Proportional differentiations provisioning Packet Scheduling & Buffer Management

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Proportional differentiations provisioning Packet Scheduling & Buffer Management

Yang ChenLANDER CSE Department

SUNY at Buffalo

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Outlines

Motivations and termsProportional differentiationImplementations and related issuesConclusion and Future works

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Quality of Service (QoS)

What is QoS? A measurement of how well the network

behaves and an attempt to define the characteristic and properties of specific services.

Who need QoS? User:

More applications have strict service requirements: low packet loss rate, short delay, etc;

Network operator: Resource in a network must be used efficiently;

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Intserv

Integrated Service Try to achieve per-flow, end to end

service guarantees; Per-flow state is kept at intermediate

router; Admission control, resource

reservation and corresponding signaling are required;

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Diffserv

Differentiated Service Aggregate individual flows with

similar QoS requirements; No complex signaling; Can be implemented gradually (on

the congested links);

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Differentiated Service

Absolute (quantitative) Provide a macro-flow with a

quantitative performance level.

Relative (qualitative) Provide a number of classes with

increasing performance.

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Primary Tradeoffs

Fairness Access to excess capacity

Isolation Protection from excess traffic from other users

Efficiency Number of flows that can be accommodated

for a given level of service

Complexity In terms of implementation and control

overhead

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QoS metrics of interest in packet networks

Average packet delayPacket loss rateDeadline violation probabilityJitterEtc….

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Scheduling and buffer management

Scheduling Support service differentiation on bandwidth

by controlling the actual transmission of packet.

Take effect on time-related QoS metrics.

Buffer management Support service differentiation on buffer by

deciding which packet can be stored for future transmission.

Take effect on loss-related QoS metrics.

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Outlines


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Proportional Differentiation

DefinitionIf qi is the QoS metric of interest, and si is the differentiation factor for class i, we have:

)...1,( Njis

s

q

q

j

i

j

i

For example: Given two classes 1 and 2, and the QoS metric is packet loss rate, s1=1; s2=2, the packet loss rate of class 2 should be twice that of the loss rate of class 1.

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Proportional Differentiation

Pros Controllable

Differentiation level between service classes can

be controlled by network operator; Predictable

Performance of higher classes is consistently better than the performance of lower Class

even in short time scale;

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Outlines


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Recall: QoS metrics of interest

Average packet delayPacket loss rateDeadline violation probabilityJitterEtc….

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Proportionally differentiated packet delay

Waiting Time Priority (WTP) Scheduling

One packet need to be scheduled

On-line priority measurement

is done

Class 0

Class 1

Class N

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Class 0

Class 1

Class NClass 1 has the highest priority


Waiting Time Priority (WTP) Scheduling

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Wait Time Priority (WTP) Scheduling Suppose class i is backlogged at time

t, and that wi(t) is the head waiting time of class i at t;

We have normalized head waiting time of class i at t as:

When a packet need to be scheduled, a backlogged class j is selected for

iii stwtw /)()(~

)(~max arg)(

twj itBi

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Proportional Average Delay schedulingHybrid Proportional Delay schedulingBacklog Proportional Rate schedulingEtc….

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Proportionally differentiated loss rate

Buffer Management

Class 0

Class 1

Class 2

Total buffer size 20

One packet arrives

On-line priority

measurement is done

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Buffer Management

Class 0

Class 1

Class 2


Class 0 has the lowest priority

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Buffer Management

Class 0

Class 1

Class 2


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Proportional Loss Rate (PLR) dropper Suppose there are two counters for each

class i, counter ai records packet arrival history of class i, counter di records packet drop history of class i;

We have normalized packet loss rate of class i as:

When a packet needs to be dropped, a backlogged class j is selected for

)/(~

iiii sadl

)(~

min arg)(

tlj itBi

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PLR() Using the entire packet loss history

PLR(M) Using the most recent M packet entry

PLR with active resetting Using the most recent packet entry with variable

history length within a limited deviation on proportional relations

Predicting the average drop distance di is the average number of successfully forwarded

packets between two packet drops, loss rate li is 1/di;

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Loss rate and Packet delay

Fluid flow assumption Service rate of class i is ri; Loss rate of class i is li;

An optimization problem is formulated with Objectives:

Minimum service rate changes ri; Minimum loss rate li;

Constraints: Proportional relations on loss rates and packet

delays of different service class;

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Deadline violation probability

Motivation Performance of multimedia applications do

not depend on average delay much but on the probability that the transmission delay exceeds a certain threshold

Deadline Each class i is associated with a delay bound

i. A packet of class i arriving at time tA will

receive a tag tA+ i as its deadline.

