Some Thoughts on Future Networks

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Some Thoughts on Future Networks

• Tatsuya Suda
• Professor, University of California
• (web) netresearch.ics.uci.edu
• (email) suda_at_ics.uci.edu

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Outline

• Where are we now?
• Where are we heading?
• What do we need to do?

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Where are we now?Next Generation Internet (NGI)
Initiative
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Major Departments
Independent Agencies
NSF-9
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NGI (Next Generation Internet)

• NSF, DARPA, NASA, NIST, NIH, (DoE)
• Large Scale Networking working group (in the
  National Science and Technology Council under the
  White House) to coordinate multi-agency efforts

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NGI (Next Generation Internet)

• 3 NGI Goals
• Goal 1 Basic Network Research
• Goal 2 NGI Testbed
• Goal 3 Evolutionary Applications

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vBNS Backbone Network Map
CTC Cornell Theory Center
NOR Cleveland
C
Ameritech NAP
DNG Chicago
NCAR National Center for Atmospheric Research
WOR New York City
C
C
A
C
Sprint NAP
C
C
HAY San Francisco
C
A
PSC Pittsburgh Supercomputer Center
PYM Perryman, MD
A
C
C
DNJ Denver
C
C
Pac Bell NAP
NCSA National Center for Supercomputing Applicatio
ns
C
RTO Los Angeles
MFS NAP
C
A
C
AST Atlanta
C
HSJ Houston
SDSC San Diego Supercomputer Center
C
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California Research Network-2 (CalREN-2)
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CalREN-2 - Southern Region
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U.C. Irvine ATM Network
Sparc5
ICS Department
TAXI
Sparc5
OC-3
OC-3
Pentium
Pentium
OC-3
OC-3
Gigaport
ISI
OC-12
OC-3
SCCSD
OC-3
OC-3
7507
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STAR TAPScience Technology and Research Transit
Access Point
http//www.startap.net
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Canadas CAnet II Network
Source http//www.canarie.ca/frames/n3.html
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Europe TEN-34Trans-European Network 34 Mbps
Source http//www.dante.net/ten-34/ten34net.gif
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APAN - Asia Pacific Advanced Network
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Applications

• Digital libraries, remote operation of medicine,
  environment, crisis management, manufacturing,
  basic science, Federal information services

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Virtual Temporal Bone

• VR model
• viewers explore using a wand and a special pair
  of eyeglasses while facing ImmersaDesk
• surgeons familiarize themselves with the complex
  structures composing the ear

Source http//www2.sbhis.uic.edu/VRML/vtb0.htm
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CAVE Research Network CAVERN

• next generation collaborative networking
  infrastructure
• alliance of industrial and research institutions
• equipped with CAVEs, ImmersaDesks, and
  high-performance computing resources
• interconnected by high-speed networks

CAVE with NICE for Children
Source http//www.evl.uic.edu/spiff/covr/vrserver
.html
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SuperComputing97 Demo from MPI-Garching
Gravity Waves from Two Black Holes Colliding
3-D
Source http//jean-luc.ncsa.uiuc.edu/Movies/Image
s
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• Similar IT project started in Japan last year

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Where are we now?Example Internet Traffic
Control Schemes
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• Need new scalable techniques/mechanisms
• traffic control
• QoS support

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• Researchers
• This conference
• CalREN 2 Experiments
• QoS Routing
• Muticast
• IPv6
• IETF
• QoS Policy Server Architectures
• MPLS, Traffic Engineering
• Many other topics being discussed

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Current Internet

• Philosophy
• Simple, robust and scalable
• Complexity pushed to the edges of the network
• End-to-end flow and congestion control
• Core-stateless Network
• Core routers are NOT required to
• Keep per-flow state accounting or queueing
• Perform per-flow processing or scheduling
• Implement per-flow congestion/flow control

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• Simple and scalable
• Limited controllability
• Some shortcomings
• Congestion collapse
• Unfair bandwidth allocation

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Core-stateful Networks

• ATM and Telephone networks
• Virtual circuit (VC) based
• Fixed path
• Routers or switches keep per VC state (VC
  forwarding table) and may perform per VC tasks
• Can guarantee quality of service
• Complex and expensive to deploy

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Core-stateless Networks

• Core routers are NOT required to
• Keep per-flow state accounting or queueing
• Perform per-flow processing or scheduling
• Implement per-flow congestion/flow control
• Simple and scalable
• Limited controllability

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Example Core Stateless MechanismRandom Early
Drop (RED)

• Drop a new packet with a probability dependent on
  the buffer occupancy
• not a fixed threshold
• Example

4
3
2
1
5
P(drop) 1 - e-bb
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Weighted Fair Queueing (WFQ)

• Flow classifier places packets in per-flow queues
  (core-stateful approach)
• Scheduler (e.g. deficit round robin) fairly
  serves the per-flow queues
• Packets from flows transmitting at lower rates
  experience lower delays!

