page 1 scheduling, diversity and qos issues scheduling, diversity and qos issues madrid, 4 november...

Scheduling, Diversity and QoS IssuesScheduling, Diversity and QoS Issues

Madrid, 4 November 2005

A. Gameiro

Univ. Aveiro / Inst. Telec.

Outline

Introduction Diversity

Multiuser Diversity

Scheduling algorithms Opportunistic scheduling

The problem of QoS

Scheduling approaches to cope with QoS Categories

Issues

Framework based on the utility function

Comments and conclusions

Introduction - Diversity

Main characteristic of wireless networks randomness Network topology is not fixed (users move, enter, leave the network...)

Link characteristics vary with time , position

This randomness can however be used to provide diversity

Classical forms of diversity Time

Frequency

Space (antenna)

Typically this sort of diversity is exploited on a point to point link level basis

Multiuser diversity I

When viewing the downlink at the network level K independent links corresponding to the different active users

If for BS if the goal is to maximize the DL throughput

» Then there are K independent links available

possible to exploit the randomness of the different links, by at a given time transmitting through a good quality link

multiuser diversity (Knopp & Humblet)

Exploited at the network level and not on a point to point basis

Fading channel

Mobile user

h1(t)hK(t)

Multiuser diversity II

Cha

nnel

gai

n

time

ch1ch2ch3best

• System with high number of users and independent channels

high probability that at any time there is a good quality channel available

• Long term total throughput can be maximized by always using the best channel

Example

The higher the number of users / channel variability the higher diversity we have

Some schemes can be used to induce / enhance diversity random beamforming with dumb antennas

Packet Scheduling and Multiuser Diversity

Downlink packet scheduling

Fading channel

Mobile user

h1(t)hK(t)

...

Flow 1 Flow K

Radio block data or packet

• Objective: transmit the data blocks in queues 1....K to mobiles 1...K, through hi(t)

• Inherent multiuser diversity in the downlink of cellular can be directly exploited

• In each time slot transmit the packets in the queue associated with the best quality channel

• Opportunistic Scheduling

Opportunistic scheduling I

Independence of the links and time variability allows Achieve maximum throughput by opportunistically scheduling the transmissions

» Save power, learn to recognize the opportunities and seize the chance when it arises

Diversity increases with the number of users More users better channel available

What are the issues? Issues are a consequence of the goal implicitly associated with the algorithm

Goal throughput maximization

if quality of communication or network performance involves other parameters no guarantee that the algorithm performs well considering eventual additional parameters

This makes it applicable for best effort packet services

If the quality of communication involves parameters other that the correct delivery of bits algorithm must be reanalyzed

Quality of Service I

Trends in telecommunication networks Provide end to end quality of service

» different traffic types get different levels of network service

Characteristics used for QoS Bandwidth allocation (bandwidth: misnomer for data rate)

Delay bound

Jitter bound (Jitter = variation in delay)

Loss rate

Quality of service II

What are the issues associated with QoS provision? Isolation / sharing

» Provision of individualized quality communication guarantees facilitated if different flows are isolated

Isolation is inherent in circuit switched networks

but

in the current Internet (IP) all flows share all resources at the packet level.

» Ensuring QoS requires isolation

» However too much isolation lower the resource utilization

To support QoS in IP network, need to emulate the traffic isolation while sharing resources at the packet level

Delay bounds

» IntServ requires scheduling to support delay bounds.

» Delay bounds reflect the trade-off between isolation and sharing.

Quality of Service III

Provision of QoS services generally involve putting in place mechanisms that ensure Fairness - access to network resources

Isolation - protection from excessive usage of network resources from other users

Subject to general goals of

Efficiency

Complexity

Quality of Service V

To reach these goals in packet switched networks requires collaboration of many components Admission Control

Scheduling

» Which packet gets transmitted first on the output link significantly impacts QoS guarantees for different flows.

• Scheduling affects delay, jitter and loss rate.

