Fuzzy logic based congestion control

1
Fuzzy logic based congestion control

• Andreas Pitsillides,
• Department of Computer Science
• University of Cyprus

Ahmet Sekercioglu, Department of Informatics
Swinburne University of Technology
2
Congestion control problem

• Generally accepted that
• network congestion control remains critical issue
  and high priority, especially given growing size,
  demand, and speed (bandwidth) of the increasingly
  integrated services networks.
• Despite research efforts spanning few decades
  and large number of different schemes proposed,
• no universally acceptable control solutions in
  fight against congestion (a control strategy, a
  control system, or a package of control
  solutions).
• Current solutions in existing networks
• increasingly becoming ineffective, and
• generally accepted that these cannot easily scale
  up even with various proposed fixes.

3
congestion

• Definition
• network state in which performance degrades due
  to saturation of network resources,
  (communication links, network switches, processor
  cycles, and memory buffers).
• E.g. if communication link delivers packets to
  queue at higher rate than its service rate, then
  size of queue grows. If queue space finite then
  in addition to delay, losses occur
• Control
• refers to set of actions taken by network to
  minimise intensity, spread, and duration of
  congestion.
• optimal control of networks of queues
• well-known, much studied, and notoriously
  difficult problem, even for simplest of cases.
  E.g. Papathemetriou and Tsitsiklis show problem
  of optimally controlling simple network of queues
  with simple arrival and service distributions and
  multiple customer classes is complete for
  exponential time (i.e. provably intractable).

4
Congestion control

• effect of network congestion is degradation in
  the network performance.
• user experiences long delays in delivery of
  messages, perhaps with heavy losses caused by
  buffer overflows.
• Thus degradation in quality of delivered service,
  with need for retransmissions of packets (for
  services intolerant to loss). In event of
  retransmissions, drop in throughput, eventually
  leading to network throughput collapse.
• Congestion is a complex process to define.
• felt by degradation of performance.
• loss, delay and throughput deterioration good
  indicators of congestion.
• A good congestion control system should be
  preventive, if possible. Otherwise should react
  quickly and minimise spread of congestion and its
  duration.

5
Congestion can be sensed (or predicted) by

• packet loss
• sensed by the queue as an overflow,
• sensed by destination (through sequence number)
  and acknowledged to a user,
• sensed by sender due to lack of acknowledgment
  (timeout mechanism) to indicate loss
• packet delay
• can be inferred by the queue size,
• observed by destination and acknowledged to user
  (e.g. with time stamps in packet headers)
• observed by sender, e.g. by packet probe to
  measure Round Trip Time (RTT)
• loss of throughput
• observed by the sender queue size(waiting time in
  queue)
• other calculated or observed event through which
  congestion can be inferred
• increased network queue length and its growth
• calculated from measured data, e.g queue inflow
  and its effect on future queue behaviour.

6
potential problems of control

• Large scale
• Distributed nature
• Large geographic spread (at its limit it covers
  globe)
• Increasingly processing delay at nodes gets
  smaller, in comparison to propagation delay in
  links. Large-bandwidth delay product makes
  control of congestion through feedback
  potentially difficult
• Diverse nature and behaviour of carried traffic
  (voice, video, www, ftp, e-mail, .)
• Unpredictable and time varying user behaviour
• Lack of appropriate dynamic models for control
• Expectation of the need for guaranteed levels of
  performance to each user, which can be negotiated
  with the network

7
(lack of) modelling of network system

• To design control system necessary to model input
  and output cause and effect of system.
• difficulty of deriving such model
• Several factors affect model, and some are time
  varying. Take model of traffic behaviour as
  example
• diverse user (human) behavior affects way traffic
  is generated (can be time varying and different
  for different humans, even for same interactive
  services).
• inherent fuzzines, e.g, in
• definition of contract between user and network
  and its policing,
• in the controls (declared objectives of controls
  and observed behaviour of the system).
• Data generation, organisation and retrieval (long
  range dependance shown for both source
  generation, as well as storage of data)
• Traffic aggregation process is a very complex
  onestudies suggest self-similarity preserved
  under variety of network operations and network
  conditions
• Network controls (speculation that fractal
  features in network traffic remain even after
  network controls).
• Network evolution (self-similarity appears robust
  to network changes, eg. upgrades).

8
control theoretic concepts

• Making use of control theoretic concepts has
  potential benefits, including
• Simpler an/or more effective algorithms with more
  predictable properties.
• Better understanding of performance of controlled
  system (including dynamic behaviour).
• Better understanding of existing non-linear
  algorithms, including need for any fixes
  (jacketing software).
• Better analysis techniques for large systems of
  interacting algorithms.
• A major difficulty in control system design is to
  reconcile the large-scale, fuzzy, real problems
  with the simple well-define problems that control
  theory can typically handle
• good understanding of fundamental control theory
  (which can be sophisticated and complex), as well
  as deep understanding of system under control
  (not necessarily in form of accurate mathematical
  model).

9
Existing approaches to congestion control and
trends

• For historical reasons, and due to fundamental
  philosophical differences earlier approach to
  congestion control, differed between TCP/IP and
  ATM
• However some convergence between classical TCP/IP
  and ATM approach evident, (RFC2309, RFC2481, ATM
  Forum).
• become clear that existing TCP congestion
  avoidance mechanisms (RFC2001), while necessary
  and powerful, not sufficient.
• Basically, there is a limit as to how much
  control can be accomplished from edges of network
  
• Some mechanisms needed in routers to complement
  endpoint congestion avoidance mechanisms. (Need
  for gateway control realised early e.g. see
  Jacobson, 1988, where for future work gateway
  side advocated as necessary).
• Evolutionary, for TCP/IP and ATM we see
• progressive shift of controls from edges of
  network (initially open loop then edge binary
  feedback) to inside network. Feedback also
  shifting from implicit to explicit, from pure
  binary to multivalued and explicit.

