Active Queue Management Rong Pan Cisco System EE384y
Active Queue Management Rong Pan Cisco System EE384y Spring Quarter 2008 Outline Queue Management Drop as a way to feedback to TCP sources Part of a closed-loop Traditional Queue Management Drop Tail Problems Active Queue Management RED
CHOKe AFD 2 Queue Management: Drops/Marks - A Feedback Mechanism To Regulate End TCP Hosts End hosts send TCP traffic -> Queue size Network elements, switches/routers, generate drops/ marks based on their queue sizes Drops/Marks: regulation messages to end hosts TCP sources respond to drops/marks by cutting down their windows, i.e. sending rate 3 TCP+Queue Management
- A closed-loop control system C W/R _ + N + q
_ 0.5 - + 1 Time Delay p Queue Management 4
Drop Tail - problems Lock out Full queue Bias against bursty traffic Global synchronization 5 Tail Drop Queue Management Lock-Out Max Queue Length 6 Tail Drop Queue Management
Full-Queue Only drop packets when queue is full long steady-state delay 7 Bias Against Bursty Traffic Max Queue Length 8 Tail Drop Queue Management Global Synchronization Max Queue Length 9
Alternative Queue Management Schemes Drop from front on full queue Drop at random on full queue both solve the lock-out problem both have the full-queues problem 10 Active Queue Management Goals Solve tail-drop problems
no lock-out behavior no full queue no bias against bursty flow no global synchronization Provide better QoS at network nodes low steady-state delay lower packet dropping 11 Random Early Detection (RED) Arriving packet no
AvgQsize > Minth? yes Admit the new packet end no Admit packet with a probability p end AvgQsize > Maxth? yes Drop the new packet
end 12 Drop Probability RED Dropping Curve 1 maxp 0 minth maxth Average Queue Size
13 Effectiveness of RED - Lock-Out & Global Synchronization Packets are randomly dropped Each flow has the same probability of being discarded 14 Effectiveness of RED - Full-Queue & Bias against bursty traffic Drop packets probabilistically in anticipation of congestion not when queue is full Use qavg to decide packet dropping probability: allow instantaneous bursts
15 What QoS does RED Provide? Lower buffer delay: good interactive service qavg is controlled to be small Given responsive flows: packet dropping is reduced early congestion indication allows traffic to throttle back before congestion Given responsive flows: fair bandwidth allocation 16 Bad News - unresponsive end hosts udp
udp tcp tcp udp tcp tcp The Internet Connectionless; Best-Effort 17 Scheduling & Queue Management What routers want to do? isolate unresponsive flows (e.g. UDP)
provide Quality of Service to all users Two ways to do it scheduling algorithms: e.g. FQ, CSFQ, SFQ queue management algorithms with fairness enhancement: e.g. CHOKe, AFD, WRED 18 Active Queue Manament With Enhancement to Fairness FIFO Approximate fair bandwidth allocation Provide isolation from unresponsive flows Be as simple as RED
19 CHOKe RED Arriving packet AvgQsize > Minth? no Drawyes a packet at random from queue Admit the new packet end
no yes Flow id same as AvgQsize > Maxth? nopacket id ? the new no yes Drop> the packet Admit packet with AvgQsize Drop both Maxnew th? no
p matched packets a probability yes end end with Admit packet a probability p end end Drop the new packet end 20 Random Sampling from Queue
UDP flow A randomly chosen packet more likely from the unresponsive flow Adversary cant fool the system 21 Comparison of Flow ID Compare the flow id with the incoming packet more acurate
Reduce the chance of dropping packets from a TCPfriendly flows. 22 Dropping Mechanism Drop packets (both incoming and matching samples ) More arrival -> More Drop Give users a disincentive to send more 23 Simulation Setup S(1) m TCP Sources S(2)
10000 Total UDP Arrival Rate (Kbps) 29 Analytical Model discards from the queue permeable tube with leakage 30 Fluid Analysis N: the total number of packets in the buffer
Li(t): the survival rate for flow i packets Li(t)t - Li(t +t)t = i t Li(t)t /N - dLi(t)/dt = i Li(t) N Li(0) = i (1-pi ) Li(D) = i (1-2pi ) 31 Model vs Simulation - multiple TCPs and one UDP 350 1/(1+e) Throughput
Arrival Rate 32 Fluid Model - Multiple samples Multiple samples are chosen Li(t)t - Li(t +t)t = Mi t Li(t)t /N - dLi(t)/dt = Mi Li(t) N Li(0) = i (1-pi )M Li(D) = i (1-pi )M - Mi pi 33 Two Samples - multiple TCPs and one UDP UDP Throughput(Kbps)
UDP Arrival Rate(Mbps) 2 34 Two Samples - multiple TCPs and two UDP UDP Throughput(Kbps) 200 160 120 80 NS Simulation Fluid Model 40
0 0 0.5 1 1.5 2 UDP Arrival Rate(Mbps) 35 What If We Use a Small Amount of State?
