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The Cognitive Net is
Coming
Liotta,A
IEEE Spectrum, Vol. 50, No. 8, p.26-.
ISSN 0018-9235
August 2013
Introduction
 Major Internet service providers around the world are
now reporting global latencies greater than 120
milliseconds
 How slowly traffic would move if console gamers and
cable television watchers, who now consume hundreds
of exabytes of data off-line, suddenly migrated to cloud–
based services.
 The problem is not simply one of volume
 Network operators will always be able to add capacity by transmitting
data more efficiently and by rolling out more cables and cellular base
station.
 The real troubles lies with the technology at the heart of the internet is its
routing architecture
Challenges
The Path to Intelligent Routing
 The future Internet will need smarter routing algorithms
to handle diverse data flows and prevent failures
 Although there are no tried and true solutions yet, early
designs might follow an architecture like this one
 Routing Device
 Can be any network node, such as a phone, a television, a
car or an environment sensor
 The Routing and Forwarding Engines
 Determines the best pathways to get data packets to their
destinations and queue them for transmission
The Path to Intelligent Routing (Cont’d)
 The autonomic controller
 Directs the routing and forwarding engines by following the MAPE
loop: It monitors internal sensor data and signals from other nodes,
analyzes that information, plans a response, and executes it.
 The cognitive engine
 Helps the router adapt to unforeseen changes by following the
OOPDAL loop: It observes the environment, orients the systems by
prioritizing tasks, plans options, decides on a plan, acts on it and
learns from its action.
Intelligent Routing Software
Monitor-Analyze-Plan-Execute
(MAPE) loop
 One idea, proposed by IBM
 Algorithm of this architecture must perform four main
task:
 They monitor a router’s environment
 battery level,
 memory capacity,
 the type of traffic it’s seeing,
 the number of nodes it’s connected to, and
 the bandwidth of those connection
 The knowledge algorithms analyze all that data
 Use statistical techniques to determine whether the inputs are typical and if
they aren’t, whether the router can handle them
 Example: if the router that typically receives low-quality video streams
suddenly receives a high-quality one, the algorithms calculate whether the
router can process the stream before the video packers fill its buffer
Monitor-Analyze-Plan-Execute
(MAPE) loop (Cont’d)
 They plan a response to any potential problem
 Such as an incoming video stream that’s too large
 Example :They may figure the best plan is to ask the
video server to lower the stream’s bit rate. Or they may
find its better to break up the stream and work with other
nodes to spread the data over many different pathways.
 They execute the plan
 The execution commands may modify the routing tables,
tweak the queuing methods, reduce transmission power,
or select a different transmission channel, among many
possible actions
The cognitive engine
 This architecture used cognitive algorithms
 Unlike autonomic system, which rely on
predetermined rules, cognitive algorithm make
decisions based on experience
 Example :When you reach for a ball flying toward you,
for example, you decide where to position your hand
by recalling previous successes. If you catch the ball,
the experience reinforces your reasoning. If you drop
the ball, you will receive your strategy
The cognitive engine (Cont’d)
 Cognition algorithms orient the system by evaluating and
prioritizing the gathered information
 For low-priority actions, the algorithms consider alternative
plans. Then they decide on a plan and act on it, either by
triggering new internal behavior or by signaling nearby nodes
 When more-urgent action is needed, the algorithms can
bypass one or both of the planning and decision-making steps
 Finally, by observing the results of these actions, they would
generate prediction models that would continually modify the
knowledge algorithm, thereby improving the router’s ability to
manage diverse data flows

