Bio-Networking Architecture

1
Bio-Networking Architecture

• Michael Wang
• Larry Chen
• Professor Tatsuya Suda
• UCI Network Research Group
• February 26, 1999

2
Outline of Talk

• Motivation and Overview of Bio-Networking
  Architecture
• Example application of Bio-Networking
  Architecture and its features
• adaptation and evolution
• security and survivability
• scalability and simplicity
• Bio-Networking Architecture implementation

3
Motivation

• Requirements for Future Network Services and
  Applications
• Adapt to heterogeneous and dynamic conditions
• Secure
• Survivable
• Scalable
• Easy to design and manage

4
Large Scale Biological Systems

• Example bee colony
• Key features
• Adapt to environment by direction action
• Evolve to more optimal forms
• Secure
• Survivable
• Scalable
• Relatively simple components (individual bees)
• Unifying concept emergent behavior

5
Bio-Networking Architecture

• Model the construction of network services and
  applications after biological systems
• A service or application is implemented by a
  distributed, collective entity called a
  super-entity
• A super-entity consists of multiple, autonomous
  entities called cyber-entities
• Cyber-entities have behaviors similar to
  biological entities
• migration, reproduction, mutation, protection,
  interaction

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Bio-Networking Architecture
super-entity
Individual cyber-entities
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Bio-Networking Architecture

• Two additional concepts
• Exchange of services for energy (food, money)
• Cyber-entities try to gain as much energy as
  possible, while expending as little as possible
• Cyber-entities can store energy
• Directory or service discovery capability called
  Social Networking
• Well suited for Bio-Networking Architecture
  because it can find mobile, replicated
  cyber-entities

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Bio-Networking Architecture

• Social Networking
• Cyber-entities establish relationships with
  family and friend cyber-entities
• Cyber-entities with relationships inform each
  other of their super-entity membership and
  current network location
• When a cyber-entity wants to find a cyber-entity
  belonging to another super-entity (a web page or
  a service), it queries its family and friends
• If they dont have a relationship with a
  cyber-entity in the target super-entity, then
  they query their family and friends
• A set of desired cyber-entities may be returned
  from this procedure. The querying cyber-entity
  can determine which of the desired cyber-entities
  has the best response time.

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Bio-Networking Architecture

• Social Networking Issues
• Overhead recursive querying of friends and
  family may cause large amounts of network traffic
• queries may be qualified by a maximum propagation
  count
• initial queries have low propagation count which
  is increased if previous queries did not find any
  desired cyber-entities
• Determinism social network may be partitioned so
  that one set of cyber-entities cannot find
  another set
• need simulation and analysis to determine
  probability of this scenario
• Security a cyber-entity can lie about who it has
  a relationship with
• the cyber-entity can compare multiple responses

10
Outline of Talk

• Motivation and Overview of the Bio-Networking
  Architecture
• Example application of the Bio-Networking
  Architecture and its features
• adaptation and evolution
• security and survivability
• scalability and simplicity
• Bio-Networking Architecture implementation

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Example Super-Presence

• Super-Presence represents a person, organization,
  or concept in the network
• Super-Presence holds, disseminates, protects
• email address, web pages, files, preferences, etc
• Super-Presence is made up of cyber-entities which
  can
• migrate
• reproduce with mutations
• protect their bodies
• communicate and interact with other cyber-entities

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Super-Presence
Distributor B
Factory
Corporate and RD Headquarters
Distributor A
Factory
13
Super-Presence
Distributor B
Factory
Corporate and RD Headquarters
Distributor A
Factory
14
Super-Presence
Distributor B
Factory
Corporate and RD Headquarters
Distributor A
Factory
15
Super-Presence
Distributor B
Factory
Corporate and RD Headquarters
Distributor A
Factory
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Super-Presence Adaptation

• Super-Presence adapts its configuration in
  response to amount of user demand, location of
  user demand, and network resource constraints
• This is the emergent result of the
  cyber-entities migration and reproduction
  behaviors
• Administrators do not need to manually replicate
  or tune the Super-Presence

