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15 -441 Computer Networking Lecture 20 – Queue Management and Qo. S 15 -441 Computer Networking Lecture 20 – Queue Management and Qo. S

Project 3 • Start EARLY • Tomorrow’s recitation Lecture 20: QOS (c) CMU, 2005 Project 3 • Start EARLY • Tomorrow’s recitation Lecture 20: QOS (c) CMU, 2005 -10 2

Traffic and Resource Management • Resources statistically shared • Overload causes congestion • packet Traffic and Resource Management • Resources statistically shared • Overload causes congestion • packet delayed or dropped • application performance suffer • Local vs. network wide • Transient vs. persistent • Challenge • high resource utilization • high application performance Lecture 20: QOS (c) CMU, 2005 -10 3

Resource Management Approaches • Increase resources • install new links, faster routers • capacity Resource Management Approaches • Increase resources • install new links, faster routers • capacity planning, provisioning, traffic engineering • happen at longer timescale • Reduce or delay demand • Reactive approach: encourage everyone to reduce or delay demand • Reservation approach: some requests will be rejected by the network Lecture 20: QOS (c) CMU, 2005 -10 4

Congestion Control in Today’s Internet • End-system-only solution (TCP) • dynamically estimates network state Congestion Control in Today’s Internet • End-system-only solution (TCP) • dynamically estimates network state • packet loss signals congestion • reduces transmission rate in presence of congestion • routers play little role TCP TCP Feedback Control Time scale Lecture 20: QOS Capacity Planning RTT (ms) Months (c) CMU, 2005 -10 5

More Ideas on Traffic Management • Improve TCP • Stay with end-point only architecture More Ideas on Traffic Management • Improve TCP • Stay with end-point only architecture • Enhance routers to help TCP • Random Early Discard • Enhance routers to control traffic • Rate limiting • Fair Queueing • Provide Qo. S by limiting congestion Lecture 20: QOS (c) CMU, 2005 -10 6

Router Mechanisms • Buffer management: when and which packet to drop? • Scheduling: which Router Mechanisms • Buffer management: when and which packet to drop? • Scheduling: which packet to transmit next? flow 1 1 Classifier 2 flow 2 Scheduler flow n Buffer management Lecture 20: QOS (c) CMU, 2005 -10 7

Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Lecture 20: QOS (c) CMU, 2005 -10 8

Queuing Disciplines • Each router must implement some queuing discipline • Queuing allocates both Queuing Disciplines • Each router must implement some queuing discipline • Queuing allocates both bandwidth and buffer space: • Bandwidth: which packet to serve (transmit) next • Buffer space: which packet to drop next (when required) • Queuing also affects latency Lecture 20: QOS (c) CMU, 2005 -10 9

Typical Internet Queuing • FIFO + drop-tail • Simplest choice • Used widely in Typical Internet Queuing • FIFO + drop-tail • Simplest choice • Used widely in the Internet • FIFO (first-in-first-out) • Implies single class of traffic • Drop-tail • Arriving packets get dropped when queue is full regardless of flow or importance • Important distinction: • FIFO: scheduling discipline • Drop-tail: drop policy Lecture 20: QOS (c) CMU, 2005 -10 10

FIFO + Drop-tail Problems • Leaves responsibility of congestion control completely to the edges FIFO + Drop-tail Problems • Leaves responsibility of congestion control completely to the edges (e. g. , TCP) • Does not separate between different flows • No policing: send more packets get more service • Synchronization: end hosts react to same events Lecture 20: QOS (c) CMU, 2005 -10 11

FIFO + Drop-tail Problems • Full queues • Routers are forced to have large FIFO + Drop-tail Problems • Full queues • Routers are forced to have large queues to maintain high utilizations • TCP detects congestion from loss • Forces network to have long standing queues in steady-state • Lock-out problem • Drop-tail routers treat bursty traffic poorly • Traffic gets synchronized easily allows a few flows to monopolize the queue space Lecture 20: QOS (c) CMU, 2005 -10 12

Active Queue Management • Design active router queue management to aid congestion control • Active Queue Management • Design active router queue management to aid congestion control • Why? • Router has unified view of queuing behavior • Routers see actual queue occupancy (distinguish queue delay and propagation delay) • Routers can decide on transient congestion, based on workload Lecture 20: QOS (c) CMU, 2005 -10 13

