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Computer Networks with Internet Technology William Stallings

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Title: Chapter 05 Congestion and Performance Issues Author: Adrian J Pullin Last modified by: Adrian J Pullin Created Date: 11/14/1999 9:59:24 AM Document ... – PowerPoint PPT presentation

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Title: Computer Networks with Internet Technology William Stallings


1
Computer Networks with Internet
Technology William Stallings
  • Chapter 05
  • Congestion and Performance Issues

2
High-Speed LANs
  • Speed and power of personal computers has
    increased
  • LAN viable and essential computing platform
  • Client/server computing dominant architecture
  • Web-focused intranet
  • Frequent transfer of potentially large volumes of
    data in a transaction-oriented environment
  • 10-Mbps Ethernet and 16-Mbps token ring not up to
    job

3
Uses of High-Speed LANs
  • Centralized server farms
  • Client systems draw huge amounts of data from
    multiple centralized servers
  • E.g. color publishing
  • Servers hold tens of gigabytes of image data that
    must be downloaded to workstations
  • Power workgroups
  • Small number of users drawing data across network
  • E.g.s Software development group, computer-aided
    design (CAD)
  • High-speed local backbone
  • LANs proliferate at a site,
  • High-speed interconnection is necessary

4
Corporate Wide Area Networking Needs
  • Up to 1990s, centralized data processing model
  • Dispersed employees into multiple smaller offices
  • Growing use of telecommuting
  • Application structure changed
  • Client/server and intranet computing
  • More reliance on PCs, workstations, and servers
  • GUIs gives user graphic applications, multimedia
    etc.
  • Internet access
  • A few mouse clicks can trigger huge volumes of
    data
  • Traffic patterns unpredictable
  • Average load has risen
  • More data transported off premises
  • Traditionally 80 traffic local 20 wide area
  • No longer applies
  • Greater burden on LAN backbones and on WAN

5
Digital Electronics Examples
  • Digital Versatile Disk (DVD)
  • Huge storage capacity and vivid quality
  • Digital camcorder
  • Easy for individuals and companies to make
    digital video files and place on Web sites
  •  Digital Still Camera
  • Individual personal pictures
  • Companies online product catalogs with full-color
    pictures of every product

6
QoS on The Internet
  • IP designed to provide best-effort, fair delivery
    service
  • All packets treated equally
  • As traffic grows, congestion occurs, all packet
    delivery slowed
  • Packets dropped at random to ease congestion
  • Only networking scheme designed to support both
    traditional TCP and UDP and real-time traffic is
    ATM
  • Means constructing second infrastructure for
    real-time traffic or replacing existing IP-based
    configuration with ATM
  • Two types of traffic
  • Elastic traffic can adjust, over wide ranges, to
    changes in delay and throughput
  • Supported on TCP/IP
  • Handle congestion by reducing rate data presented
    to network

7
Elastic Traffic
  • File transfer, electronic mail, remote logon,
    network management, Web access
  • E-mail insensitive to changes in delay
  • User expects file transfer delay proportional to
    file size and so is sensitive to changes in
    throughput
  • With network management, delay is not concern
  • If failures cause congestion, network management
    messages must get through minimum delay
  • Interactive applications, (remote logon, Web
    access) quite sensitive to delay 
  • Even for elastic traffic QoS-based service could
    help

8
Inelastic Traffic
  • Inelastic traffic does not easily adapt, if at
    all, to changes in delay and throughput
  • E.g. real-time traffic
  • Voice and video
  • Requirements 
  • Throughput minimum value may be required
  • Delay e.g. stock trading
  • Delay variation Larger variation needs larger
    buffers
  • Packet loss Applications vary in packet loss
    that they can sustain
  • Difficult to meet with variable queuing delays
    and congestion losses
  • Need preferential treatment to some applications
  • Applications need to be able to state requirements

9
Supporting Both
  • When supporting inelastic traffic, elastic
    traffic must still be supported
  • Inelastic applications do not back off in the
    face of congestion
  • TCP-based applications do
  • When congested, inelastic traffic continues high
    load,
  • Elastic traffic crowded off
  • Reservation protocol can help
  • Deny requests that would leave too few resources
    available to handle current elastic traffic