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Example Core Stateless Mechanism Core-stateless
Fair Queueing (CSFQ)

• Packets are labeled by ingress routers with the
  flow input rate
• Packets are placed in a single FIFO queue
• If the queue occupancy is higher than a
  threshold, Drop-on-input module probabilistically
  drops packets from flows transmitting at a rate
  higher than their estimated fair share
• If the queue occupancy is lower than a threshold,
  CSFQ is ineffective
• Packets from all flows experience the same delay!

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Current Internet

• Philosophy
• Simple, robust and scalable
• Complexity pushed to the edges of the network
• End-to-end flow and congestion control
• Some shortcomings
• Congestion collapse
• Unfair bandwidth allocation

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Congestion Collapse
Bottleneck link
Dest
Video Source
Dest
FTP Source
Undelivered packets
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Unfairness due toUnresponsive Flows
Video Source
Dest
FTP Source
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Unfairness due toLong Propagation Delay
Dest
1
FTP Source
10
FTP Source
Dest
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New MechanismNetwork Border Patrol (NBP)

• Our solution
• Core-stateless congestion avoidance mechanism
• How it worksUses adaptive feedback-based traffic
  policing to match the input and output rates
• Egress routers return explicit rate feedback to
  ingress router
• Ingress routers police traffic at input
• TCP-friendly rate control algorithm at ingress
  routers
• Feedback control algorithm detects congestion by
  monitoring the round trip time (RTT)

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Network Border Patrol (NBP)Example Illustration
6 Mbps
4 Mbps
6 Mbps
4 Mbps
4.1 Mbps
6 Mbps
4 Mbps
4 Mbps
4 Mbps
6 Mbps
6 Mbps
6 Mbps
6 Mbps
4 Mbps
4 Mbps
4.1 Mbps
4.1 Mbps
FLOW 1
FLOW 2
6 Mbps
4 Mbps
2 Mbps
2 Mbps
2 Mbps
2 Mbps
2 Mbps
2 Mbps
2 Mbps
2 Mbps
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Network Border Patrol (NBP)Example Illustration
FLOW 1
FLOW 2
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Network Border Patrol (NBP)Example Illustration
FLOW 1
FLOW 2
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Network Border Patrol (NBP)Example Illustration
FLOW 1
FLOW 2
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Network Border Patrol (NBP)

• NBP architectural components
• Ingress routers police and rate control input
  traffic
• Egress routers monitor traffic and return
  congestion feedback
• per flow base at edge routers
• Core routers unchanged
• stateless at core routers
• End systems unchanged
• NBP Internals
• Feedback control algorithm
• Rate control algorithm

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NBP Architectural ComponentsIngress Router
Output port of an NBP ingress router
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NBP Architectural ComponentsEgress Router
Input port of an NBP egress router
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NBP Feedback Control AlgorithmFeedback Control
Packet Format
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Example of the Problem of Unfairness due to
Unresponsive Flows
Current Internet

• Long queues at routers
• Unresponsive flow consumes most network
  bandwidth

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Example of NBP protection against unresponsive
flows
Internet with Network Border Patrol

• Short queues
• Fairer bandwidth allocation among competing
  flows
• Unresponsive flows packets are dropped at
  network input

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• Up to now
• application aggregated into a smaller number of
  flows
• flows hopefully are long lasting and not too
  dynamic
• control flows
• Future
• Current model may not be valid

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• Is what we do really what we need in future
  networks?

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Where are we heading?
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BackgroundProcessing Capability Everywhere

• Boundaries between game machines, home
  appliances, PCs, mobile phones are disappearing

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• Sony Playstation II
• hardware highlights
• 128 bits CPU, support for high definition 3D
  graphics
• 32 MB SRAM
• CD-ROM/DVD
• two USB ports
• one 1394 port
• one PCMCIA card slot
• Not just a game machine, it is a computer

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• Digital TV, Internet TV
• Watch TV programs on your PC and lap top
• Boundary between PCs/laptops and home appliances
  disappearing

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• Home appliances are being connected through a
  network
• Wired
• HomePNA (Home Phoneline Networking Alliance)
• Ethernet/Fast Ethernet
• IEEE 1394
• Wireless
• Bluetooth
• IEEE 802.11

