Bionet Project Overview: Applying Biological Concepts and Mechanisms for Designing Adaptable, Scalable and Survivable Communication Software

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Bionet Project OverviewApplying Biological
Concepts and Mechanisms for Designing Adaptable,
Scalable and Survivable Communication Software

• Jun Suzuki and Tatsuya Suda
• jsuzuki_at_ics.uci.edu Dept. of Information and
  Computer Science,University of California, Irvine

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Agenda

• Bio-Networking Architecture Overview
• Motivation to Bionet Project
• Observations of large scale biological systems
  that scale, adapt, and survive in dynamic
  environment
• How bio concepts are used in the Bionet project
• Bio-Networking Architecture Design
• Current Project Status and Future Work
• Conclusion

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Motivation Bionet Project

• The explosive growth of the net places larger and
  more challenging demands on underlying
  communication software.
• Future network services and applications have to
  satisfy
• Scalability
• They have to be able to scale to billions of
  nodes and users.
• Adaptability
• They have to be able to adapt to diverse and
  dynamic conditions in the network.

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• Availability and Survivability
• They have to be secure and highly available.
• Autonomy
• They have to require minimal human configuration
  and management.
• Networks need to have built-in mechanisms to
  provide these features

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Bionet Applying Biological Concepts and
Mechanisms

• Observation large scale biological systems
  scale, adapt, and survive
• e.g. bee colony
• Bionet applying biological concepts/mechanisms
  to network services and applications
• A bee colony can scale to a large number of bees
  because all activities of the colony are carried
  out without centralized control.
• decentralization

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• Bees act autonomously, influenced by local
  conditions and local interactions with other
  bees.
• The bee colony also adapts to dynamic conditions,
  often to optimize its food gain relative to
  energy expenditure.
• The bee colony is survivable because it is not
  dependent on any single bee
• Scalability, adaptability and survivability are
  not present in any single bee. Rather, they
  emerge from the collective actions and
  interactions of all the bees in the colony.

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Complex Adaptive System
a super entity with emergent characteristics
emergencethrough self-organization
Feedback andreinforcementlearning
individual entities (agents)
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Agenda

• Bio-Networking Architecture Overview
• Motivation to Bionet Project
• Observations of large scale biological systems
  that scale, adapt, and survive in dynamic
  environment
• How bio concepts are used in the Bionet project
• Bio-Networking Architecture Design
• Current Project Status and Future Work
• Conclusion

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Emergent Behavior

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

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• Bio-Network
• application constructed from a collection of
  cyber-entities (objects/agents)
• cyber-entities have biological behaviors
• e.g. migration, reproduction, death, energy
  exchange
• each cyber-entity has basic functionality related
  to its application and service

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Food and Energy

• Biological systems
• biological entities naturally strive to gain
  energy by seeking and consuming food

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• Bio-Network
• cyber-entity stores/expends energy (food/money)
• Energy is the unit of exchange for service or
  resource usages.
• energy exchange
• CE gains energy from a user/another CE in
  exchange for performing a service
• CE expend energy to use network/computing
  resources
• energy used as a control mechanism (natural
  selection mechanism)
• abundance induces replication or reproduction
• scarcity induces death

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Evolution and Adaptation

• Biological systems
• the biological system specializes and optimizes
  itself for environmental changes.
• key enablers
• diversity from mutations and crossovers during
  replication/reproduction
• natural selection keeps entities with beneficial
  features alive and increase reproduction
  probability

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• Bio-Network
• 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 CEs
• CEs replicate/reproduce with mutation/crossover
• natural selection
• death from energy starvation
• replication/reproduction from energy abundance

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Agenda

• Bio-Networking Architecture Overview
• Motivation to Bionet Project
• Observations of large scale biological systems
  that scale, adapt, and survive in dynamic
  environment
• How bio concepts are used in the Bionet project
• Bio-Networking Architecture Design
• Current Project Status and Future Work
• Conclusion

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Bionet Platform Architecture
CE
CE
CE
CE
CE Context
Bionet Services
Bionet Container
Bionet Platform
Java VM
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Bionet Platform Components

• Bionet Platform
• is developed by Java and runs on a Java virtual
  machine.
• Major components
• Cyber entity
• runs and moves autonomously
• Provides a simple service
• is the smallest component in Bionet environment.
• Cyber entity context
• Bionet Services
• Bionet Container

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Ineter-CE Communication

• A CE has a single method service, which accepts
  a message written in an Agent Communication
  Language (ACL).
• Each CE has an ACL interpretation engine.