10
Figure 5.1 Application Delay Sensitivity and
Criticality
11
Performance Requirements Response Time
  • Time it takes a system to react to a given input
  • Time between last keystroke and beginning of
    display of result
  • Time it takes for system to respond to request
  • Quicker response imposes greater cost
  • Computer processing power
  • Competing requirements
  • Providing rapid response to some processes may
    penalize others
  • User response time
  • Between user receiving complete reply and enters
    next command (think time)
  • System response time
  • Between user entering command and complete
    response 

12
Figure 5.2 Response Time Results for
High-Function Graphics
13
Figure 5.3 Response Time Requirements
14
Throughput
  • Higher transmission speed makes possible
    increased support for different services
  • e.g., Integrated Services Digital Network ISDN
    and broadband-based multimedia services
  • Need to know demands each service puts on storage
    and communications of systems
  • Services grouped into data, audio, image, and
    video

15
Figure 5.4 Required Data Rates for Various
Information Types
16
Figure 5.5 Effective Throughput
17
Performance Metrics
  • Throughput, or capacity
  • Data rate in bits per second (bps)
  • Affected by multiplexing
  • Effective capacity reduced by protocol overhead
  • Header bits TCP and IPv4 at least 40 bytes
  • Control overhead e.g. acknowledgements 
  • Delay
  • Average time for block of data to go from system
    to system
  • Round-trip delay
  • Getting data from one system to another plus
    delay acknowledgement  
  • Transmission delay Time for transmitter to send
    all bits of packet
  • Propagation delay Time for one bit to transit
    from source to destination
  • Processing delay Time required to process packet
    at source prior to sending, at any intermediate
    router or switch, and at destination prior to
    delivering to application
  • Queuing delay Time spend waiting in queues

18
Example Effect of Different Types of Delay
64kbps
  • Ignore any processing or queuing delays
  • 1-megabit file across USA (4800km)
  • Fiber optic link
  • Propagation rate speed of light (approximately 3
    ? 108 m/s)
  • Propagation delay (4800?103)/(3?108) 0.016 s
  • In that time host transmits (64 ? 103)(0.016)
    1024 bits
  • Transmission delay (106)/(64 ? 103) 15.625 s
  • Time to transmit file is Transmission delay plus
    propagation delay 15.641 s
  • Transmission delay dominates propagation delay
  • Higher-speed channel would reduce time required

19
Example Effect of Different Types of Delay 1
Gbps
  • Propagation delay is still the same
  • Note this as it is often forgotten!
  • Transmission delay (106)/(106 ? 103) 0.001 s
  • Total time to transmit file 0.017 s
  • Propagation delay dominates
  • Increasing data rate will not noticeably speed up
    delivery of file
  • Preceding example depends on data rate, distance,
    propagation velocity, and size of packet
  • These parameters combined into single critical
    system parameter, commonly denoted a

20
a (1)
  • where
  • R data rate, or capacity, of the link
  • L number of bits in a packet
  • d distance between source and destination
  • v velocity of propagation of the signal
  • D propagation delay

21
a (2)
  • Looking at the final fraction, can also be
    expressed
  •  
  •  
  • For fixed packet length, a dependent on R ? D
    product
  • 64-kbps link, a 1.024 ? 103
  • 1-Gbps link, a 16

22
Impact of a
  • Send sequence of packets and wait for
    acknowledgment to each packet before sending next
  • Stop-and-wait protocol
  • Transmission time normalized to 1 propagation
    time is a
  • a gt 1
  • Link's bit length greater than that of packet
  • Assume ACK packet is small enough to ignore its
    transmission time
  • t 0, Station A begins transmitting packet
  • t 1, A completes transmission
  • t a, leading edge of packet reaches B
  • t 1 a, B has received entire packet
  • Immediately transmits small acknowledgment packet
  • T 1 2a, acknowledgment arrives at A
  • Total elapsed time is 1 2a
  • Hence normalized rate packets can be transmitted
    is 1/(1 2a)
  • Same result with a lt 1

23
Figure 5.6 Effect of a on Link Utilization
24
Throughput as Function of a
  • For a gt 1 stop-and-wait inefficient
  • Gigabit WANs even for large packets (e.g., 1 Mb),
    channel is seriously underutilized

