Congestion Control in Communication Networks
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1 Congestion Control in Communication Networks
2 Introduction Congestion occurs when number of packets transmitted approaches network capacity Objective of congestion control: keep number of packets below level at which performance drops off dramatically
3 Queuing Theory Data network is a network of queues If arrival rate > transmission rate (λ >μ) queue size grows without bound and packet delay goes to infinity ( )
4 At Saturation Point, 2 Strategies Discard any incoming packet if no buffer available Saturated node exercises flow control over neighbors May cause congestion to propagate throughout network
5 Ideal Network Performance I.e., infinite buffers and no overhead for packet transmission or congestion control Throughput increases with offered load until full capacity Packet delay increases with offered load approaching infinity at full capacity Power = throughput / delay
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7 Practical Performance I.e., finite buffers and non-zero packet processing overhead With no congestion control, increased load eventually causes moderate congestion: throughput increases at slower rate than load Further increased load causes packet delays to increase and eventually throughput to drop to zero
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9 Congestion Control Approaches Backpressure Request from destination to source to reduce rate Choke packet: ICMP Source Quench Implicit congestion signaling Source detects congestion from transmission delays and discarded packets and reduces flow
10 Explicit congestion signaling Direction Backward Forward Categories Binary Credit-based rate-based
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12 Traffic Management Issues Fairness Last-in-first-discarded (i.e. drop-tail) may not be fair Quality of Service (QoS) provision of service differentiation Voice, video: delay sensitive, loss insensitive File transfer, mail: delay insensitive, loss sensitive Interactive computing: delay and loss sensitive Reservations Policing: excess traffic discarded or handled on best-effort basis T M C C
13 Discard policies Sometimes, you gain by throwing away
14 Proactive Packet Discard Congestion management by proactive packet discard Before buffer full Used on single FIFO queue or multiple queues for elastic traffic E.g. Random Early Detection (RED)
15 Random Early Detection (RED) Motivation Surges fill buffers and cause discards On TCP this is a signal to enter slow start phase, reducing load Lost packets need to be resent Adds to load and delay Global synchronization Traffic burst fills queues so packets lost Many TCP connections enter slow start Traffic drops so network under utilized Connections leave slow start at same time causing burst Bigger buffers do not help Try to anticipate onset of congestion and tell one connection to slow down
16 RED Design Goals Congestion avoidance Global synchronization avoidance Current systems inform connections to back off implicitly by dropping packets Avoidance of bias to bursty traffic Discard arriving packets will do this Bound on average queue length Hence control on average delay
17 RED Algorithm Overview Calculate average queue size avg if avg < THmin queue packet else if Thmin < avg < Thmax calculate probability Pa with probability Pa discard packet else with probability 1-Pa queue packet else if avg THmax discard packet
18 RED Buffer
19 RED Algorithm Detail
20 Discard Probability Why the conversion from Pb to Pa? Using Pb directly tends to penalize bursty traffic prematurely proportional to F=(avg-THmin)/(THmax-THmin) Pa remains low and then rises quickly when count approaches 1/(F Pmax)-1 Result: number of packets allowed to join queue between discards is uniformly distributed in [1,2,...,1/P b]
21
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23 Traffic Shaping In order to make stochastic process more deterministic: Determine total_capacity for aggregated flows Divide flows into classes (voice, video, data etc.) For each flow: if used_capacity + new flow < total_capacity, admit; else no admission; end; During transfer, force compliance by controlling rates Token bucket system common mechanism
24 Token Bucket System Max # cells departing = R = ρt + β from UPC
25 Transport (TCP) performance TCP has many mechanisms for optimising utility, let s look at a few How does TCP perform Flow Control? Understand the need for TCP Congestion Control Dynamic Window Management
26 TCP Flow Control Uses a form of sliding window Differs from mechanism used in LLC, HDLC and others: Decouples acknowledgement of received data units from granting permission to send more TCP s flow control is known as a credit allocation scheme (i.e. credit-based): And each transmitted octet has a sequence number
27 TCP Header Fields for Flow Control Sequence number (SN) of first octet/byte in data segment Acknowledgement (ACK) number (AN) next octet to receive, (if any) Window (W) If ACK contains AN = i, W = j: Octets through SN = i 1 acknowledged Permission is granted to send W = j more octets, i.e., from octets i through i+j-1
28 Chapter 12 TCP Traffic Control 5
29 Credit Allocation is Flexible Suppose last message B issued was AN = i, W = j: To increase credit to k (k > j) when no new data, B issues AN = i, W = k To acknowledge a segment containing m octets (m < j) without allocating more credit, B issues AN = i + m, W = j m
30 Credit Policy Receiver needs a policy for how much credit to give sender Conservative approach: grant credit up to limit of available buffer space May limit throughput in long-delay situations Optimistic approach: grant credit based on expectation of freeing space before data arrives Dangerous if gamble fails, lots of discard
31 Effect of Window Size W = TCP window size (octets) R = Data rate (bps) at TCP source D = Propagation delay (seconds) After TCP source begins transmitting, it takes D seconds for first octet to arrive, and D seconds for acknowledgement to return TCP source could transmit at most 2RD bits, or RD/4 octets (i.e. rate-delay product)
32 Normalized Throughput S (b/s) 1 W >= 2RD W 2RD W < 2RD S =
33 MSS (16-bit) Wnd Scale Factor (max 14) Timestamp Performance Options Default max wnd size = Scaled Wnd size = 216*24 1 = 220 1
34 Complicating Factors Multiple TCP connections are multiplexed over same network interface, reducing available R and efficiency For multi-hop connections, D is the sum of delays across each link plus delays at each router If source data rate R exceeds data rate on one of the hops, that hop will be a bottleneck Lost segments are retransmitted, reducing throughput Impact depends on retransmission policy
35 Retransmission Strategy TCP relies exclusively on positive ACKs and retransmission on ACK timeout There is no explicit negative ACK Retransmission required when: 1. Segment arrives damaged, as indicated by checksum error, causing receiver to discard segment 2. Segment fails to arrive, lost!