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• Wireless phone are becoming an information
  processing terminal
• I-mode by NTT Docomo

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• One dimension of network expansion
• home (home appliances, game machines)
• portable terminals (mobile phones, lab tops)

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Background Wearable Computers

• Wearable computers
• advanced forms (i.e., extremely small, light
  weight, integrated functions, evolvable,
  flexible) of
• wireless phone, hand held computers, video game
  terminals
• IC cards (digital cash, ID, bank cards, credit
  cards)
• googols
• CPUs in clothing
• Direct brain-computer interface

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• One dimension of network expansion
• small devices/chips on human users

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BackgroundSensor Net and Autonomous Device Net

• Network of very small sensor devices
• device miniaturization (dust sensors, sensors in
  blood vessel, active bar codes, sensors in
  freeway)
• sensor very simple function, simple
  communication capabilities

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• Dust sensors
• Float in the air to monitor environment
  conditions
• Sensor robots for Mars exploration
• Sensors for military applications
• Dump small sensors in the enemy territory
• Bio-degradable

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• Network of autonomous devices
• autonomous sensors
• mobile
• self-organizing to collectively perform one task
• flexible/evolvable (hardware and software)
• NASA, Mars exploration
• robots

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• One dimension of network expansion
• extremely small devices everywhere
• autonomous devices

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Future Networks

• Diverse network connecting human users and
  non-human devices everywhere (home appliances,
  mobile phones, wearable computers, small devices
  and autonomous devices)
• Extremely large scale network

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Background Applications

• Applications for fun
• email through mobile phone, i-mode (chat with
  friends)
• digital pets (e.g., pokemon, SONY robot dog)
• video games (e.g., interactive network games with
  high definition 3D graphics)
• players across the network (racing, fighting/war)
• of games for wireless phones at Tokyo game
  show, fall 00

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Future ApplicationsSome Examples

• Support for a large number of highly dynamic,
  highly customized small communities of users
• ad hoc creation of a net at a Michael Jacksons
  concert after the concert, ad hoc creation of a
  net in Rome among those who are pop music funs
  etc., etc.

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• Extending human presence everywhere
• realistic and personalized/customized
  communication services
• transmitting
• human emotions/feelings (happy, sad, etc.)
• smells
• taste
• to appeal to humans 5 senses

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• Virtual Society
• all human social functions done in a virtual
  society
• cyber-self (software entity representing you)
• conducts social functions (i.e., moves around on
  a net, meets other cyber-selves, make friends)
• conducts economic functions (i.e., conducts
  business (e.g., buy and sell goods))
• conducts biological functions (i.e., mates,
  produces off springs)

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• Highly customized and personalized applications
• Future applications are NOT
• digital libraries
• remote operation of medicine
• distance learning
• Federal information services

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Future Networks

• Extremely large scale, diverse network connecting
  human users and various devices
• Highly dynamic, diverse, realistic, and
  personalized applications

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• application aggregated into a small number of
  flows model may not be valid
• private
• highly dynamic
• diverse traffic characteristics
• large scale, but may have locality
• I dont need to control a refrigerator in Rome
• hot spots

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What do we need to do?
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• Are Higher speed and better QoS important? Not
  sufficient to handle
• scalability
• diversity (of users, of applications)
• dynamic environments
• realistic communications
• customizability/personalization
• adaptability/flexibility/evolvability

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Applications

• Identify promising applications
• Study application requirements, and design a net
  based on them

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• Example Internet TV
• does not necessary need higher speed and better
  QoS
• popular usage will be
• set top box at home screens TV programs/shows
  based on user preference
• stores them in a storage device at home
• view them later when users have time
• no strict QoS and high speed requirement on
  networks

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• Example
• QoS (location accuracy, reliable communication)
  may not be important for some applications (e.g.,
  for sensors)
• world-wide roaming may not be important for
  highly personalized friends-chatting-with-friends
  applications

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Diverse Applications over a Small Set of
Infrastructures

• Need to support a huge number of highly
  personalized diverse applications
• Interface one-size fits all underlying net to
  diverse applications
• where and how?
• middleware becomes important

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• Middleware needs to map application level QoS
  onto network level QoS
• QoS aware middleware
• Application, middleware, network mechanisms
  helping each other to achieve QoS

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Distributed/Local Architecture

• Pursue 100 distributed network architecture
• no central or coordinating entities
• assume autonomous entities
• build a large scale system from a simple
  localized algorithms/behaviors/designs
• example no directory service
• discover when needed by asking friends

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• Accept overhead
• we gain other features (self-organization and
  -coordination, flexibility, adaptability, service
  emergence and evolution, etc.)