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Super-Presence Evolution

• Algorithmic diversity can be automatically
  generated and introduced by the designers
• Cyber-entities with the most optimal algorithms
  live longer and reproduce more
• Evolution may produce localized results
• Designer is freed from having to set the optimal
  parameters in the new cyber-entities

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Super-Presence Security

• Bio-Architecture based security is used in
  conjunction with traditional security techniques
  such as encryption and authentication
• Replication inherent in Bio-Networking
  Architecture gives us additional mechanisms
• Variable rate of state update
• Cyber-entities change state after random timeout
  period
• Consistency checking
• Cyber-entities periodically check each other for
  consistency

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Security idea 1 Variable Rate of State Change

• Cyber-entities change their state only after
  random timeout

Authenticated (signed) Update Request
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Security idea 2 Consistency Checking

• Cyber-entities periodically check each others
  state for consistency

Successful attack
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Super-Presence Survivability

• Replication
• Emergence
• Wide distribution in the network
• Algorithmic diversity

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Super-Presence Scalability

• In the Bio-Networking Architecture, there is no
  master entity, so control bottlenecks do not form
• Cyber-entities act autonomously, on a local
  basis, and using local information. This local
  interaction can be repeated as the cyber-entity
  population grows

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Super-Presence Reduced Complexity

• Cyber-entity behaviors are relatively simple to
  design and implement. The complex behaviors
  emerge from collective behaviors and interactions
  of the cyber-entities
• Administration of Super-Presence is simplified
  because it adapts and evolves to heterogeneous
  and dynamic network conditions

24
Outline of Talk

• Motivation and Overview of the Bio-Networking
  Architecture
• Example application of the Bio-Networking
  Architecture and its features
• adaptation and evolution
• security and survivability
• scalability and simplicity
• Bio-Networking Architecture implementation

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Implementation Node Architecture
Resource Cyber-entity
Other Cyber-entities
Resource negotiation Energy exchange
Resource configuration control
Resource access
Bio-Networking Platform Software
Virtual Machine (e.g. Java Virtual Machine)
Unmodified, commercial software
Heterogeneous OS Hardware
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Implementation Platform Software

• Platform software provides
• Cyber-entity execution environment
• protects platform itself and cyber-entities from
  each other
• Strict resource control
• prevent denial of service through resource
  exhaustion
• promote natural selection process in
  Bio-Networking environment
• Migration and lifecycle facilities
• Communications facilities
• Energy management (prevent cheating by
  cyber-entities)

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Implementation Platform Software

• Platform software can send a list of all
  cyber-entities residing on it to its neighbors.
  This is a form of pheromone emission and
  propagation.
• This allows a cyber-entity to know what other
  cyber-entities on neighboring nodes
• This information may be used to improve the
  performance of the social networking mechanism
• Presence of sibling cyber-entities nearby also
  affects other cyber-entity behaviors (e.g.
  migration, reproduction)

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Implementation Platform Software

• Implementing functionality in platform software
  versus implementing functionality in cyber-entity
  behaviors
• Platform software can implement common
  functionality
• this reduces size of the cyber-entities
• platform software functionality can be optimized
• Platform software is more secure
• Implementing functionality as a cyber-entity
  behavior allows them to be easily changed and
  evolved

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Implementation Cyber-entity
ID
Super-entity ID
Attributes
Type
Stored Energy
Age
Non-Executable Data
Body
Cyber-entity
Executable Code
Migration
Replication
Reproduction
Behavior
Protection
Service
Communication
Pheromone Emission
...
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Implementation Behaviors

• A cyber-entity behavior can be implemented by a
  number of algorithms
• Each algorithm consists of several
• factors - small blocks of code
• parameters - variables
• Factors and parameters are automatically varied
  during replication and reproduction
• Human designers can introduce new algorithms,
  factors, or variables into the population at any
  time