36 AFD: Goal Approximate weighted bandwidth allocation Not only AQM, approximate WDRR scheduling Provide soft queues in addition to physical queues Keep the state requirement small Be simple to implement 37 AFD Algorithm: Details (Basic Case: Equal Share) Di = Drop Probability for Class i Arriving Packets
Qlen 1-Di Qref Class i Di Mi = Arrival estimate for Class i (Bytes over interval Ts) Mfair = f(Qlen, Qlen_old,Qref) Fair Share If Mi Mfair : No Drop (Di = 0) If Mi > Mfair : Di > 0 such that
Mi (1-Di) = Mfair 38 AFD Algorithm: Details (General Case) Di = Drop Probability for Class i Arriving Packets Qlen 1-Di Qref Class i Di Mi = Arrival estimate for Class i
(Bytes over interval Ts) Mfair = f(Qlen, Qlen_old,Qref) Fair Share If Mi F(Mfair,Mini,Maxi,Wi, ): No Drop (Di = 0) If Mi > Mfair : Di > 0 such that Mi (1-Di) = F(Mfair,Mini,Maxi,Wi, ) 39 Not Per-Flow State Fraction of flows State requirement on the order of # of unresponsive flows Elephant Traps (developed jointly Stanford and Cisco)
40 AFD Solution: Details Based on 3 simple mechanisms estimate per class arrival rate counting per class bytes over fixed intervals ( Ts ) potential averaging over multiple intervals estimate deserved departure rate (so as to achieve the proper bandwidth allocation for each class) observation of queue length as measure of congestion perform selective dropping (pre-enqueue) to drive arrival rate to the desired departure rate 41 Mixed Traffic with Different Levels of Unresponsiveness
Throughput (kbps) 500 400 300 200 100 0 RED CHOKe AFD 42 Drop Probabilities (note differential dropping)
Drop Probability 0.3 0.25 0.2 0.15 0.1 0.05 0 RED CHOKe AFD 43 5 TCP Flows
0 50 100 10 TCP Flows Class 2 Class 1 Different Number of TCP Flows in Each Class 150
200 0 time 50 100 150 200 time 200
time 20 TCP Flows Class 3 Class 4 15 TCP Flows 0 50 100 150
200 time 0 50 100 150 44 Different Class Throughput Comparison 45
Queue Length 46 Mfair 47 AFD Implementation Issues Monitor Arrival Rate Determine Drop Probability Maximize Link Utilization 48 Arrival Monitoring Keep a counter for each class
Count the data arrivals (in bytes) of each class in 10ms interval: arvi Arrival rate of each class is updated every 10ms mi = mi(1-1/2c)+arvi c determines the average window 49 Implementing the Drop Function If Mi Mfair then Di = 0 Otherwise, rewrite the drop function as Suppose we have predetermined drop levels, find the one such
that Di* Mi = (Mi Mfair) 50 Implementing the Drop Function Drop levels are: 1/32, 1/16, 3/32 Suppose mi = 100, mfair = 62.0 => Di = 0.380, Di 0.0 0.375 0.406 We choose the higher value using binary search
1.0 51 AFD - Summary FQ Fairness AFD Ideal CHOKe RED Simplicity Equal share is approximated in a wide variety of settings The state requirement is limited 52
Summary Traditional Queue Management Drop Tail, Drop Front, Drop Random Problems: lock-out, full queue, global synchronization, bias against bursty traffic Active Queue Management RED: cant handle unresponsive flows CHOKe: penalize unresponsive flows AFD: provides approximate fairness with limited states 53
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