Scheduling for QoS Management

Scheduling for QoS

Management

Outline

• What is Queue Management and Scheduling?

• Goals of schedul ing

• Fairness (Conservation Law/Max-min fair share)

• Various schedul ing techniques

• Research directions in scheduling

What is Scheduling?

• Packets from multiple flows compete for same outgoi ng link

• Which packets shoul d be given preference?

• How many packets shoul d be transmitted from a flow?

• Simple solution: First come best served

• Complex solution: Provide QoS guarantees

Scheduling Goals

• Sharing bandwidth

• Fairness to competing flows

• Meeting bandwidth guarantees (max and min)

• Meeting loss guarantees (mul tiple level)

• Meeting delay guarantees (mul tiple level)

• Reducing delay variations

Some Definitions

• Flow

– Packets sharing the same source and destination address, source and destination port, same protocol identification are considered to belong to a flow

• Work conserving scheduler

– It is not idle when any of the queues has a packet waiting to be served

– A server can remain idle wasting bandwidth to reduce burstiness of traffic while entering a downstream

network element

– A work conserving server follows the conservation low

Conservation Law

• Definition: Sum of the mean queuing delays received by the set of multiplexed connections, weighted by their share of link’s load is independent of the scheduling discipline – Kleinrock

• Where:

Const i

i

i i

i

N

i

q x å=

=

=

1

r l r

flows of

number

scheduler at

i flow of

time wait mean i

i flow from packets

of time service mean

i

i flow of

rate arrival mean

i

i flow of

n utilizatio mean

i

N q

x

=

=

=

=

=

l r

Example of Conservation Low

• A flow can receive lower delay from a work conserving scheduler only at the expense of another flow

• Example: two sources i and j through a router

– i generates 15Mbps, j generates 45Mbps – Outgoing link speed: 155Mbps

• FCFS scheduling

– Mean queuing delay of 1ms (t_w,i) to each

• Another scheduling discipline

– Mean queuing delay of i: t_w,i= 0.5ms

– What is the mean queuing delay for j (t_w,j) ? r_i =15/155, r_j =45/155;

r_i * 1.0 + r_j * 1.0 = r_i * 0.5 + r_j * t_w,j ; t_w,j = 1.16

Max-Min Fair Share

• Scope

– Fair share allocat ion of resources

• How it works

– Allocates the smallest of all demands from all flows

– Distribute remaining resources equally competing of the flows

• This scheme guarantees that a fl ow either gets what it wants or it is not worse than any other competi ng flow

Max-Min Fair Share

• Let’s assume:

– 1, 2, …k competing f lows

– Each demanding x₁≤ x₂ ≤ … ≤x_k, where the total resource is R unit

• How it works:

– The flow with lowest demand (1) gets R/k unit

– If R/k > x₁, then R/k – x₁ goes back to resource pool – Remaining k-1 flows get additional R/k + (R/k – x1)/(k-

1)

– The process iterates until:

• All resources are exhausted

• All demands have been met

Example of MMFS

• Consider:

– ATM network with outgoing link capacity of 155Mbps

– 5 competing sources with bandwidth: (1)23, (2)27, (3)35, (4)45, (5)55Mbps

• Initially

– The resource is divided equally: 155/5 = 31Mbps each – The first (1) takes 31Mbps

• Then

– The remaining 8Mbps (31-23) are divided equally among the remaining 4 sources (2Mbps each), thus 33Mbps.

– The second source needs only 27Mbps, the residual 6 (33-27) are divided among the remaining 3 sources (2Mpbs each), thus 33+2=35Mpbs each

– The algorithm stops allocation since all of the sources need 35Mbps or more

Scheduling Disciplines

• First come first serve (FCFS)

• Priority (PQ)

• Round Robin (RR)/Weighed round robin

• Deficit round robin (DRR)

• Weighted fair queuing (WFQ)

• Class based queui ng (CBQ)

First Come First Serve

• Packets enqueued into a common buffer

• Server serves packet from front of queue

• No fair sharing of bandwidth

– A greedy source can occupy most of the

queue and cause delay to other flows using the same queue

– TCP flows get penalized (congest ion

sensitive) w.r.t. UDP (no congest ion control)

• No flow isolation

• No priority or QoS guarantee

FCFS example

(a) No buffer occupied

(b) Buffer full, packet dropped

Priority Queuing

• Multiple queues with priority 0 to n-1

• Priority 0 served first

• Priority i served only if 0 to i-1 empty

• Highest priority – lowest delay/loss, highest bandwidth

• Possible starvation of lower class

• FCFS can be used i n each queue

Priority Queue example

The number of queues depends on the supported pri ority levels of the protocol

Generalized Processor Sharing

• Ideal work conserving scheme

• Flows kept in separate queue

• Serve infinitesimal amount of data from each queue

– Serve all active queues in f inite time

• Weight can be associat ed with each queue

– Each queue is served in proportion of its weight

• Achieves max-min fair share

– If there are K active flows, each one gets 1/K_th of a share of max-min resource

– Achieves also max -min weighted f air share

Generalized Processor Sharing

• In GPS terminology, a connection is called

backlogged when it has data present in queue

• Lets assume that there are K flows to be served by a server implementing GPS with weights

w(1), .. w(k) Service rate of i_th flow in interval [τ, t]

is represent ed as R(i, τ,t). For any backlogged flow i in interval [τ,t] and for another flow j, the following equation holds:

) ( /

) ( )

, , ( /

) , ,

( i t R j t i j

R t t ³ v v

t

Generalized Processor Sharing

• Max-Min fair share is achieved by

allocating the residual resource such that i t gets shared by the backlogged connection in proportion of its weight