25
Figure 5.7 Normalized Throughput as a Function of
a for Stop-and-Wait
26
Improving Performance
  • If lots of users each use small portion of
    capacity, then for each user, effective capacity
    is considerably smaller, reducing a
  • Each user has smaller data rate
  • May be inadequate
  • If application uses channel with high a,
    performance can be improved by allowing
    application to treat channel as pipeline
  • Continuous flow of packets
  • Not waiting for acknowledgment to individual
    packet
  • Problems
  • Flow control
  • Error control
  • Congestion control

27
Flow control
  • B may need to temporarily restrict flow of
    packets
  • Buffer is filling up or application is
    temporarily busy
  • By the time signal from B arrives at A, many
    additional packets in the pipeline
  • If B cannot absorb these packets, they must be
    discarded

28
Error control
  • If B detects error it may request retransmission
  • If B unable to store incoming packets out of
    order, A must retransmit packet in error and all
    subsequent packets
  • Selective retransmission v. Go-Back-N

29
Congestion control
  • Various methods by which A can learn there is
    congestion
  • A should reduce the flow of packets
  • Large value of a
  • Many packets in pipeline between onset of
    congestion and when A learns about it

30
Queuing Delays
  • Often queuing delays are dominant
  • Grow dramatically as system approaches capacity
  • In shared facility (e.g., network, transmission
    line, time-sharing system, road network, checkout
    lines, ) performance typically responds
    exponentially to increased demand
  • Figure 5.8 representative example
  • Upper line shows user response time on shared
    facility as load increases
  • Load expressed as fraction of capacity
  • Lower line is simple projection based on
    knowledge of system behavior up to load of 0.5
  • Note performance will in fact collapse beyond
    about 0.8 to 0.9

31
Figure 5.8 Projected Versus Actual Response Time
32
What Is Congestion?
  • Congestion occurs when the number of packets
    being transmitted through the network approaches
    the packet handling capacity of the network
  • Congestion control aims to keep number of packets
    below level at which performance falls off
    dramatically
  • Data network is a network of queues
  • Generally 80 utilization is critical
  • Finite queues mean data may be lost

33
Figure 5.9 Input and Output Queues at Node
34
Effects of Congestion
  • Packets arriving are stored at input buffers
  • Routing decision made
  • Packet moves to output buffer
  • Packets queued for output transmitted as fast as
    possible
  • Statistical time division multiplexing
  • If packets arrive to fast to be routed, or to be
    output, buffers will fill
  • Can discard packets
  • Can use flow control
  • Can propagate congestion through network

35
Figure 5.10 Interaction of Queues in a Data
Network
36
Figure 5.11 Ideal Network Utilization
37
Practical Performance
  • Ideal assumes infinite buffers and no overhead
  • Buffers are finite
  • Overheads occur in exchanging congestion control
    messages

38
Figure 5.12 The Effects of Congestion
39
Figure 5.13 Mechanisms for Congestion Control
40
Backpressure
  • If node becomes congested it can slow down or
    halt flow of packets from other nodes
  • May mean that other nodes have to apply control
    on incoming packet rates
  • Propagates back to source
  • Can restrict to logical connections generating
    most traffic
  • Used in connection oriented that allow hop by hop
    congestion control (e.g. X.25)
  • Not used in ATM nor frame relay
  • Only recently developed for IP

41
Choke Packet
  • Control packet
  • Generated at congested node
  • Sent to source node
  • e.g. ICMP source quench
  • From router or destination
  • Source cuts back until no more source quench
    message
  • Sent for every discarded packet, or anticipated
  • Rather crude mechanism

42
Implicit Congestion Signaling
  • Transmission delay may increase with congestion
  • Packet may be discarded
  • Source can detect these as implicit indications
    of congestion
  • Useful on connectionless (datagram) networks
  • e.g. IP based
  • (TCP includes congestion and flow control - see
    chapter 17)
  • Used in frame relay LAPF

43
Explicit Congestion Signaling
  • Network alerts end systems of increasing
    congestion
  • End systems take steps to reduce offered load
  • Backwards
  • Congestion avoidance in opposite direction to
    packet required
  • Forwards
  • Congestion avoidance in same direction as packet
    required

44
Categories of Explicit Signaling
  • Binary
  • A bit set in a packet indicates congestion
  • Credit based
  • Indicates how many packets source may send
  • Common for end to end flow control
  • Rate based
  • Supply explicit data rate limit
  • e.g. ATM