• Allows protection against misbehaving flows.

Buffer Management

Congestion Control

Opportunistic Scheduling and QoS I

How does the opportunistic scheduling algorithm answer the generic QoS requirements?

» Fairness / Isolation

• This is greedy algorithm users with good channels can starve users with bad channels

» Delay

• Over a large time span any user will be scheduled but there is no control of the maximum delay

This makes it applicable for best effort traffic

Opportunistic Scheduling and QoS II

Even the throughput maximization is only guaranteed for full queue situations

If this is not the case a user with a high quality channel but momentarily low traffic may be using resources without need

A max-min strategy would give better throughput results

» In time slot n, scheduler selects user k such than

))(),((minmaxarg)( nQnRnk jjj

Rj(n) number of bits that can be transmitted within slot n over channel jQj(n) length (in bits ) of queue j prior to slot n

QoS and Scheduling

Scheduling is a major component in the QoS scheme

There are conflicts in a packet switched network between The goal of sharing resources

The need to provide some flow isolation and fairness to guarantee QoS

Scheduler designed to maximize throughput does not answer these objectives

But these goals and implementation problems are not specific of wireless networks also exist in the wired world to fullfill the quest of QoS provision

Are there solutions that can be imported?

QoS and scheduling – Wired vs Wireless I

There are some fundamental difference between wired and wireless networks Wired networks: can assume time-invariant physical links in most cases

» Packet schedulers use information from the upper layers(QoS requirements, forwarding policies,…) to decide about which packets should be transmitted

Scheduler

PHY layer

Upper layersRead QoS requirements / packet attributes, get forwarding policies

QoS and Scheduling – Wired vs Wireless II

What happens with wireless networks? Dynamic topology: user moves around, also enter and leave

Quality of the wireless channel is typically different for different users, and randomly changes with time (on both slow and fast time scales).

Wireless bandwidth is usually a scarce resource that needs to be used efficiently (can not overprovision the wireless link).

Excessive amount of interference and higher error rates are typical.

A scheduling algorithm that does not account for this variability of the channel will have low efficiency would be for most scenarios very poor

wireless networks require scheduling algorithms that use the PHY layer information

QoS and Scheduling IV

Opportunistic scheduling includes channel information by using the channel quality indicator to schedule packets, but does not use any information from the upper layers concerning the nature and requirements of the traffic It represents the opposite case of common wired scheduling algorithms

Scheduler

PHY layer

Upper layers

Get Channel State Information

QoS and Scheduling V

To provide QoS in packet switched wireless networks a cross-layer design approach is needed to design schedulers Service requirements have to be taken into account

Physical layer information needs also to be considered

Scheduler

PHY layer

Upper layers

Get Channel State Information

Read QoS requirements / packet atributes, get forwarding policies

Scheduling Schemes for QoS I

What have been the proposals design schedulers able to support QoS ?

Proposals fit in two main classes

Class 1

Algorithms only use Phy layer information but process it so that QoS related parameters performance will improve

Main goal: to improve fairness

With improved fairness, delay statistics (max, jitter) expected to improve

Class 1

Algorithms only use Phy layer information but process it so that QoS related parameters performance will improve

Main goal: to improve fairness

With improved fairness, delay statistics (max, jitter) expected to improve

Class 2

Algorithms expliciltly use QoS requirements

Algorithm can then be designed to match some QoS related parameter

Class 2

Algorithms expliciltly use QoS requirements

Algorithm can then be designed to match some QoS related parameter

Class 1 Scheduling algorithms for QoS I

Class 1 Best known algorithm

Proportional fair scheduling

In time slot n, scheduler selects user k such than

Rk(n) rate channel k can support

Tk(n) average throughput over given window of user k

» Can be updated using more sophisticated filtering

Variant

))(

)((maxarg)(

nT

nRnk

j

j

j

)(

)(maxarg)(

n

nnk

i

i

i

Proportional Fair Scheduling I

Proportional fair scheduling

Provides fairness

» In the long term channel usage is 1/K for each user, even if the average SINR’s are different

