � m Roanltisoffiie

Figure 3: The distribution of references among files

Table 4: Results of Adaptive Hierarchical Weighted Fair Queuing Scheduling Algorithm

Number of Subscribers 750 1000 1250 1500 1750 2000 Throughput Mbps 1388.25 1838.83 2301.01 2740.00 2919.23 2936.69 GoodPut 1350.67 1789.59 2238.10 2666.40 2839.06 2855.12 Dropped out 0.00% 0.10% 0.25% 0.69% 5.45% 12.82% Retransmission 5.81% 7.01% 8.88% 12.47% 14.16% 11.98% Latency 2.45 4.19 9.58 36.59 194.62 253.59 Jitter 15.52 24.04 40.61 81.70 135.53 164.91

In Figure 5 throughput of AHWFQ is always better than

WFQ. The difference in throughput values increase as the

number of subscribers increase. In the case of WFQ, The

throughput reaches a constant value when the number of

subscribers is 1250 while AHWFQ reaches a higher

throughput value when the number of subscribers is 1750.

This shows how the AHWFQ adapts itself with the increase

of the traffic.

239

2.5

log-log Cumulative Distribution Function (lIcd)

3.5 4.5 Log File size

5.5 6.5

Figure 4: CDF of Log-transfonned File Sizes

Table 5: Results of Weighted Fair Queuing Scheduling A1gorithm

Number of Subscribers 750 1000 1250 1500 1750 Throughput Mbps 1313.59 1622.30 1825.34 1898.65 1946.03 GoodPut 1278.63 1577.93 1774.61 1847.69 1894.28 Dropped out 2.94% 7.36% 12.90% 19.60% 25.48% Retransmission 8.58% 8.59% 8.05% 6.92% 6.13% Latency 9.25 13.31 16.10 24.88 31.97 Jitter 880.48 1714.14 1805.08 3402.57 4655.50

- Adapt", II"";;,,; Wei!tted Fa, OtJeting - Weiifjed Fa'Q,eting

1210 1500 1750 2000 NurrtJerofSlJbsoibers

Figure 5: Throughput

2000 1994.93 1941.01 30.38% 5.58% 26.24 4026.30

To measure the actual performance we ignored

retransmission and dropped jobs and the protocol overhead.

Hence we calculated the goodput of the two scheduling

algorithms. We have the same pattern as throughput as seen

in Figure 6. Figure 7 shows that the rate of dropping out jobs when

using WFQ increases linearly with the increase of number of

subscribers. Meanwhile, in the case of AHWFQ, the rate of

dropped out jobs is almost constant with the increase of the traffic tell the number of subscribers reach 1500. Then, it

increases linearly with the number of subscribers. Dropped

out jobs is significantly less in the case of AHWFQ compared

with the case of WFQ.

30110

2150

2SOO

! 81250 "

� < � 2000 8 � ( 1750

1500

1210 750

- Adapt", II"";;,,; Wei!tted Fa. OtJeting - Weiifj.d Fa. OtJ.ling

1000 1250 1500 Nun1lOfOf5<t>salbers

Figure 6: Goodput

1750 2000

Retransmission is another important QoS aspect that

affects the whole performance of the network. Retransmission decreases with the increase of subscribers in

the case of WFQ. The reason is due to the increase of

dropped jobs as seen in Figure 7. In case of AHWFQ,

retransmission increases with the increase of the number of

subscribers so long as the dropped out jobs rate is low. But

when the traffic increases and the dropped out jobs increases, the retransmission decrease (Figure 8).

30

10

- � .. �f3",,<OW.g�ed F.lrO,!iI"l

-WeiglrledF,irOtJ!1lrg

/ 750 1000 1250 1500

Nul1'llOfof5<t>salbers

Figure 7: Dropped out jobs

1750 2000

240

The higher rate of dropped out jobs and retransmission in

the case of WFQ scheduling algorithm explain why

throughput is less than AHWFQ scheduling algorithm. The AHWFQ scheduling algorithm has less latency in

scenarios with number of subscribers is less than 1250. When

traffic increase Latency increase and became worse than

WFQ, as seen in Figure 9. Actually, AHWFQ decreases the

number of dropped out jobs as seen previously on the trade

off the latency. This is done by reordering jobs in queues and giving higher priorities for jobs waited in the queue for long

time and is about to reach its time out.

- A<I\pII" Hlemrdico1Wogljod Fair OUeU"l - Weigllled FairOuelil'g

750 1000 1250 1500 1750 2000 NurrIlerof_bers

Figure 8: Retransmission

LICeocy 300

- A<I\pII" Hlemrdico1Wogljod Fair OUeU"l - Weigllled FairOuelil'g

250

i200 �

� f 150 � . " � �100

50

750 1000 1250 1500 1750 2000 NurrIlerof_bers

Figure 9: Latency

Jitter is the variance of the deviation in delay from the

average latency of the network. This can be noticed from

Figures 9, and 10. Both jitter and latency has the same

pattern. Jitter is smaller in the case of AHWFQ when traffic

is small or moderate. When traffic is high jitter of the

AHWFQ is worse but the base station continues delivering

jobs to subscribers. While WFQ jitter is less, but with high dropped out jobs.

- A<I\pII .. Hlem""co1Wog�od Fllr OUeU"l -Weig�od F.irOUeU"l

750 1000 1250 1500 Nurrterof_bers

Figure 10: Jitter

IV. CONCLUSION

1750 20110

In this paper we proposed Adaptive Hierarchical Weighted

Fair Queuing scheduling algorithm and compared its

performance with the Weighted Fair Queuing scheduling algorithm. We found that the AHWFQ has better QoS aspects

with all traffic loads. Dropped out jobs is always less.

Throughput and Goodput are higher. Latency and Jitter are

better. When traffic increases significantly the AHWFQ

adapts the priorities of the queues and the priorities of the

jobs in the queues to avoid reaching their time out limit and

dropping them. This decreases the dropping out rate on the

trade off the Latency and jitter.

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