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Implementation Migration Behavior
One possible migration algorithm
calculate if more than 80 of requests coming
from a single direction

W2
calculate cost of migration
W1
gt M
If
then migrate
Factors which affect migration
W1, W2, and M are parameters
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Implementation Reproduction

• Factors that affect reproduction behavior
• stored energy level
• current resource cost in the network
• availability of desirable mate
• reproductive aggressiveness

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Implementation Crossover Mutation
cyber-entity A
child cyber-entity
Ownermwang
Behaviors abcde
Info xyz
Ownermwang
Behaviors abcde
Info xyz
child got behaviors a,b from parent A
behaviors c,d from parent B behavior e is a
mutation
Ownermwang
Behaviors abcde
cyber-entity B
Info xyz
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Implementation Pheromone Emission

• Pheromones are emitted by biological entities for
  communication (e.g. marking a trail to food,
  readiness to mate, signify danger). Pheromones
  can be propagated, but decay with distance and
  time. Cyber-entities can also use this mechanism
  for establishing relationships in social
  networking, reproduction, etc.
• Factors of the pheromone emission behavior
• information contained in pheromone
• frequency and strength of pheromone emission

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Implementation Increasing Diversity

• Human designers can introduce behavioral
  diversity into the cyber-entity population
• This will increase rate of adaptation and
  evolution
• This will also make the population more immune to
  specific attacks or failures

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Implementation Deployment

• Bio-Networking Architecture can be deployed
  incrementally in todays IP networks
• Administrators load Bio-Networking Platform
  Software on various computers
• Bio-Networking nodes form a virtual network on
  top of IP network, similar to mbone
• Users can access Bio-Networking services and
  applications by download a Bio-Networking enabled
  applet into their conventional browsers

37
Research Questions

• What are the beneficial concepts and mechanisms
  from the biological world?
• What is the relationship between individual
  behaviors and emergent behaviors?
• What is the stability/adaptability tradeoff?
• Can Bio-Networking Architecture evolve fast
  enough?
• How secure and survivable is the Bio-Networking
  Architecture?
• What is the performance and overheads of Social
  Networking?

38
Related Work

• AI and robotics
• use a collection of simple intelligent components
  rather than building a monolithic complex
  intelligence
• Bio-Networking uses the same approach in the
  construction of network services and applications
  
• Artificial Life and Santa Fe Swarm project
• simulate biological processes and emergent
  behavior
• Bio-Networking applies lessons learned to network
  apps
• Network applications modeled after the immune
  system
• Bio-Networking provides a framework for applying
  the ideas developed in this area, e.g. mutation,
  diversity during reproduction

39
Related Work

• Intrusion detection systems
• detects intrusion based on signature or anomaly
• Bio-Networking detects intrusion based on
  inconsistency in state. Both approaches can be
  used as multiple layers of defense.
• Protection of Critical Information Infrastructure
• Presidential commission study saw need to protect
  information infrastructure
• Bio-Networking is trying to satisfy this new
  challenge
• Mobile agent systems
• Bio-Networking also consists of mobile agents,
  but they are governed by biological principles

40
Related Work

• Active Networks
• allows users to add new protocol behaviors in the
  network
• Bio-Networking allows users to add new
  application layer behaviors that can adapt and
  evolve based on usage
• Web caching
• web objects replicated to reduce server load
• Bio-Networking adds economic model for
  determining which objects are most valuable to
  cache, deals with updates better, and has
  inherent security features
• Jini (Sun Microsystems)
• ubiquitous model of computing based on Java
• Bio-Networking is less centralized and can adapt
  more

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• Aphid A web caching (replication) service based
  on the Bio-Networking Architecture

Bio-Networking Platform w/ limited resources
BlueHat/prodA
BlueHat/prodB
Bio-Networking Platform with abundant (CPU,
memory, disk) resources (high-end server)
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
42

• Aphid A web caching (replication) service based
  on the Bio-Networking Architecture