» Translates in better delay properties

» Can be easily adapted to different classes of traffic by applying different weights

Problems Fairness only achieved in the long term

No absolute guarantee concerning delay

Even with fairness potential instability problems

))(

)((maxarg)(

nT

nRwnk

j

jj

j

Strictly speaking the use of weights specified by the upper layers would move the algorithm to class 2

Stable scheduling algorithm means all queues bounded

Class 1 Scheduling algorithms for QoS II

Other class 1 approaches “Force” TDMA like component (Bettesh & Shamai)

Improvement of Round Robin scheduling and some variants of it through channel awareness

Class 2 Scheduling Schemes - Concept

Class 2 approaches Concept

Scheduler

PHY layer

L3

Channel estimation

Provide CSI

QoS requirements

Read QoS requirements / packet atributes

Takes decisions in order to optimize some cost / revenue function f(QoS,CSI)

Class 2 Scheduling Schemes I

What are the difficulties with this approach? Definition

» What is an appropriate cost or revenue function?

Implementation

» Optimum implementation quickly degenerates in a very complex optimization problem if many parameters (CSI and QoS) are considered

Class 2 Scheduling Schemes II

Utility function based class 2 approach

Considers that associated with each packet there is an utility function

is the packet delay, and to consider different classes of service one can define different functions Ui(.), i=1,...L.

Utility decreasing with the delay

)(iU

delay

utili

ty

Traffic moderately sensitive to delay

Bounded delay traffic

Class 2 Scheduling Schemes III

Previous work based on individual utility functions

Algorithm selects packets of queue that maximize

» i(n), delay of the head of line packet of user (queue) i at the beginning of time slot n

» Ri(n): offered rate by channel I

Algorithms that fit in this class

» Weighted delay

» Weighted queue

)())((maxarg)( , nRnUnk iiii

Although very simple these two algorithms are shown to be stable

(Stable means the scheduling algorithm must be able to keep all queues bounded)

Class 2 – The utility function

Either weighted delay or weighted queue algorithms leads as in the max(C/I) algorithm to a greedy algorithm

Why?

» The utility function is related to a single user

• Not guaranteed that what is best for a given user translates in benefices for all

• The utility function which in the opportunistic scheme was the channel rate was replaced by another one involving the delay or queue length but again this is an individual function for each user

Utility function - Comments

Utility function

Why is an individualized utility function not appropriate?

» If there is a decision to send data to a given user translates in a benefit to this particular user there is also cost in deferring the transmissions for other users

» If packets have delay bounds

• Deferring transmission reduces the number of opportunities to transmit increasing the probability that a packet will be lost

» Even if there are no strict bounds but jitter tolerance the usefulness of the packet will be lower

• Jitter will affect the overall quality

What would then be needed ?

» A global utility function, that accounts for the whole set of active users, and includes the costs associated with the non-scheduled users

Generalization of the utility function I

Basic idea

1. At the beginning of time slot n, the packets in the queues represent a certain amount of utility

The total potential utility

This represent the utility that will be achieved if they all the packets are transmitted successfully while fulfilling any other constraints (delay, jitter,…) that may exist

2. If user j is scheduled for time slot n, then an amount of utility

is transferred to user j

Rj(n) number of radio blocks (packets) than cam be transmitted through channel j during slot n

Qj(n): vector representing the state of the queue j at the beginning of time-slot n

)(nU p

))(),(( nnRU jjj Q

)|1( jnU Tp

That is we start with an amount of potential utility

Transfer part of it to user j

And are left with the remaining utility

Generalization of the utility function II

3. Assuming user j has been selected, the packets not transmitted have their delay increased by one and the remaining potential utility associated with the queues Qj(n+1|j) is