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The Cognitive Net is Coming

  • 1. The Cognitive Net is Coming Liotta,A IEEE Spectrum, Vol. 50, No. 8, p.26-. ISSN 0018-9235 August 2013
  • 2. Introduction  Major Internet service providers around the world are now reporting global latencies greater than 120 milliseconds  How slowly traffic would move if console gamers and cable television watchers, who now consume hundreds of exabytes of data off-line, suddenly migrated to cloud– based services.  The problem is not simply one of volume  Network operators will always be able to add capacity by transmitting data more efficiently and by rolling out more cables and cellular base station.  The real troubles lies with the technology at the heart of the internet is its routing architecture
  • 4. The Path to Intelligent Routing  The future Internet will need smarter routing algorithms to handle diverse data flows and prevent failures  Although there are no tried and true solutions yet, early designs might follow an architecture like this one  Routing Device  Can be any network node, such as a phone, a television, a car or an environment sensor  The Routing and Forwarding Engines  Determines the best pathways to get data packets to their destinations and queue them for transmission
  • 5. The Path to Intelligent Routing (Cont’d)  The autonomic controller  Directs the routing and forwarding engines by following the MAPE loop: It monitors internal sensor data and signals from other nodes, analyzes that information, plans a response, and executes it.  The cognitive engine  Helps the router adapt to unforeseen changes by following the OOPDAL loop: It observes the environment, orients the systems by prioritizing tasks, plans options, decides on a plan, acts on it and learns from its action.
  • 7. Monitor-Analyze-Plan-Execute (MAPE) loop  One idea, proposed by IBM  Algorithm of this architecture must perform four main task:  They monitor a router’s environment  battery level,  memory capacity,  the type of traffic it’s seeing,  the number of nodes it’s connected to, and  the bandwidth of those connection  The knowledge algorithms analyze all that data  Use statistical techniques to determine whether the inputs are typical and if they aren’t, whether the router can handle them  Example: if the router that typically receives low-quality video streams suddenly receives a high-quality one, the algorithms calculate whether the router can process the stream before the video packers fill its buffer
  • 8. Monitor-Analyze-Plan-Execute (MAPE) loop (Cont’d)  They plan a response to any potential problem  Such as an incoming video stream that’s too large  Example :They may figure the best plan is to ask the video server to lower the stream’s bit rate. Or they may find its better to break up the stream and work with other nodes to spread the data over many different pathways.  They execute the plan  The execution commands may modify the routing tables, tweak the queuing methods, reduce transmission power, or select a different transmission channel, among many possible actions
  • 9. The cognitive engine  This architecture used cognitive algorithms  Unlike autonomic system, which rely on predetermined rules, cognitive algorithm make decisions based on experience  Example :When you reach for a ball flying toward you, for example, you decide where to position your hand by recalling previous successes. If you catch the ball, the experience reinforces your reasoning. If you drop the ball, you will receive your strategy
  • 10. The cognitive engine (Cont’d)  Cognition algorithms orient the system by evaluating and prioritizing the gathered information  For low-priority actions, the algorithms consider alternative plans. Then they decide on a plan and act on it, either by triggering new internal behavior or by signaling nearby nodes  When more-urgent action is needed, the algorithms can bypass one or both of the planning and decision-making steps  Finally, by observing the results of these actions, they would generate prediction models that would continually modify the knowledge algorithm, thereby improving the router’s ability to manage diverse data flows

Editor's Notes

  1. Title ideas: (was) Cognitive and Information Science Others: The Cognitive Perspective in Information Science Research Cognitive Science and the Cognitive Perspective in IS: Background and Applications ??...
  2. Not discrete areas that can be studied without touching on the other areas. In LIS, ideas from each of these areas apply in overlapping and interconnecting ways. For example, in the process of building intelligent agents. Breakdown of general topic areas from: Eysenck, M.W. ed. (1990). The Blackwell Dictionary of Cognitive Psychology. Cambridge, Massachusetts: Basil Blackwell Ltd.
  3. Definition: Perception is the complex sequence of processes by which we take the information received from our senses and then organize and interpret it, which in turn allows us to see and hear the world around us as meaningful, recognizable objects and events with clear locations in space and time. (encyl. Of Cog Sci) Perception: Is limited Is selective Refers to the distal stimulus, not the proximal stimulus (the physical object or event, not the light/sound/whatever patterns that arrive at sensory receptors) Requires time Not entirely veridical (accurate, faithful, or with high fidelity) Requires memory Requires internal representations Is influenced by context
  4. Definition: The mental manipulation of symbolic representations of objects and of relations among objects Development of taxonomy of thinking skills: Verbal reasoning Argument analysis Thinking as hypothesis testing Likelihood and uncertainty Decision-making and problem-solving Reasoning -Thought as formal logic. Inferring  AI, etc. Stages of problem solving Mental models Stone, D J The influence of mental models and goals on search patterns during Web interaction Journal of the American Society for Information Science and Technology; 53 (13) 2002, p.1152-1169