10
Network controls

• network system
• large distributed complex system, with difficult
  often highly non-linear, time varying and chaotic
  behaviour.
• inherent fuzziness in definition of controls
  (declared objectives and observed behaviour).
• Dynamic or static modelling of such system for
  (open or closed loop) control is extremely
  complex.
• Measurements on the state of the network are
  incomplete, often relatively poor and time
  delayed.
• Its sheer numerical size and geographic spread
  are mind-boggling. E.g. customers (active
  services) in 10s of millions, network elements in
  100s of million, and global coverage.
• Computational intelligence to handle complexity
  and fuzziness present in network system surely
  has an essential role to play here. We should
  exploit tolerance for imprecision and uncertainty
  to achieve tractability, robustness and low cost

11
Computational Intelligence (CI)

• area of fundamental and applied research
  involving numerical information processing (in
  contrast to symbolic information processing
  techniques of Artificial Intelligence (AI)).
• Nowadays, CI research is very active and
  consequently its applications are appearing in
  some end user products.
• definition of CI can be given indirectly by
  observing the exhibited properties of a system
  that employs CI components
• A system is computationally intelligent when it
  deals only with numerical (low-level) data, has a
  pattern recognition component, and does not use
  knowledge in the AI sense and additionally, when
  it (begins to) exhibit
• computational adaptivity
• computational fault tolerance
• speed approaching human-like turnaround
• error rates that approximate human performance.
• The major building blocks of CI are artificial
  neural networks, fuzzy logic, and evolutionary
  computation.

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Fuzzy Explicit Rate Marking (FERM) for congestion
control

• Basic idea
• measure queue length and queue growth rates
  (hence providing rudimentary prediction of future
  behaviour) at the output buffer of a switch,
• calculate and send explicit rate control signals
  to traffic sources to avoid or alleviate
  congestion.
• explicit rate control signals calculated
  periodically by fuzzy inference engines located
  in switches, and sent to traffic sources in
  resource management (RM) cells.
• analyzed and compared performance of FERM with
  EPRCA in detail regarding fairness,
  responsiveness, resource utilization and cell
  loss in LAN and WAN environments. FERM has been
  further refined (FERM2) and as an adaptive scheme
  which has self tuning capabilities (A-FERM).

13
FERM2 explicit rate congestion control scheme

• Note desired queue length is explicit, can be
  set by a higher level control module to provide
  more dynamic resource utilization across switches
  utilised by VC

14
FERM 2 Operation

• Overall operation compliant with ATM Forum
  Traffic Management Specification, Version 4.
• Source Cell rates adjusted by Explicit Rate (ER)
  carried by RM cells.
• RM cells periodically generated by traffic
  sources, transmitted towards destination end
  systems, and initial ER information is set by the
  ICR.
• Destination end systems bounce RM cells back to
  sources.
• During return path, when RM cell passes through
  ATM switch, its ER value is examined and possibly
  modified.
• Data source, upon receiving RM cell, adjust cell
  rate ER. If ERgtPCR, cell ratePCR. Similarly,
  cell rateMCR if ERltMCR.
• ER provided to all active VCs at all time so
  congestion and undesired resulting behavior can
  be avoided.
• does not need to keep state of current VC
  connections sharing switch.
• Periodical ER calculations are performed by the
  Fuzzy Congestion Controllers (FCCs) located in
  each ATM switch.

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implementation of FCC

• Chosen most widely used and computationally
  lighter methods, which are
• singleton fuzzification
• t-norm algebraic product for the mathematical
  representation of the connective and
• Larsen's product rule of implication
• sup-product compositional rule of inference
• weighted mean of maximums defuzzification.

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Control surface
if queue length and queue then flow rate
too short decreasing fast increase
sharply slowly
moderately not
changing
increasing slowly decrease moderately
fast
acceptable decreasing
fast increase moderately
slowly
not changing no
change increasing slowly
decrease moderately increasing
fast too high
decreasing fast no change
slowly
not changing decrease moderately
increasing slowly
sharply fast

Set of linguistic rules defining the control
surface of the FCC
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network model for performance analysis
simulation of ATM WAN (1500 km between switches)
backbone and LAN backbone (10 km)
Real time sources
ABR sources 3-hop
10/1500 km
ABR sources 1-hop
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simulated ATM LAN under FERM2 congestion control
average end-to-end ABR cell delay vs. useful
throughput
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simulated ATM WAN under FERM2 congestion control
average end-to-end ABR cell delay vs. useful
throughput
20
Time evolution of Explicit Rate for case of LAN
calculated by the FCC
21
Time evolution of the queue length for the case
of a LAN. Note that the reference value set at
400 cell places.
22
Time evolution of Explicit Rate for the case of
the WAN calculated by the FCC
23
Time evolution of the queue length for the case
of a WAN. Note that the reference value is set at
400 cell places.
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Conclusions and recommendations

• congestion control in communication networks is a
  real challenge, especially supporting video,
  voice and data applications simultaneously.
• Computational Intelligence techniques expected to
  play central role, especially in large scale,
  geographically distributed network systems.
  Hybrids also expected to supplement these
  techniques and prove useful, especially in
  optimising overall network objectives.
• challenges include
• Agreement on structured approach to congestion
  control for network. Control theoretic concepts
  and techniques have essential role to play.
• Engineer network system with network control
  system together in order to add another degree of
  flexibility.
• Theoretical advances in handling large scale
  complex systems are required, including
  decomposition and organisation of controls.
• Globally optimise overall network objectives.
• Agreement in common simulative framework and
  common test bed framework for testing congestion
  control algorithms