)(nU p

)|1( jnU p

))(),(( nnRU jjj Q

)|1( jnU p

Generalization of the utility function III

Intuitively one would expect than for an appropriate definition of potential utility, the sum of the transferred utility and the remaining potential utility will not exceed the original potential utility

We expect that a good decision will minimize the loss of utility which leads to the scheduling decision

Although this framework is perfectly general, the key point is to find utility functions that are meaningful for the network operator and do not lead to excessive computations

)()|1())(),(( nUjnUnnRU ppjjj Q

)|1())(),((maxarg)( jnUnnRUnk pjjjj

Q

Utility functions

Simple utility functions

Delay constraints

Jitter constraints

L

i

ijjjj UU

1

)( )()( Q

L: number of packets in the queue

: delay associated with the ith packet in the queue

packet 1 head of line

Uj(.): non-increasing function

)(ij

)1()2()1(jjj

)(max)2()()( )1( xULUU jjjjjj Q

j(.): Time elapsed since the last packet of queue j has been transmitted successfully

Examples I

Single service, delay sensitive (bounded)

Utility function

0 5

-1

0

1

delay (time slots)

utili

ty

After delay exceeds 5 packet is dropped, but in the computation of the remaining utility counted

Value of -1 for the cost of dropped packet is arbitrary. Must be fixed by operator and depends on the specific service

Scenario

20 users, fixed length packets; Poisson arrival rate with intensity packets/ time slot

Independent channels specified by a packet error rate given by 10 -x (x uniformly distributed in [-3,0])

Examples II

0 5 10 15 20 25 30 35 400.4

0.5

0.6

0.7

0.8

0.9

1

delay (time-slots)

cd

f

utilitymax C/I non drop

Delay cdf for some values of intensity (arrival rate per user)

=0.04 80% load (with perfect channels)

=0

0 10 20 30 40 50 600.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

delay (time-slots)

cdf


=0.046 92% load (with perfect channels)

=0

Examples III

0.03 0.032 0.034 0.036 0.038 0.04 0.042 0.044 0.046 0.0480.8

0.82

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

arrival rate / user

no

rma

lize

d t

hro

ug

hp

ut

utilitymax C/I with drop

Normalized throughput: ratio between the number of correctly received packets for the specific algorithm and the number of correctly received packets with a max(C/I) algorithm (without packet dropping)

Examples IV

Scenario

20 users, fixed length packets; Poisson arrival rate with intensity l packets/ time slot

Independent channels specified by a packet error rate given by 10 -x

» For 10 channels x uniformly distributed in [-4,-2]

» For 10 channels x uniformly distributed in [-2,0]

0 5 10 15 20 25 30 35 40 45 500.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

delay (time-slots)

cdf


0 10 20 30 40 50 60 70 80 90 1000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

delay (time-slots)

cdf


=0.04 80% load (with perfect channels)=0

=0.046 92% load (with perfect channels)=0

Framework for scheduling in wireless networks

Scheduling in wireless networks with QoS support

What do we have Different classes of services specific forwarding policies to be provided by the

upper layers

Random traffic patterns variable traffic states at each instant

Diversity inherent at the PHY layer

objective: match the traffic and PHY layer patterns subject to the forwarding policies

SchedulerPolicies Traffic state

CSI Maximizes the utility for the operator

Comments – Conclusions

Discussed issues related to packet scheduling, diversity and QoS requirements in PS wireless networks

Wireless networks have inherent diversity In best effort packet traffic can be fully exploited maximization of throughput

But

trends towards QoS provision need for some flow isolation implies that sharing to maximize throughput is no longer optimum

Approaches towards QoS provision Cross-layer approach optimization of cost / revenue function involving parameters from higher

and PHY layers

With QoS and the need to meet very different targets for different traffics Utility of bits may be different from user to user / application to application

Metrics other than the throughput may be needed

Concept of global utility involving, policies, traffic conditions and CSI presented and discussed

page 1 scheduling, diversity and qos issues scheduling, diversity and qos issues madrid, 4 november...

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