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Example Bio-Networking

• Observation large scale biological systems
  scale, adapt, and survive
• Apply biological concepts/mechanisms to future
  Internet applications
• emergent behavior (out of simple behaviors)
• lifecycle (food/energy, reproduction, death)
• evolution through diversity and natural selection
• etc.

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• Bio-Net
• individuals cyber-entities (objects/agents)
• abstraction of various system components (users,
  resources, service components)
• autonomous with simple behaviors
• e.g., replication, reproduction, migration,
  energy exchange, relationship establishment,
  pheromone emission, death
• makes its own decision, according to its own
  behavioral policy

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• Cyber-entity example behaviors
• energy exchange
• gain energy from a cyber-entity (e.g., a user) in
  exchange for performing a service
• expend energy to receive service from other
  cyber-entities (e.g., to use network/computing
  resources)
• can be used as a natural selection mechanism
• evolution

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• relationship establishment
• a cyber-entity knows something (e.g., name,
  address, service type) about another cyber-entity
• amount of information
• strength of relationship
• service emergence

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Vision

• No central or coordinating entity exists.
• A large number of CEs (created by millions of
  millions of Internet users), autonomously
  moving/replicating,
• CEs contacting other CEs providing related
  services, making relationship,
• diverse behavior policies getting created, good
  behaviors survive, bad ones die, making system
  flexible, adaptable and evolvable

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Economic and Social Implications of IT

• Social, economic and workforce issues related to
  information technology
• legal and regulatory constraints
• tax treatment of investment in information
  technologies
• workforce skilled in the use of information
  technologies

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• Are we doing something good to ourselves?
• Email is very convenient, but
• Flooded with junk emails, and cannot find time to
  do what I want to do
• Wireless phone is very convenient, but
• It is very annoying in a train
• It is a health risk to those who wear heart pace
  maker.

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What kind of techniques and mechanisms do we
need? (near term)
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Examples of Techniques We Need

• Flexible/modular network architecture
• easy to add more (or reallocate existing)
  resources (hard/soft resources such as CPU,
  buffer, finer level resource)
• Mechanisms to help processing within a network
• not transmit fast, process at end systems
• process data on transit in a network
• consider link/network as buffer
• a link with data processing capability?

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• Mechanisms to build a large scale system from a
  simple localized algorithms, behaviors, designs
• self-organization and coordination mechanisms
• service finding mechanisms
• resource allocation/management mechanisms

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• Mechanisms to anticipate/predict and prepare
• to give users illusion of a dedicated high speed,
  good quality network
• Understanding large scale networks
• network modeling and simulation

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Examples from Soft/Middleware

• Active/smart software
• software that updates itself, monitors its
  progress toward a particular goal,
  discovers/downloads a new capability needed for
  the task at hand
• Self-organization, -coordination techniques

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Summary
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• Tremendous Internet growth
• Weve been busy doing near term research
• We probably need to take a moment and think what
  future looks like and what we need to do

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• Thanks!

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Bio-Networking Architecture
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Motivation

• Network services/applications need to be
• scalable
• adaptable (to heterogeneous/dynamic conditions)
• survivable and available
• simple/easy to design and maintain
• Networks need to have built-in mechanisms to
  provide these features
• large nets beyond ones capability to design

104

• Observation large scale biological systems
  scale, adapt, and survive
• Apply biological concepts/mechanisms to future
  Internet applications
• emergent behavior (out of simple behaviors)
• lifecycle (food/energy, reproduction, death)
• evolution through diversity and natural selection
• etc.

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Emergent Behavior

• Biological systems
• useful group behavior emerges from autonomous
  local interaction of individuals with simple
  behaviors

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• Bio-Net
• individuals cyber-entities (objects/agents)
• abstraction of various system components (users,
  resources, service components)
• autonomous with simple behaviors
• e.g., replication, reproduction, migration,
  energy exchange, relationship establishment,
  pheromone emission, death
• makes its own decision, according to its own
  behavioral policy

107

• Cyber-entity example behaviors
• energy exchange
• gain energy from a cyber-entity (e.g., a user) in
  exchange for performing a service
• expend energy to receive service from other
  cyber-entities (e.g., to use network/computing
  resources)
• can be used as a natural selection mechanism

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• relationship establishment
• a cyber-entity knows something (e.g., name,
  address, service type) about another cyber-entity
• amount of information
• strength of relationship

