Scheduling per QoS - sti.uniurb.it Didattico/QoS Scheduling.pdfSistemi di Comunicazioni Multimediali...

Legal Informatics – Computer law01 (Information Society

Domenico Condello isti information science and technology institute 1/number

Università degli studi di Urbino “Carlo Bo”Informatica ApplicataSistemi di Comunicazioni Multimediali

Massimo Parrucci Facoltà di Scienze e Tecnologie 1

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 di rections in scheduling





What is Scheduling?

• Packets from multiple flows compete for same outgoing link

• Which packets should be given preference?

• How many packets should be transmi tted 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:Constii

iii

N

iq

x

∑=

=

=

1ρ

λρ

flowsofnumber

scheduleratiflowoftimewaitmeani

iflowfrompacketsoftimeservicemeani

iflowofratearrivalmeani

iflowofnutilizatiomeani

Nqx

=

=

=

=

=

λρ





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 (tw,i) to each

• Another scheduling discipline– Mean queuing delay of i: tw,i= 0.5ms– What is the mean queuing delay for j (tw,j) ?

ρi =15/155, ρj =45/155;ρi * 1.0 + ρj * 1.0 = ρi * 0.5 + ρj * tw,j ;tw,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 competing flow





Max-Min Fair Share• Let’s assume:

– 1, 2, …k competing f lows– Each demanding x1≤ x2 ≤ … ≤xk, 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 queuing (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 fi rst• 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 priority 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/Kth 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 ω(1), .. ω(k) Service rate of ith flow in interval [τ, t] is represented as R(i, τ,t). For any backlogged flow i in interval [τ,t] and for another flow j, the following equation holds:

)(/)(),,(/),,( jitjRtiR ϖϖττ ≥

τ





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 specif ic

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 to 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 4500MTU– 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 µs 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 = 0Packet 1 of q3 gets served (500 ≥ 200) => deficit counter = 300Packet 1 of q4 gets served (500 ≥ 400) => counter = 0

Reset, no outstanding packetsNo accumulate credits for a long period)





DRR Example 3

Packet 2 of q1 not served (500 < 700) => deficit counter = 500Packet 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 = 0Packet 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 smal ler 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 calculat ion• Hard to implement with variable packet size• QoS guarantees possible (get s bandwidth in

proportion of weight)∑= )(/)( jwiRwThroughputMin





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 full and a burst of bi packets arrive for a flow of class i. Last packet to complete service will suffer a maximum delay of dmax given by equation

∑= ))(/)(/(max jwiRwbd 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. Pc

m is the time required to transmit mth packet from cth connection and w(c) is the weight of connecti on c.

)(/))(,max( 1)( cwPtRFF cm

cmm

c += −





WFQ - Round Number

• This is the number a bit-by-bit round robin scheduler (in place of GPS’s non-implementable infinitesimal data) has completed 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 calculat ion– P1: F1

i = max(0, 0.0) + 2 = 2, where R(0) = 0, f irst– P2: F1

j = max(0, 1.0) + 2 = 3, where R(1) = 1, f irst– P3: F2

i= max(2, 1.5) + 3 = 5, where R(2) = 1.5, F1i = 2

– P4: F2j = 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: F3

i = 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

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