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Earliest Deadline First (EDF)/Earliest Deadline Due schedulerShortest Time to Extinction (STE) schedulerCons: Only provide different deadline for each

service class, no differentiation for deadline violation probability, which is an important factor on some real-time application’s performance, e.g., Voice over IP.

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Weighted EDF/EDD Provides differentiated deadline

violation probability.If the scheduler is in “congested mode” ,

WEDF scheduler is applied

Class 0

Class 1

Class N

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“Congested Mode” There are more than one backlogged

class with the first packet with a deadline tA+i<ts+i (ts is the system time, i is a safety margin, e.g., i = i/10).

WEDF scheduler In “congested Mode”, a class j with

largest normalized measurement-based deadline violation probability is served.)/)((max arg

)(ii

tBistvpj

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Proportionally differentiated Jitter

Jitter Jitter of one packet is the difference of

this packet’s queueing delay and the delay of preceding packet.

Motivation Jitter will affect the performance of

both interactive and non-interactive applications involving digital continuous media.

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The long time average jitter for served packets of each class is recorded as ji*(t);The minimum jitter for all the packets in the queue is calculated as jimin(t)The average jitter for class i is:

)}()({

)()()(

min*

tqtn

tjtjtj

ii

ii

Where: ni(t): the packet of class i been served; qi(t): the packet of class i in the queue.

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Normalized average jitter

When a packet need to be scheduled, a backlogged class j is selected for

iii stjtj /)()(~

)(~

(max arg)(

tjj itBi

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Problems in the implementation

Problems Delay/Jitter differentiation

Difficult to provide accurate proportional differentiation on both long time and short time periods;

Hybrid solution will introduce extra computation; Loss rate/violation probability

Keeping the entire loss/violation history will give accurate only on long term average;

Keeping the most recent history will help the system to achieve accurate differentiation on short time period but requires extra hardware and operation.

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Feasibility Problem in this QoS model

Feasible A set of proportional factors is

feasible when there exists a work-conserving scheduler that can set the differentiation level as this set specifies.

Feasibility depends on traffic profile: total load and percentage of each class.

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Analysis on average delay Conservation Law

N

iagiii qLd

1

N

n nnn

agii

Ls

qsd

1

Assume all classes have the same packet size distribution as 1.

N

n nn

agii

s

qsd

1

nn sssddd :::: 2121

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Analysis on average delay (cont.) There is a lower bound for delay of each

class. This lower bound would result if that

class was given strict priority over the rest of the traffic

Given a steady traffic profile, one method has been proposed to figure out the feasible region of proportional factors

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Assume all classes have the same packet size distribution. The necessary and sufficient feasibility conditions are N-1 inequalities

NkddN

ki

N

kii

SPNkii ,,2 ,

Where are the average delay for service classes from k to N, which are given the strict priority over all other Service classes. All the values of can be achieved either experimentally or theoretically.

SPNkd ,

SPNkd ,

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Feasibility Problem in this QoS model Assume there are two service classes:

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Outlines


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Conclusion

Proportional differentiation is versatile. This QoS model can be implemented on

various QoS metrics;

Proportional differentiation is controllable. The level of differentiation can be adjusted by

setting different proportional factors;

Proportional differentiation is predictable. It can keep the proportional relations even in

short time period;

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Conclusion

However In order to provide finer

differentiation, as a tradeoff, complexity increases in terms of implementation and control overhead.

Infeasibility situation exists on some traffic profiles with no efficient solution.

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Future worksFeasibility testing How to judge whether the proportional factors

are properly in a dynamic traffic condition?

Class selection How to selection a service class for a particular

traffic flow in order to fulfill end-to-end/absolute QoS requirements?

Class provisioning Given traffic conditions and proportional

factors, how much resource shall we provide?

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Main ReferencesC. Dovrolis and D. Stiliadis and P. Ramanathan“Proportional Differentiated Services: Delay Differentiation and Packet Scheduling.”C. Dovrolis and P. Ramanathan“Proportional Differentiated Services, Part II: Loss Rate Differentiation and Packet Dropping.”J. Liebeherr and N. Christin“Buffer Management and Scheduling for Enhanced Differentiated Service”S. Bodamer“A New Scheduling Mechanism to Provide Relative Differentiation for Real-Time IP Traffic.”T. Quynh, et al. “ Relative Jitter Packet Scheduling for Differentiated Services”

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Proportional differentiations provisioning Packet Scheduling & Buffer Management