45
Traffic Management
  • Fairness
  • Quality of service
  • May want different treatment for different
    connections
  • Reservations
  • e.g. ATM
  • Traffic contract between user and network

46
Flow Control
  • Limits amount or rate of data sent
  • Reasons
  • Source may send PDUs faster than destination can
    process headers
  • Higher-level protocol user at destination may be
    slow in retrieving data
  • Destination may need to limit incoming flow to
    match outgoing flow for retransmission

47
Flow Control at Multiple Protocol Layers
  • X.25 virtual circuits (level 3) multiplexed over
    data link using LAPB (X.25 level 2)
  • Multiple TCP connections over HDLC link
  • Flow control at higher level applied to each
    logical connection independently
  • Flow control at lower level applied to total
    traffic

48
Figure 5.14 Flow Control at Multiple Protocol
Layers
49
Flow Control Scope
  • Hop Scope
  • Between intermediate systems that are directly
    connected
  • Network interface
  • Between end system and network
  • Entry-to-exit
  • Between entry to network and exit from network
  • End-to-end
  • Between end user systems

50
Figure 5.15 Flow Control Scope
51
Error Control
  • Used to recover lost or damaged PDUs
  • Involves error detection and PDU retransmission
  • Implemented together with flow control in a
    single mechanism
  • Performed at various protocol levels

52
Self-Similar Traffic
  • Predicted results from queuing analysis often
    differ substantially from observed performance
  • Validity of queuing analysis depends on Poisson
    nature of traffic
  • For some environments, traffic pattern is
    self-similar rather than Poisson
  • Network traffic is burstier and exhibits greater
    variance than previously suspected
  • Ethernet traffic has self-similar, or fractal,
    characteristic
  • Similar statistical properties at range of time
    scales milliseconds, seconds, minutes, hours,
    even days and weeks
  • Cannot expect that the traffic will "smooth out"
    over an extended period of time
  • Data clusters and clusters cluster
  • Merging of traffic streams (statistical
    multiplexer or ATM switch) does not result in
    smoothing of traffic
  • Multiplexed bursty data streams tend to produce
    bursty aggregate stream

53
Effects of Self-Similar Traffic (1)
  • Buffers needed at switches and multiplexers must
    be bigger than predicted by traditional queuing
    analysis and simulations
  • Larger buffers create greater delays in
    individual streams that originally anticipated
  • Self-similarity appears in ATM traffic,
    compressed digital video streams, SS7 control
    traffic on ISDN-based networks, Web traffic, etc.
  • Aside self-similarity common natural
    landscapes, distribution of earthquakes, ocean
    waves, turbulent flow, stock market fluctuations,
    pattern of errors and data traffic
  • Buffer design and management requires rethinking
  • Was assumed linear increases in buffer sizes gave
    nearly exponential decreases in packet loss and
    proportional increase in effective use of
    capacity
  • With self-similar traffic decrease in loss less
    than expected, and modest increase in utilization
    needs significant increase in buffer size

54
Effects of Self-Similar Traffic (2)
  • Slight increase in active connections through
    switch can result in large increase in packet
    loss
  • Parameters of network design more sensitive to
    actual traffic pattern than expected
  • Designs need to be more conservative
  • Priority scheduling schemes need to be reexamined
  • Prolonged burst of traffic from highest priority
    could starve other classes
  • Static congestion control strategy must assume
    waves of multiple peak periods will occur
  • Dynamic strategy difficult to implement
  • Based on measurement of recent traffic
  • Can fail to adapt to rapidly changing conditions
  • Congestion prevention by appropriate sizing
    difficult
  • Traffic does not exhibit predictable level of
    busy period traffic
  • Congestion avoidance by monitoring traffic and
    adapting flow control and routing difficult
  • Congestion can occur unexpectedly and with
    dramatic intensity
  • Congestion recovery is complicated by need to
    make sure critical network control messages not
    lost in repeated waves of traffic

55
Why Have We Only Just Found Out?
  • Requires processing of massive amount of data
  • Over long observation period
  • Practical effects obvious
  • ATM switch vendors, among others, have found that
    their products did not perform as advertised
  • Inadequate buffering and failure to take into
    account delays caused by burstiness

56
Required Reading
  • Stallings chapter 05
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