Design Objectives • Keep throughput high and delay low • High power (throughput/delay) • Design Objectives • Keep throughput high and delay low • High power (throughput/delay) • Accommodate bursts • Queue size should reflect ability to accept bursts rather than steady-state queuing • Improve TCP performance with minimal hardware changes Lecture 20: QOS (c) CMU, 2005 -10 14

Lock-out Problem • Random drop • Packet arriving when queue is full causes some Lock-out Problem • Random drop • Packet arriving when queue is full causes some random packet to be dropped • Drop front • On full queue, drop packet at head of queue • Random drop and drop front solve the lock-out problem but not the full-queues problem Lecture 20: QOS (c) CMU, 2005 -10 15

Full Queues Problem • Drop packets before queue becomes full (early drop) • Intuition: Full Queues Problem • Drop packets before queue becomes full (early drop) • Intuition: notify senders of incipient congestion • Example: early random drop (ERD): • If qlen > drop level, drop each new packet with fixed probability p • Does not control misbehaving users Lecture 20: QOS (c) CMU, 2005 -10 16

Random Early Detection (RED) • Detect incipient congestion • Assume hosts respond to lost Random Early Detection (RED) • Detect incipient congestion • Assume hosts respond to lost packets • Avoid window synchronization • Randomly mark packets • Avoid bias against bursty traffic Lecture 20: QOS (c) CMU, 2005 -10 17

RED Algorithm • Maintain running average of queue length • If avg < minth RED Algorithm • Maintain running average of queue length • If avg < minth do nothing • Low queuing, send packets through • If avg > maxth, drop packet • Protection from misbehaving sources • Else mark packet in a manner proportional to queue length • Notify sources of incipient congestion Lecture 20: QOS (c) CMU, 2005 -10 18

RED Operation Min thresh Max thresh P(drop) Average Queue Length 1. 0 max. P RED Operation Min thresh Max thresh P(drop) Average Queue Length 1. 0 max. P minth Lecture 20: QOS maxth (c) CMU, 2005 -10 Avg queue length 19

Explicit Congestion Notification (ECN) [ Floyd and Ramakrishnan 98] • Traditional mechanism • packet Explicit Congestion Notification (ECN) [ Floyd and Ramakrishnan 98] • Traditional mechanism • packet drop as implicit congestion signal to end systems • TCP will slow down • Works well for bulk data transfer • Does not work well for delay sensitive applications • audio, WEB, telnet • Explicit Congestion Notification (ECN) • borrow ideas from DECBit • use two bits in IP header • ECN-Capable Transport (ECT) bit set by sender • Congestion Experienced (CE) bit set by router Lecture 20: QOS (c) CMU, 2005 -10 20

Congestion Control Summary • Architecture: end system detects congestion and slow down • Starting Congestion Control Summary • Architecture: end system detects congestion and slow down • Starting point: • slow start/congestion avoidance • packet drop detected by retransmission timeout RTO as congestion signal • fast retransmission/fast recovery • packet drop detected by three duplicate acks • TCP Improvement: • New. Reno: better handle multiple losses in one round trip • SACK: better feedback to source • Net. Reno: reduce RTO in high loss rate, small window scenario • FACK, Net. Reno: better end system control law Lecture 20: QOS (c) CMU, 2005 -10 21

Congestion Control Summary (II) • Router support • RED: early signaling • ECN: explicit Congestion Control Summary (II) • Router support • RED: early signaling • ECN: explicit signaling Lecture 20: QOS (c) CMU, 2005 -10 22

Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Lecture 20: QOS (c) CMU, 2005 -10 23

Problems to achieving fairness • Works only if most sources implement TCP • most Problems to achieving fairness • Works only if most sources implement TCP • most sources are cooperative • most sources implement homogeneous/compatible control law • compatible means less aggressive than TCP • What if sources do not play by the rule? Lecture 20: QOS Lecture 22: 2006 -11 -14 24

An Example • 1 UDP (10 Mbps) and 31 TCPs sharing a 10 Mbps An Example • 1 UDP (10 Mbps) and 31 TCPs sharing a 10 Mbps line UDP (#1) - 10 Mbps TCP (#2). . . TCP (#32) UDP (#1) Bottleneck link (10 Mbps) Lecture 22: 2006 -11 -14 TCP (#2). . . TCP (#32) 25

Throughput of UDP and TCP Flows With FIFO Lecture 22: 2006 -11 -14 26 Throughput of UDP and TCP Flows With FIFO Lecture 22: 2006 -11 -14 26

