Packet Schedulingfor QoS management

TELCOM2321 – CS2520Wide Area NetworksDr. Walter Cerroni

University of Bologna – ItalyVisiting Assistant Professor at SIS, Telecom Program

Slides partly based on Dr. Znati’s material

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Multi-service network

• Single network infrastructure carrying traffic from different services (data, voice, video...) in an integrated way

• Different quality of service requirements to be met

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Performance bounds

• Deterministic– holding for every packet sent on a connection

• Statistical– probability that a packet violates the bound is less

than a specified value– one-in-N bound, no more than one packet in N

consecutive packets violates the bound

• Common performance bounds– minimum bandwidth– maximum loss– maximum delay– maximum delay jitter

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Performance bounds: Delay jitter

• Delay jitter bound requires that the network limit the difference between the largest and the smallest delays experienced by packets

dela

y pr

ob. d

ensi

ty fu

nctio

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delay

propagationdelay

jitter

averagedelay

worst-casedelay

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Performance bounds: Delay jitter

• Delay jitter bound is useful for audio/video playback, where receiver can compensate delay variations– delaying the first packet by the delay-jitter bound in an elasticity

buffer– playing back packets at a constant rate from the buffer

Time

CBR playbackat the client

playbackbuffer delay

variablenetwork delay

CBR streamingat the source

Cum

ulat

ive

data

reception at the client

buffe

red

data

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Performance bounds: Delay jitter

• Playback buffer delay must be appropriate– if too short, it will cause losses– if too large, it will affect interactivity

Time

Cum

ulat

ive

data

loss

too large e2e delay

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The role of the network

• The network should have enough resources available to be able to satisfy the performance bounds required by the application– admission control– congestion control

• The network should efficiently manage the resources available in order to guarantee a quality of service acceptable to the application– packet scheduling at intermediate nodes

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Packet scheduling

• Packets are queued at intermediate nodes– store and forward

• The server on each queue must– decide which packet to service next– manage the queue of backlogged packets

• The server uses a scheduling discipline to– fairly share the resources– provide performance guarantees

• Trade-off between– traffic isolation

• circuit switching: guaranteed dedicated resources

– resource sharing• packet switching: complete resource sharing

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Scheduling policy requirements

• A scheduler should be– not too expensive in terms of required hardware– scalable

• the number of operations to implement a discipline should be as independent as possible of the number of scheduled connections

– fair• it should allocate a fair share of the link capacity and output

queue buffer to each connection

– protective• the misbehavior of one source should not affect the

performance received by other sources

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The simplest scheduler: FCFS or FIFO

• First Come First Served or First In First Out• Packets are served in the same order as they arrive• Most commonly used scheduling discipline• Example

– M/G/1 queue: Poisson arrivals, generic service time

• Stability condition on server utilization• Probability of idle server state

queue server

arrival rate departure rate

averageservice time

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M/G/1: Average waiting time

• Each packet arriving at the queue– is served immediately, if server is idle

• waiting time = 0 with probability

– must wait for the packet currently being served to finish its service, if server is busy

• waiting time = residual service time with prob.

• average residual service time:

– must wait also for all packets previously queued to finish theirservice, if queue is not empty

• waiting time = sum of service times for each queued packet• average number of packets in the queue (Little’s theorem):

• Average waiting time

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M/G/1: Average waiting time

• Solving for :

– the average waiting time depends on arrival rate, server utilization (i.e. load) and service time distribution

• Exponential services: M/M/1

• Deterministic services: M/D/1

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Limitations of FCFS

• All packets are serviced the same way• No consideration for delay sensitivity

– small packets must wait for long packets to be serviced

• Unfair– connections with large packets usually receive better services by

getting higher percentage of server time

• Not protective– greedy connections are advantaged over friendlier connections– an excessively active connection may cause other connections

implementing congestion control to back off more than required

• The cause is complete resource sharing without traffic isolation– isolation through separate queues for different traffic flows or

classes sharing the same bandwidth

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Separate queues

• Flow with arrival rate and average service time• server utilization due to flow packets

• Stability condition:

...

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Priority queuing

• priority classes

• Class with arrival rate and average service time

• Class 1 has highest priority, then class 2, then class 3, ...

