Computer Networks with Internet Technology William Stallings

1
Computer Networks with Internet
TechnologyWilliam 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 5.5Effective 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 5.11Ideal NetworkUtilization
37
Practical Performance

• Ideal assumes infinite buffers and no overhead
• Buffers are finite
• Overheads occur in exchanging congestion control
  messages

38
Figure 5.12The 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 5.15Flow 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