Fairness Goals • Allocate resources fairly • Isolate ill-behaved users • Router does not Fairness Goals • Allocate resources fairly • Isolate ill-behaved users • Router does not send explicit feedback to source • Still needs e 2 e congestion control • Still achieve statistical muxing • One flow can fill entire pipe if no contenders • Work conserving scheduler never idles link if it has a packet Lecture 20: QOS (c) CMU, 2005 -10 27

What is Fairness? • At what granularity? • Flows, connections, domains? • What if What is Fairness? • At what granularity? • Flows, connections, domains? • What if users have different RTTs/links/etc. • Should it share a link fairly or be TCP fair? • Maximize fairness index? • Fairness = (Sxi)2/n(Sxi 2) 0

Max-min Fairness • Allocate user with “small” demand what it wants, evenly divide unused Max-min Fairness • Allocate user with “small” demand what it wants, evenly divide unused resources to “big” users • Formally: • Resources allocated in terms of increasing demand • No source gets resource share larger than its demand • Sources with unsatisfied demands get equal share of resource Lecture 20: QOS (c) CMU, 2005 -10 29

Implementing Max-min Fairness • Generalized processor sharing • Fluid fairness • Bitwise round robin Implementing Max-min Fairness • Generalized processor sharing • Fluid fairness • Bitwise round robin among all queues • Why not simple round robin? • Variable packet length can get more service by sending bigger packets • Unfair instantaneous service rate • What if arrive just before/after packet departs? Lecture 20: QOS (c) CMU, 2005 -10 30

Bit-by-bit RR • Single flow: clock ticks when a bit is transmitted. For packet Bit-by-bit RR • Single flow: clock ticks when a bit is transmitted. For packet i: • Pi = length, Ai = arrival time, Si = begin transmit time, Fi = finish transmit time • Fi = Si+Pi = max (Fi-1, Ai) + Pi • Multiple flows: clock ticks when a bit from all active flows is transmitted round number • Can calculate Fi for each packet if number of flows is know at all times • Why do we need to know flow count? need to know A This can be complicated Lecture 20: QOS (c) CMU, 2005 -10 31

Bit-by-bit RR Illustration • Not feasible to interleave bits on real networks • FQ Bit-by-bit RR Illustration • Not feasible to interleave bits on real networks • FQ simulates bit-by-bit RR Lecture 20: QOS (c) CMU, 2005 -10 32

Fair Queuing • Mapping bit-by-bit schedule onto packet transmission schedule • Transmit packet with Fair Queuing • Mapping bit-by-bit schedule onto packet transmission schedule • Transmit packet with the lowest Fi at any given time • How do you compute Fi? Lecture 20: QOS (c) CMU, 2005 -10 33

Bit-by-bit RR Example Flow 1 Flow 2 Output F=10 F=8 Flow 1 (arriving) F=5 Bit-by-bit RR Example Flow 1 Flow 2 Output F=10 F=8 Flow 1 (arriving) F=5 Cannot preempt packet currently being transmitted Flow 2 Output transmitting F=10 F=2 (c) CMU, 2005 -10 35

Fair Queuing Tradeoffs • Complex computation • Classification into flows may be hard • Fair Queuing Tradeoffs • Complex computation • Classification into flows may be hard • Must keep queues sorted by finish times • d. R/dt changes whenever the flow count changes • Complex state • Must keep queue per flow • Hard in routers with many flows (e. g. , backbone routers) • Flow aggregation is a possibility (e. g. do fairness per domain) • FQ can control congestion by monitoring flows • Non-adaptive flows can still be a problem – why? Lecture 20: QOS (c) CMU, 2005 -10 36

Example Outcome: Throughput of TCP and UDP Flows With Fair Queueing Router Lecture 22: Example Outcome: Throughput of TCP and UDP Flows With Fair Queueing Router Lecture 22: 2006 -11 -14 37

Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Lecture 20: QOS (c) CMU, 2005 -10 38

Motivation • Internet currently provides one single class of “best-effort” service • No assurances Motivation • Internet currently provides one single class of “best-effort” service • No assurances about delivery • At internet design most applications are elastic • Tolerate delays and losses • Can adapt to congestion • Today, many “real-time” applications are inelastic Lecture 20: QOS (c) CMU, 2005 -10 39

Why a New Service Model? • What is the basic objective of network design? Why a New Service Model? • What is the basic objective of network design? • Maximize total bandwidth? Minimize latency? • Maximize user satisfaction – the total utility given to users • What does utility vs. bandwidth look like? • Shape depends on application • Must be non-decreasing function Lecture 20: QOS (c) CMU, 2005 -10 40

