DRR Adaptive Quantum Scheduling Algorithm for WiMAX Multihop Relay Networks

transcript

DRR Adaptive Quantum Scheduling

DRR Adaptive Quantum Scheduling Algorithmfor WiMAX Multihop Relay Networks

Santos, Einar Cesar / Guardieiro, Paulo Roberto

Federal University of Goias / Federal University of Uberlandia

einar@ufg.br, prguardieiro@ufu.br

June, 2015

Overview

1 Introduction

2 Problem and Motivation

3 Proposal Description

4 Modeling and Simulation

5 Results Analysis

6 Conclusion

Introduction

WiMAX Multihop Relay (MR) Networks provides greatimprovements related to previous specifications like:

Increased coverage area;Reduction of deployment cost;Increase of average system throughput;So on.

Good part of proposed scheduling rules for WiMAX MRdisregards optimal use of basic resources available

DRR algorithm doesn’t considers variation in transmitedpacket size

We propose a DRR with adaptive quantum schedulingalgorithm for dealing with different traffic types

Introduction

Itroduction

We also used a queue management algorithm and acongestion control mechanism to balance average queue size

Problem and Motivation

It is a challenge find the best way to allocate resources withgood fairness

IEEE 802.16 standard doesn’t provide rules to implementresource allocation, leaving to manufacturers

On situations when the packet size is unknown, DRRalgorithm appears as a interesting alternative

To the best of our knowledge, there are few proposalsfocusing WiMAX MR specifications which combines goodscheduling strategies while consider:

Optimal use of available resources;Balance of average queues length;Packet size variation;Fair access to everyone.

Proposal Description

Define a dynamic mechanism to quantify and allocateresources on downlink channel

Resources are adjusted according to congestion stateinformation and average queue length in Relay Station (RS)output buffer

Essentially, four fundamental concepts are considered:

Connection state information;Queue management;DRR scheduling with adaptive quantum;Quantum calculation.

Connection State Information

The RS has ability to send information to BS about theconnection state according to situation (signalling)

Connection state information is obtained according to RSaverage queue length compared with two preset thresholds:maximum and minimum length allowed

Connection State Information

WiMAX frame has an additional subheader known asExtended Subheader Field (ESF)

This subheader has a reserved field of 8 bits used toimplement functions not included in standard

Were used 2 bits to conduct connection state information

Connection State Values

NORMAL (00): indicates a normal network activity withoutcongestion or packet drops;

PRE-CONGESTION (01): the average queue length size isbetween the minimum and maximum length preset thresholds;

CONGESTION (10): points to congestion state and packetdropping;

DROP (11): the adopted queue management algorithmdrops a packet.

Queue Management Algorithm

To balance average queue length was used the AdaptiveRandom Early Detection (ARED) algorithm

ARED is specific to TCP applications but, adjusted intoWiMAX MAC layer

The algorithm defines more flexibility in the dropping packetsprobability due to increase of real traffic

DRR Scheduling With Adaptive Quantum1: while ActiveList not empty do2: removes first ActiveList index3: calculate quantum Qi

4: if Qi > MTU then5: Qi ← MTU6: end if7: DCi ← Qi + DCi

8: while DCi > 0 AND queue is not empty do9: tp ← queue size10: if tp ≤ DCi then11: send queue12: DCi ← DCi − tp13: else14: break loop15: end if16: end while17: if queue is empty then18: DCi ← 019: else20: put i on ActiveList21: end if22: end while

Quantum Calculation

Original Quantum

qorig = MTU3

Adaptives Quantum

Qi =2qorigγCi

∀Ci ∈ Z : Ci > 0

Ci is the average queue length obtained from the i-thconnection state signaling frame sent by RS

γ is an equilibrium constant used to establish a miminumthreshold for adaptive quantum size Qi used

MTU is the maximum threshold

Modeling and Simulation

Modeling and Simulation - Tools

NS-2 Simulator

A modified NIST WiMAX module designed to operateaccording WiMAX MR standard (IEEE 802.16j specifically)

Modeling and Simulation - Scenario

We consider a network operating in Transparent Relay Station(T-RS) mode only and centralized scheduling mode

Was considered to establish a basic network:

1 Base Station (BS) connected to backhaul;1 Relay Station (RS) to increase coverage area;

Variable number of Mobile Stations (MSs), uniformly distributed

around BS and RS.