• GPS is an ideal scheme, because i t serves an infinitesimal amount of data

• Variations can be implemented in real systems

Round Robin

• Flows kept in separate queues

• Serve one packet from each non-empty queue

– Can be seen as GPS where one packet replaces infinitesimal data

• Fair share

– Load balancing among f lows – No advantage to being greedy

• What if packet size variable? No bandwidth guarantee

– Large packet queue gains more bandwidth (long time spent here)

– Not possible dif ferential treatment or specific allocation of bandwidth to specif ic queues

Weighted Round Robin

• Modification to RR

– Allows variable length packet

– Serves n packet from a queue depending on a weight – n adjusted to specif ic fraction of link share

• Assume 3 ATM sources (small cell size) wit h weights 0.75, 1.0 and 1.5. If these weights are normalised t o integer values, each source will be served 3, 4 and 6 cells in each round

Weighted Round Robin

• Needs to know packet size a priori

– Large packets receive more than allocated weight

– Need to adjust weight depending on the mean packet size

• Example:

– Serial link 500MTU, ethernet 1500MTU and FDDI 4500MT U – Weights: 0.33, 0.66, 1.0

– Weight normalized with packet size: 6, 4, 2 packets

• Fairness problem at small time scale

– In the example above, W RR is not fair on a time scale of less than 9000 byte transmission time (72 ms at

1000Mbps)

Deficit Round Robin

• Improves WRR

– Serves variable length packets

– No need to know packet size a priori

• How it works

– Initially serves each queue quantum (queue based) worth of bits – if packet less than or equal to quantum, serve it

else increment deficit_counter (queue based) by quantum – If no more outstanding packet, reset deficit_counter (Why?) – Set quantum to minimum MTU of all incoming links

• Serves more packets at a time if their size is less than the quantum

DRR Example 1

Quantum = 500 for all queues (may have different quantum for each queue)

DRR Example 2

Packet 1 of q1 gets served (500 ≥ 500) => deficit counter = 0 Packet 1 of q3 gets served (500 ≥ 200) => deficit counter = 300 Packet 1 of q4 gets served (500 ≥ 400) => counter = 0

Reset, no outstanding packets

No accumulate credits for a long period)

DRR Example 3

Packet 2 of q1 not served (500 < 700) => deficit counter = 500

Packet 2 of q3 gets served (300+500 > 500)=> deficit counter = 300 Outstanding 400 bytes packet (packet 3)

DRR Example 4

Packet 3 of q1 gets served (500 + 500 > 700) => deficit counter = 0 Packet 3 of q3 gets served (300 + 500 > 400) => deficit counter = 0

Deficit Round Robin

• Set the quantum to serve at l east an MTU of the link

– Ex 1500 bytes for Ethernet

• Fairness problem at smaller time scale

– Shorter than a packet time

Weighted Fair Queuing

• Packets tagged with a value identifying the time last bit of packet should be transmitted using

GPS simulation

• Packet with lowest tag value transmitted by scheduler

• Uses complex finish time calculation

• Hard to implement with variable packet size

• QoS guarantees possible (get s bandwidth in proportion of weight)

å

= Rw(i) / w( j) Throughput

Min

WFQ Delay Bounds

• Delay can be bounded if flows can be policed (token bucket)

• Flows regulated by token bucket are put in dif ferent queues

• Each queue has assigned weight

• With token bucket policing, assume that initially the

token bucket is f ull and a burst of b_i packets arrive for a flow of class i. Last packet to complete service will suffer a maximum delay of d_max given by equation

å

= /( ( ) / ( ))

max b Rw i w j

d ⁱ

WFQ Delay with Token bucket

WFQ - Finish Time Calculation

• Following equation shows the fi nish time calculation where R(t) is called round

number. P^c_m is the time required to

transmit m_th packet from c_th connection and w(c) is the weight of connecti on c.

) (

/ ))

( ,

max(

)

(

F R t P w c

F

=

+

WFQ - Round Number

• This is the number a bit-by-bit round robin

scheduler (in place of GPS’s non-implementable infinitesimal data) has complet ed at a given time

• The round number is a variable that depends on number of active queues to be served (inversely proportional to the number of active queues).

The more queues to serve, the longer a round will take to complete

WFQ – Example

• Assume:

– Three equally weighted connections: i, j, k – Link service rate = 1 unit/s

• Packet details:

– P1: arrival time 0, connection i, size = 2 units – P2: arrival time 1, connection j, size = 2 units – P3: arrival time 2, connection i, size = 3 units – P4: arrival time 2, connection j, size = 1 units – P5: arrival time 3, connection k, size = 4 units – P6: arrival time 4, connection i, size = 1 units

WFQ – Example

• Finish time calculation

– P1: F₁ⁱ = max(0, 0.0) + 2 = 2, where R(0) = 0, first – P2: F₁^j = max(0, 1.0) + 2 = 3, where R(1) = 1, first

– P3: F₂ⁱ= max(2, 1.5) + 3 = 5, where R(2) = 1.5, F₁ⁱ = 2 – P4: F₂^j = max(3, 1.5) + 1 = 4, where R(2) = 1.5, F₁^j = 3 – P5: F₁^k = max(0, 2.0) + 4 = 6, where R(3) = 2.0, first – P6: F₃ⁱ = max(5, 2.33) + 1 = 6, where R(4) = 2.33, F₂ⁱ

= 5

• Rate of round number is controlled by number of active connections

WFQ – Round Number Calculation

• Iterated deletion problem

– Inaccurate estimation of active connections – Done for every packet arrival

0 1 2 3 4 5 6 7

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Class Based Queuing