Utility Curve Shapes U U Elastic BW U Hard real-time BW Delay- or Rate-adaptive Utility Curve Shapes U U Elastic BW U Hard real-time BW Delay- or Rate-adaptive Stay to the right and you are fine for all curves BW (c) CMU, 2005 -10 41

Utility curve – Elastic traffic U Elastic Bandwidth Does equal allocation of bandwidth maximize Utility curve – Elastic traffic U Elastic Bandwidth Does equal allocation of bandwidth maximize total utility? (c) CMU, 2005 -10 42

Admission Control • If U(bandwidth) is concave elastic applications U • Incremental utility is Admission Control • If U(bandwidth) is concave elastic applications U • Incremental utility is decreasing with increasing bandwidth • Is always advantageous to have more flows with lower bandwidth Elastic BW • No need of admission control; This is why the Internet works! Lecture 20: QOS (c) CMU, 2005 -10 43

Utility Curves – Inelastic traffic U Delay-adaptive U BW Hard real-time BW Does equal Utility Curves – Inelastic traffic U Delay-adaptive U BW Hard real-time BW Does equal allocation of bandwidth maximize total utility? (c) CMU, 2005 -10 44

Inelastic Applications • Continuous media applications • Lower and upper limit on acceptable performance. Inelastic Applications • Continuous media applications • Lower and upper limit on acceptable performance. • BW below which video and audio are not intelligible • Internet telephones, teleconferencing with high delay (200 - 300 ms) impair human interaction • Sometimes called “tolerant real-time” since they can adapt to the performance of the network • Hard real-time applications • Require hard limits on performance • E. g. control applications Lecture 20: QOS (c) CMU, 2005 -10 45

Admission Control • If U is convex inelastic applications • U(number of flows) is Admission Control • If U is convex inelastic applications • U(number of flows) is no longer monotonically increasing • Need admission control to maximize total utility • Admission control deciding when adding more people would reduce overall utility U Delay-adaptive BW • Basically avoids overload Lecture 20: QOS (c) CMU, 2005 -10 46

Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Overview • Queue management & RED • Fair-queuing • Why QOS? • Integrated services Lecture 20: QOS (c) CMU, 2005 -10 47

Components of Integrated Services 1. Type of commitment What does the network promise? 2. Components of Integrated Services 1. Type of commitment What does the network promise? 2. Packet scheduling How does the network meet promises? 3. Service interface How does the application describe what it wants? 4. Establishing the guarantee How is the promise communicated to/from the network How is admission of new applications controlled? Lecture 20: QOS (c) CMU, 2005 -10 48

Type of Commitments • Guaranteed service • For hard real-time applications • Fixed guarantee, Type of Commitments • Guaranteed service • For hard real-time applications • Fixed guarantee, network meets commitment if clients send at agreed-upon rate • Predicted service • For delay-adaptive applications • Two components • If conditions do not change, commit to current service • If conditions change, take steps to deliver consistent performance (help apps minimize playback delay) • Implicit assumption – network does not change much over time • Datagram/best effort service Lecture 20: QOS (c) CMU, 2005 -10 49

Scheduling for Guaranteed Traffic • Use token bucket filter to characterize traffic • Described Scheduling for Guaranteed Traffic • Use token bucket filter to characterize traffic • Described by rate r and bucket depth b • Use Weighted Fair-Queueing at the routers • Parekh’s bound for worst case queuing delay = b/r Lecture 20: QOS (c) CMU, 2005 -10 50

Token Bucket Filter Tokens enter bucket at rate r Operation: Bucket depth b: capacity Token Bucket Filter Tokens enter bucket at rate r Operation: Bucket depth b: capacity of bucket Lecture 20: QOS • If bucket fills, tokens are discarded • Sending a packet of size P uses P tokens • If bucket has P tokens, packet sent at max rate, else must wait for tokens to accumulate (c) CMU, 2005 -10 51

Token Bucket Operation Tokens Overflow Packet Enough tokens packet goes through, tokens removed (c) Token Bucket Operation Tokens Overflow Packet Enough tokens packet goes through, tokens removed (c) CMU, 2005 -10 Packet Not enough tokens wait for tokens to accumulate 52