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• relationship can be used to group cyber-entities
  collectively providing a service
• application constructed from a collection of
  cyber-entities
• e.g., a web server (application) from a
  collection of web pages (cyber-entities)

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Evolution and Adaptation

• Biological systems
• the biological system adjusts itself for
  environmental changes of long-term and short-term
• key components
• diversity from mutations and crossovers during
  replication/reproduction
• natural selection keeps entities with beneficial
  features alive and increase reproduction
  probability

111

• Bio-Net
• cyber-entities (CEs) evolve, adapt, and localize
  through diversity and natural selection
• diversity
• A CE behavior can be implemented by a number of
  algorithms/policies
• human designers can introduce diversity in CE
  behaviors
• CEs replicate/reproduce with mutation/crossover
  in behavior policies

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• natural selection (energy as a natural selection
  mechanism)
• death from energy starvation
• tendency to replicate/reproduce from energy
  abundance

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Example Application
Image analyzer CE
Video camera CE
Rock MP3 CE
Sensor CE
Stereo CE
Some CE
Latin MP3 CE
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Example Application
Image analyzer CE
Video camera CE
Rock MP3 CE
Sensor CE
Some CE
Latin MP3 CE
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Example Application
Image analyzer CE
Video camera CE
Rock MP3 CE
Sensor CE
Some CE
Latin MP3 CE
116
Example Application
Image analyzer CE
Video camera CE
Rock MP3 CE
Sensor CE
Stereo CE
Some CE
Latin MP3 CE
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Example CE BehaviorAdaptation Simulation

• Cyber-entity behaviors implemented
• replication, death, migration

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Cyber-Entity Behaviors

• Replication
• If current energy level gt aggressiveness, then
  create a new entity of same type
• Death
• if current energy level 0, then, die
• Migration
• migrate towards source of energy (user requesting
  service)
• avoid coexisting on a node with same entity

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Energy Seeking Entity (Simulation 1)
Entity 3 w1 .575, w2 .45, aggress 4.5
Entity 1 w1 .5, w2 .5, aggress 4
Entity 2 w1 .425, w2 .575, aggress 2.25
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Energy Seeking Entity (Simulation 1)
Entity 3 w1 .575, w2 .45, aggress 4.5
Entity 1 w1 .5, w2 .5, aggress 4
Entity 2 w1 .425, w2 .575, aggress 2.25
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Energy Seeking Entity (Simulation 1)
Entity 3 w1 .575, w2 .45, aggress 4.5
Entity 1 w1 .5, w2 .5, aggress 4
Entity 2 w1 .425, w2 .575, aggress 2.25
122
Energy Seeking Entity (Simulation 1)
Entity 3 w1 .575, w2 .45, aggress 4.5
Entity 1 w1 .5, w2 .5, aggress 4
Entity 2 w1 .425, w2 .575, aggress 2.25
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Energy Seeking Entity (Simulation 1)
Entity 3 w1 .575, w2 .45, aggress 4.5
Entity 1 w1 .5, w2 .5, aggress 4
Entity 2 w1 .425, w2 .575, aggress 2.25
124
Vision

• No central or coordinating entity exists.
• A large number of CEs (created by millions of
  millions of Internet users), autonomously
  moving/replicating,
• CEs contacting other CEs providing related
  services, making relationship,
• diverse behavior policies getting created, good
  behaviors survive, bad ones die, making system
  flexible, adaptable and evolvable

125
Research Philosophy

• Pursue 100 distributed architecture
• no central or coordinating entities
• no directory service
• assume only autonomous individuals
  (cyber-entities)
• Accept overhead
• we gain other features (flexibility,
  adaptability, service emergence and evolution,
  etc.)

126
Research Issues

• Identify Key Biological Concepts
• Bio-Networking Architecture Designs
• cyber-entity design
• platform software design
• Discovery Mechanisms
• Evolution and Adaptation
• Application Designs

127
Current Status

• Preliminary simulations for web type service
  done
• showed adaptability of the Bio-Net
• Currently working on
• theoretical study of stability conditions
• platform design
• application design
• dynamic creation of user community
• e-commerce, Internet ad applications
• security for energy exchange

128

• implicit service model
• CEs sense the environment (e.g., user), and
  automatically start service when certain
  conditions are met (without an explicit request
  from a user)
• Only the CEs within users vicinity respond and
  collaborate to provide service
• depending on which CEs are nearby, services
  differ (no repeatability)

129
Summary

• Bio-Net a new paradigm
• Applications constructed using the Bio-Net are
  adaptable, evolvable, secure, survivable,
  scalable, and simple.
• plans
• mathematical analysis
• simulations
• implementations