The aim is to optimize the performance from least amount ofavailable resources

The maximum distance from mobile devices to BS is of 1 km,and the RS is 500 meters distant from the BS, deployed asfixed RS.

Modeling and Simulation - Scenario

Evaluated scenario

RSMS n

Modeling and Simulation - Simulation Parameters

PHY and MAC layer simulation parameters

Parameter Value

Modulation OFDMFrequency 3.5 GHz

Channel Bandwidth 5 MHzFrame Duration 5 msDuplexing Mode TDD

Antenna OmnidirectionalQueue Size - BS 100 packetsQueue Size - RS 50 packets

DL Ratio 0.5UL Ratio 0.5

Propagation Model Two Ray GroundModulation Scheme 64QAM 3/4Gamma Value (γ) 0.04

Modeling and Simulation - Simulation Parameters

Application parameters adopted in service classes

Class of ServiceParameter UGS rtPS nrtPS BE

Application VoIP MPEG-2 MPEG-4 FTP Web

Transmission Rate 64 Kbps (CBR) 48 - 384 Kbps (VBR) 64 - 512 Kbps (VBR) 512 Kbps - 2 Mbps until 1 Mbps

Packet Size 240 bytes Variable until 1400 bytes Variable Variable

Tolerated Delay 150 ms 150 ms 150 ms - -

Tolerated Jitter 30 ms 50 ms 50 ms - -

TCP/UDP UDP UDP UDP TCP TCP

Total simulation time: 110 seconds

Results Analysis

Results were obtained based on average of multiplesimulations, calculated with 95% of confidence intervalTwo situations were analyzed: static quantum and adaptivequantum.

Results Analysis

Total average delay performance for UGS and rtPS classes

10 20 30 40 50

Number of MSs

Static Quantum - UGSAdaptive Quantum - UGS

Static Quantum - rtPSAdaptive Quantum - rtPS

Results Analysis

Total average jitter performance for UGS and rtPS classes

10 20 30 40 50 60 70 80 90 100

Static Quantum - UGSAdaptive Quantum - UGS

Static Quantum - rtPSAdaptive Quantum - rtPS

Number of MSs

Results Analysis

Total average throughput performance for rtPS class withMPEG-2 audio application

10 20 30 40 50 60 70 80 90 100

Adaptive QuantumStatic Quantum

Number of MSs

Results Analysis

Total average throughput performance for rtPS class withMPEG-4 video application

10 20 30 40 50 60 70 80 90 100

Adaptive QuantumStatic Quantum

Number of MSs

Results Analysis

Total average throughput performance for nrtPS class

10 20 30 40 50 60 70 80 90 100

l Avera

Number of MSs

Static QuantumAdaptive Quantum

Results Analysis

Total average throughput performance for BE class

10 20 30 40 50 60 70 80 90 100

l Avera

Number of MSs

Static QuantumAdaptive Quantum

Conclusion

Results demonstrate better performance of DRR withadaptive quantum scheduling algorithm over conventionalwith static quantumThey also demonstrated an increase in total averagethroughput performance with adaptive quantum algorithm:

Up to 37.5% for nrtPS class27% for BE class4% for rtPS class with MPEG-4 video application in the worstcase

With MPEG-2 video application and low traffic demand,performance matches for both algorithms: static and adaptivequantumAs improvement of this work, we suggest an optimizationmethod for VoIP traffic (UGS) with DRR adaptive quantumalgorithm

Conclusion

Thank you!

DRR Adaptive Quantum Scheduling Algorithm for WiMAX Multihop Relay Networks

Documents