36 Timers A timer is associated with each segment as it is sent, i.e. retransmission timer (RTO) If timer expires before segment ACKed, sender must retransmit Key Design Issue: Find a suitable value of retransmission timer?? Too small: many unnecessary retransmissions, wasting network bandwidth Too large: delay in handling lost segment
37 Two Strategies Timer should be longer than round-trip time (RTT) (send segment, receive ACK) Also, remember delay is variable Strategies: 1. Fixed timer 2. Adaptive
38 Some problems with Adaptive Schemes Peer TCP entity perform cumulative acknowledgements and not acknowledge immediately For retransmitted segments, can t tell whether ACK is response to original transmission or retransmission Network conditions may change suddenly However, adaptive is still better than fixed timer!
39 First try: Adaptive Retransmission Timer Average Round-Trip Time (ARTT) Each term given same weight [1/(K+1)] May not adjust properly to recent changes as this more likely reflect future behaviour Give more weight to recent values!
40 RFC 793 Exponential Averaging Smoothed Round-Trip Time (SRTT) SRTT(K + 1) = α SRTT(K) + (1 α) RTT(K + 1) To see the effect of α, lets see SRTT expansion: SRTT(K + 1) = (1 α) RTT(K + 1) + α(1 α) RTT(K) + α2(1 α) RTT(K-1) + + αk(1 α) RTT(1) E.g., if α = 0.8: SRTT(K + 1) = 0.2 RTT(K + 1) RTT(K) RTT(K-1) The older the observation, the less it is counted in the average (0 < α < 1)
41 Exponential Smoothing Coefficients
42 Figure 12.5 Exponential Averaging
43 RFC 793 Retransmission Timeout RTO should be set slightly > current SRTT RTO(K + 1) = Min(UB, Max(LB, β SRTT(K + 1))) UB, LB: pre-chosen fixed upper and lower bounds Example values for α, β: 0.8 < α < < β < 2.0
44 Variance causes problems
45 How to battle high variability? Van Jacobson s algorithm Extend ERTT Algorithm with variance sampling: As above for SRTT and ERTT Estimate Deviation (DRTT) as: DRTT(K + 1) = (1-α) DRTT(K) + α (SRTT - ERTT) TimeOut (RTO) = ERTT + 4*DRTT
46 Figure 12.8 Jacobson s RTO Calculations Chapter 12 TCP Traffic Control
47 Two Other Factors Jacobson s algorithm can significantly improve TCP performance, but: What RTO to use for retransmitted segments? ANSWER: exponential RTO backoff algorithm Which round-trip samples to use as input to Jacobson s algorithm? ANSWER: Karn s algorithm
48 Exponential RTO Backoff Increase RTO each time the same segment retransmitted backoff process Multiply RTO by constant: RTO = q RTO When q = 2 is called binary exponential backoff (similar to Ethernet backoff)
49 Which Round-trip Samples? If an ack is received for retransmitted segment, there are 2 possibilities: 1. Ack is for first transmission 2. Ack is for second transmission TCP source cannot distinguish these 2 cases No valid way to calculate RTT: From first transmission to ack, or From second transmission to ack?
50 Karn s Algorithm Do not use measured RTT of retransmitted segments to update SRTT and SDEV Calculate backoff RTO when a retransmission occurs Use backoff RTO for segments until an ACK arrives for a segment that has not been retransmitted Then Jacobson s algorithm is reactivated to calculate RTO
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