BlueHat/prodB
Bio-Networking Platform w/ limited resources
BlueHat/prodA
BlueHat/prodB
Bio-Networking Platform with abundant (CPU,
memory, disk) resources (high-end server)
BlueHat/prodB
BlueHat/prodB
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
43

• Aphid A web caching (replication) service based
  on the Bio-Networking Architecture

BlueHat/prodB
Bio-Networking Platform w/ limited resources
BlueHat/prodA
BlueHat/prodB
BlueHat/prodC
Bio-Networking Platform with abundant (CPU,
memory, disk) resources (high-end server)
BlueHat/prodB
BlueHat/prodB
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
44

• Aphid A web caching (replication) service based
  on the Bio-Networking Architecture

BlueHat/prodB
Bio-Networking Platform w/ limited resources
BlueHat/prodA
BlueHat/prodB
BlueHat/prodC
Bio-Networking Platform with abundant (CPU,
memory, disk) resources (high-end server)
BlueHat/prodB
BlueHat/prodC
BlueHat/prodC
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
Bio-Networking Platform w/ limited resources
45

• Aphid A web caching (replication) service based
  on the Bio-Networking Architecture

46
Web Caching Squid

• client browsers are manually configured with
  address of proxy
• proxy caches web pages
• proxies can be manually configured to look for
  web pages in nearby proxies
• Difficult issues remain stale cache pages,
  dynamic content, tracking hits
• cache replacement algorithm(s) do not consider
  the value to a user (work related versus
  recreational info are cached equally)
• no inherent security or survivability features

47
Web Caching AWC

• easy migration path from Squid to Adaptive Web
  Caching (AWC), only proxies have to be modified
• proxies self-organize in a group and share their
  cache space to create a larger cache
• clients still manually configured to point at a
  proxy
• difficult issues of stale pages, dynamic content,
  tracking page hits are not resolved
• cache replacement algorithm does not consider
  value to the user
• no inherent security or survivability features

48
Web Caching Aphid (Bio-Net based)

• cyber-entities each hold a copy of a web page
• cyber-entities autonomously replicate, migrate,
  and die based on user demand
• clients find closest or least loaded cyber-entity
  using the social networking capability of the
  Bio-Networking Architecture
• update of the web page is propagated to all
  cyber-entities (solves the stale web page
  problem)
• because each cyber-entities is a thread of
  execution, it can generate dynamic content,
  e.g. mortgage calculator
• cyber-entities can track number of hits

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Web Caching Aphid (Bio-Net based)

• cyber-entities receive energy units depending on
  the value of their web page, also expends energy
  depending on their size (cache replacement
  algorithm based on economic principles)
• cyber-entities have inherent security features,
  e.g. consistency checking
• cyber-entities have survivability features, e.g.
  there is no master copy of the web page

50
Web Caching Aphid (Bio-Net based)

• Limitations
• clients need to download plug-ins which implement
  social networking
• users/clients may need to indicate value of a
  web page request
• overhead of communications among cyber-entities
• cyber-entities do not have an algorithm to ensure
  globally synchronized, causal update of their
  content (this may not be a big problem for web
  pages)

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Summary and Conclusion

• Bio-Networking Architecture represents a new
  paradigm in the construction of network services
  and applications
• Services and applications consists of multiple,
  autonomous cyber-entities that exhibit emergent
  behavior
• Bio-Networking Architecture is scalable,
  adaptable, evolvable, secure, survivable, and
  simple

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More Information

• Bio-Networking Architecture web site
• http//netresearch.ics.uci.edu/bionet
• mwang_at_ics.uci.edu
• larryc_at_ics.uci.edu
• suda_at_ics.uci.edu

53

• Aphid A web caching (replication) service based
  on the Bio-Networking Architecture

Bio-Networking Platform
Bio-Networking Platform
A
B
C
Bio-Networking Platform
Bio-Networking Platform
Bio-Networking Platform
Bio-Networking Platform
Bio-Networking Platform
Bio-Networking Platform