• Low-priority packets are serviced only when there are no packets of higher priority waiting to be serviced– FCFS within the same class– arrivals of high-priority packets do not stop current service of low-

priority packets (non-preemptive)

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M/G/1 with priorities: Average waiting time

• Each class packet arriving at the queue– is served immediately, if server is idle

• waiting time = 0 with probability

– must wait for the packet currently being served to finish its service, if server is busy

• average residual service time:

– must wait for all packets of class previously queued to finish their service

• average number of class packets in the queue:each requiring average service time

– must wait also for all packets of class that have been received during its waiting time to finish their service

• average number of class packets arrived during class packetwaiting time:each requiring average service time

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M/G/1 with priorities: Average waiting time

• Average class waiting time

• Solving for

• Same expression in recursive format (Cobham’s formula)

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Example: 2 classes

Assuming identical exponential services for both classes and

we get

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Example: 2 classes

0

2

4

6

8

10

12

14

16

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0 0.2 0.4 0.6 0.8 1

no priorities

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Limitations of priority queuing

• Effective only when high priority traffic is a small part of the total traffic– efficient admission control and policing may be required

• Not protective– a misbehaving source at a higher priority level can increase delay

at lower priority levels

• Unfair– starvation of low-priority queues in case of heavy high-priority

traffic

• More sophisticated scheduling disciplines are required– better traffic isolation– fairer resource sharing

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Conservation Law

• Traffic isolation while sharing resources is limited by the conservation law

• The sum of the mean queuing delays received by the set of multiplexed connections, weighted by their share of the link's load, is independent of the scheduling discipline

• In other words, a scheduling discipline can reduce a particular connection's mean delay, compared with FCFS, only at the expense of another connection

• Valid if server is idle only when queues are empty – work-conserving schedulers

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Non-Work Conserving Schedulers

• A non-work conserving scheduler may idle even when packets are currently waiting for service

• A non-work conserving scheduler can reduce delay jitter if packets are only serviced at their eligibility times– packets are serviced no faster than the allowed rate– in case of exceeding bursts, non-eligible packets are retained– introducing idle time makes the traffic more predictable– bursts do not build up within the network

• A(k), arrival time for k-th packet• E(k), eligibility time for k-th packet• XMIN, inverse of the highest allowed packet rate• E(k+1) = max { E(k) + XMIN, A(k+1) }

• Bandwidth wasted during forced idle times• Jitter reduced at the cost of increased average delay

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Max-Min fair share

• Resources should be shared in a fair way, especially when they are not sufficient to satisfy all requests

• Max-Min fair share– bandwidth allocation technique that tries to maximize the

minimum share for non-satisfied flows

• Basic principles– resources are allocated in order of increasing demands– users with relatively small demands are satisfied– no user gets a resource share larger than its demand– users with unsatisfied demands get an equal share of unused

resources

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Max-Min fair share

1. Order demands d1, d2, ..., dN such that d1 ≤ d2 ≤ ... ≤ dN

2. Compute the initial fair share of the total capacity C:FS = C / N

3. For each demand dk such that dk ≤ FS allocate the requested bandwidth

4. Compute the remaining bandwidth R and the number of unsatisfied demands M

5. Repeat steps 3 and 4 using the updated fair share FS = R / M until no demand left is ≤ FS

6. Allocate the current FS to remaining demands

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Max-Min fair share: Example

1. Demands: d1 = 23, d2 = 27, d3 = 35, d4 = 45, d5 = 552. Bandwidth: C = 1553. FS = 155 / 5 = 314. d1 and d2 satisfied, M = 35. Bandwidth left: R = 155 – (23 + 27) = 1056. FS = 105 / 3 = 357. d3 satisfied, M = 28. R = 105 – 35 = 709. FS = 70 / 2 = 3510. d4 and d5 not satisfied � they get 35

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Scheduling and admission control

• Given a set of currently admitted connections and a descriptor for a new connection request, the network must verify whether it is able to meet performance requirements of new connection

• It is also desirable that the admission policy does not lead to network underutilization

• A scheduler define its “schedulable region” as the set of all possible combinations of performance bounds that it can simultaneously meet– resources are finite, so a server can only provide performance

bounds to a finite number of connections

• New connection requests are matched with the schedulable region of relevant nodes

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Scheduling and admission control

• Schedulable region is a technique for efficient admission control and a way to measure the efficiency of a scheduling discipline– given a schedulable region, admission control consists of

checking whether the resulting combination of connection parameters lies within the scheduling region or not

– the larger the scheduling region, the more efficient the scheduling policy

No. of class 1 requests

No. of class 2requests

(C1,C2)

(C1MAX,0)

schedulableregion

(0,C2MAX)