Transcript Objectives
CprE599: Creative Component Adaptive RED with Dynamic Threshold Adjustment Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Objectives Explain the role of active queue management in performance optimization of TCP/IP networks Explain ARDTA Explain performance analysis of ARDTA algorithms using Ns-2 Network Simulator Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Contents Passive queue management Active queue management (AQM) Comparison of AQM algorithms AQM and ARDTA Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Passive Queue Management Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Passive Queue Management Two states: No packet drop No early congestion warning to senders 100% packet drop Cause all senders to back off Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Passive Queue Management Algorithms – Drop Tail Drop Tail Drop packets from the tail of the queue All arriving packets are dropped when the queue size reaches a certain threshold Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Passive Queue Management Algorithms – Drop From Front Drop from Front Drop packets from the front of the queue Drop the packet in the buffer with the oldest age Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Passive Queue Management Algorithms – Push Out Push out The latest buffered packet is pushed out from the queue Make room for a newly arriving packet Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Problems with Passive Queue Management A trade-off between the buffer size and QoS Larger buffer results in higher throughput, but longer delay Lock out: A single connection monopolises the buffer space Give rise to fairness problem Full queue: Queue is full for a long period of time Long queuing delay Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Active Queue Management Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Active Queue Management (AQM) Provide preventive measures to manage a buffer to eliminate problems associated with PQM Characteristics: Preventive random packet drop is performed before the buffer is full The probability of preventive packet drop increases with the increasing level of congestion Goals: Reduce dropped packets Support low-delay interactive services Avoid lock-out Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Random Early Detection (RED) A router maintains two thresholds: Minth: Accept all packets until the queue reaches Minth Drop packets with a linear drop probability when the queue is greater than Minth Maxth: All packets are dropped with probability of 1when the queue exceeds this threshold Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed RED Algorithm Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed RED Drop Function Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Dropping probability in RED P(drop) 1.0 MaxP AvgLen MinThresh Adaptive RED with Dynamic Threshold Adjustment MaxThresh Md. Manzoor Murshed Selection of Maximum Drop Probability for RED Selection of Maxdrop significantly affects the performance of RED Too small: Active packet drops not enough to prevent global synchronisation Too large: Decreases the throughput Optimal value depends on number of connections, round trip time, etc. Selection of an optimal value for Maxdrop remains an open issue Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Detailed algorithm for RED pb Maxp(avg−Minth)/(Maxth−Minth) papb/(1−count x pb) Adaptive RED with Dynamic Threshold Adjustment Initialization: avg 0; count -1; for each packet arrival calculate the new average queue size avg: if the queue is nonempty avg (1- wq)avg + wq q else m f (time - q_time) avg (1- wq)mavg if Minth ≤ avg < Maxth increment count calculate probability pa: pb Maxp(avg−Minth)/(Maxth - Minth) pa pb/(1−count x pb) with probability pa: mark the arriving packet count 0 else if maxth < avg mark the arriving packet count 0 else count -1 when queue becomes empty q_time time Md. Manzoor Murshed Calculation of the Average Queue Length Avg queue length works as a low pass filter (LPF) Controls the active packet drop Accumulate short term congestion Trace long term congestion Improves response time m = f (time−q time) avgi = (1−w)m x avgi-1 . Adaptive RED with Dynamic Threshold Adjustment avgt=(1–w)avgt-1 + wqt Md. Manzoor Murshed Average queue size estimation in RED AvgLen = (1-weight) * AvgLen + weight * SampleLen Weight is normally around 0.002 (and often a negative power of two) How often should we sample the queue size? What if packets have not arrived for a while? Queue length Instantaneous Average Time Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Modified Algorithm to Calculate Average queue during Idle Time Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed ARDTA Algorithm Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Model to Calculate Threshold Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Calculation of Minth n2 a (1 w) a q [1 (1 w) ] w i (1 w) nk 2i k n nk k 1 w j 1 1 0 1 0 n 1 nk i 1 [ j(n 1) 1 l ] (1 w) nk nj l l 0 n2 k 1 i 1 j 1 ank anM a513 w i (1 w) nk 2i w Adaptive RED with Dynamic Threshold Adjustment n 1 [ j(n 1) 1 l ] (1 w) nk nj l l 0 Md. Manzoor Murshed Topology used for simulation Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Simulation with Poisson Arrival Process Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Summary of the Simulation for Poisson Process Algorithm Average Throughput Percent of packets Drop Mean Delay Total drops Poisson Process (Minth=3 Maxth=9) 18.3 0.35% 0.000939 81 Poisson Process (Minth=4Maxth=12) 18.44 0.28% 0.001209 65 ARDTA 18.50 0.21% 0.001068 50 Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Simulation with Self similar traffic Algorithm Average Throughput Percent of packets Drop Mean Delay Total drops SST (Minth=3 Maxth=9) 13.93 1.08% 0.0004 486 SST (Minth=4Maxth=12) 14.1 0.81% 0.00047 352 ARDTA 15.86 0.43% 0.000384 173 Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Comparison of ARDTA with RED and adaptive RED S3 S1 10Mb, 2ms 10Mb, 0ms R1 RED (Buffer size = 100) R2 1.5Mb, 10ms 10Mb, 1ms 10Mb, 3ms S2 S4 Algorithm aggregate per-link throughput(%) aggregate per-link drops(%) RED 93.87 8.2 Adaptive RED 94.33 7.8 ARDTA 94.88 7.63 Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Comparison of ARDTA with ARED and ARED Floyd Feng S1 10Mb, 2ms R1 RED (Buffer size = 35) R2 10Mb, 4ms S3 1.5Mb, 20ms 10Mb, 3ms S2 Thmin Thmax Throughput Drop % drop Delay ARDTA 4.15 Dynamic 1.41 77 0.21 0.120701 AREDFloyd 5 15 1.40 84 0.23 0.120517 AREDFeng 5 15 1.08 993 2.73% 0.07 Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed S1 10 mb 10ms S2 10 mb 10ms 10 mb 10ms S3 10 mb 20ms R1 D1 RED 10 mb 10ms S4 10 mb 10ms S5 Threshold Mean Delay Average Throughput Total drops Percent Drops AREDfloyd Thmin = 5 Thmax = 15 0.003315 7.16 56 0.31% AREDfeng Thmin = 5 Thmax = 15 0.002120 7.44 127 0.672% ARDTA Thmin = 4.15 Thmax = changed dynamically 003519 7.95 55 0.14% Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Performance Evaluation of AQM Schemes Compare the performance of RED variants against a number of criteria Throughput Packet drops Delay Methodology Simulation Modelling Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Analysis Model Single bursty input Mixture of bursty and smooth traffic Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Simulation Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed Simulation using NS-2 Simulation with NS-2 Adaptive RED with Dynamic Threshold Adjustment Md. Manzoor Murshed