Token Bucket Characteristics • On the long run, rate is limited to r • Token Bucket Characteristics • On the long run, rate is limited to r • On the short run, a burst of size b can be sent • Amount of traffic entering at interval T is bounded by: • Traffic = b + r*T • Information useful to admission algorithm Lecture 20: QOS (c) CMU, 2005 -10 53

Token Bucket • Parameters • r – average rate, i. e. , rate at Token Bucket • Parameters • r – average rate, i. e. , rate at which tokens fill the bucket • b – bucket depth • R – maximum link capacity or peak rate (optional parameter) • A bit is transmitted only when there is an available token r bps Maximum # of bits sent bits slope r b*R/(R-r) b bits slope R <= R bps time regulator Lecture 20: QOS Lecture 22: 2006 -11 -14 54

Traffic Enforcement: Example • r = 100 Kbps; b = 3 Kb; R = Traffic Enforcement: Example • r = 100 Kbps; b = 3 Kb; R = 500 Kbps (b) (a) 3 Kb 2. 2 Kb T = 2 ms : packet transmitted b = 3 Kb – 1 Kb + 2 ms*100 Kbps = 2. 2 Kb T = 0 : 1 Kb packet arrives (c) 2. 4 Kb 3 Kb T = 4 ms : 3 Kb packet arrives T = 10 ms : Lecture 20: QOS (e) (d) Lecture 22: 2006 -11 -14 0. 6 Kb T = 16 ms : packet transmitted 55

Token Bucket Specs BW 2 Flow B Flow A: r = 1 MBps, B=1 Token Bucket Specs BW 2 Flow B Flow A: r = 1 MBps, B=1 byte 1 Flow A 1 2 3 Flow B: r = 1 MBps, B=1 MB Time (c) CMU, 2005 -10 56

Guarantee Proven by Parekh • Given: • Flow i shaped with token bucket and Guarantee Proven by Parekh • Given: • Flow i shaped with token bucket and leaky bucket rate control (depth b and rate r) • Network nodes do WFQ • Cumulative queuing delay Di suffered by flow i has upper bound • Di < b/r, (where r may be much larger than average rate) • Assumes that r < link speed at any router • All sources limiting themselves to r will result in no network queuing Lecture 20: QOS (c) CMU, 2005 -10 57

Sharing versus Isolation • Impact of queueing mechanisms: • • Isolation: Isolates well-behaved from Sharing versus Isolation • Impact of queueing mechanisms: • • Isolation: Isolates well-behaved from misbehaving sources Sharing: Mixing of different sources in a way beneficial to all • FIFO: sharing • each traffic source impacts other connections directly • e. g. malicious user can grab extra bandwidth • the simplest and most common queueing discipline • averages out the delay across all flows • Priority queues: one-way sharing • high-priority traffic sources have impact on lower priority traffic only • has to be combined with admission control and traffic enforcement to avoid starvation of low-priority traffic • WFQ: two-way isolation • provides a guaranteed minimum throughput (and maximum delay) Lecture 20: QOS (c) CMU, 2005 -10 58

Putting It All Together • Assume 3 types of traffic: guaranteed, predictive, besteffort • Putting It All Together • Assume 3 types of traffic: guaranteed, predictive, besteffort • Scheduling: use WFQ in routers • Each guaranteed flow gets its own queue • All predicted service flows and best effort aggregates in single separate queue • Predictive traffic classes • Worst case delay for classes separated by order of magnitude • When high priority needs extra bandwidth – steals it from lower class • Best effort traffic acts as lowest priority class Lecture 20: QOS (c) CMU, 2005 -10 59

Service Interfaces • Guaranteed Traffic • Host specifies rate to network • Why not Service Interfaces • Guaranteed Traffic • Host specifies rate to network • Why not bucket size b? • If delay not good, ask for higher rate • Predicted Traffic • • Specifies (r, b) token bucket parameters Specifies delay D and loss rate L Network assigns priority class Policing at edges to drop or tag packets • Needed to provide isolation – why is this not done for guaranteed traffic? • WFQ provides this for guaranteed traffic Lecture 20: QOS (c) CMU, 2005 -10 60

Lessons • TCP can use help from routers • RED eliminate lock-out and full-queues Lessons • TCP can use help from routers • RED eliminate lock-out and full-queues problems • FQ heavy-weight but explicitly fair to all • Qo. S • What type of applications are there? Elastic, adaptive real-time , and hard real-time. • Why do we need admission control to maximize utility • How do token buckets + WFQ provide Qo. S guarantees? Lecture 20: QOS (c) CMU, 2005 -10 61