CE
CE
service(ltACL msggt)
ltrequestgt ltnamegtreservelt/namegt ltarggt01lt/arggt
lt/requestgt
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The class CyberEntity
extends
CyberEntity
HotelReservationAgent
private float energy private int age private
String name
private float rate private Vector reservations
Bodys non-executable data
attribute
reserve()checkAvailability() cancelRerservation()
Bodys executable code
migrate() replicate() reproduce() init(Object)
void run() void getCEContext()
AgentContextsetCEContext(CEContext) voidlog()
void onCreated() void onMove()
void onArrival() void onActivated()
void onDeactivated() void onDestroy() void
Behavior
Auxiliary methods
Callback operations
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Cyber Entity Context

• CE Context
• is an entry point for CE to access Bionet
  Services.
• is created and associated with each CE implicitly
  by Bionet Lifecycle Service, when a CE is
  created, replicated, reproduced, or migrated from
  another host.
• is called by only its associated cyber entity.

CyberEntity
CEContext
1
1
getCEContext() CEContextsetCEContext(CEContext)
void
find(String) ObjectgetBionetContainer(String)
BionetContainergetReference() CERreference
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Bionet Services
Bionet Services
RelationshipManagement
Energy Management
Cyber EntityMigration
Pheromone Emission
Social Networking
ResourceAllocation
CE Directory
ResourceSensing
Security
Cyber-Entity Comm.
Cyber-Entity Lifecycle
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Bionet Container

• Bionet Container
• is a sandbox in which CEs and Bionet Services
  run.
• runs on per-process basis.
• One or more Bionet containers can run on a single
  host.

a host
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• Bionet Platform provides the bottom most
  operations to maintain Bionet Platform.
• CE registration/unregistration
• CE activation/deactivation
• resource management
• CE reference management
• request/event parsing

available resources
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Agenda

• Bio-Networking Architecture Overview
• Motivation to Bionet Project
• Observations of large scale biological systems
  that scale, adapt, and survive in dynamic
  environment
• How bio concepts are used in the Bionet project
• Bio-Networking Architecture Design
• Current Project Status and Future Work
• Conclusion

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Current Project Status

• Bionet Simulator
• Adaptation/evolution simulation done.
• See a technical paper for details
  netresearch.ics.uci.edu/bionet/
• Results show Bio-Networking Architecture works
  well.
• Bionet Platform is design/early implementation
  stages.
• on top of CORBA and Enterprise Java Beans (EJB)

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An Ongoing projectSelf-Organizing Agents

• It has been one of the biggest problem in
  traditional distributed object computing
  environments to decide and optimize object
  locations.
• Static and manual configuration in the
  environment without object mobility
• e.g. Many objects on a powerful machine
• Frequently interacting objects on the same
  host/process
• Tedious and time-consuming
• The system/object should be stopped availability
  decreased.
• The decision is ad-hoc or static even in mobile
  agent environments.
• e.g. Developers define an agent itinerary that
  describes when and where to move at development
  time.

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• We need more dynamic and autonomous mechanism of
  deciding agent location.
• Bionet facilitates this by using self-organizing
  cyber entities
• A CE organization is emerged from autonomous
  local interactions.
• Energy exchange between CEs
• Energy exchange between an CE and its platform

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Agent-agent/agent-platform interactions

• Energy in Bionet
• Unit of exchange for service or resource usages
• Agent-agent interaction
• Each agent gains energy from another agent (or
  user) in exchange for performing a service.
• abundance induces replication or reproduction
• scarcity induces death
• Agent-platform interaction
• Each agent expends energy to use
  network/computing resources.
• e.g. thread, transport connection, memory space
  and CPU cycle
• Agent platform knows the types, amount and cost
  of available resources.

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An Example Scenario

• Each agent asks its underlying platform to assign
  a thread, and pays its energy.
• The unit cost of a thread utilization may vary
  with the number of available threads.
• More idle threads exist in a pool, cheaper the
  unit cost is.
• Each agent behaves autonomously with its
  policies.
• e.g. An agent migrates to another host when
  migration cost is cheaper than thread utilization
  cost.
• e.g. An agent deactivates itself when its thread
  utilization cost is too expensive to pay.
• The concept of energy allows agents to consume
  available resources in a distributed environment
  effectively.

CE
(2) assigns an idle thread(3) requires the cost
(1) registers
agent registration table
Thread pool
Bionet Container
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Wrap up

• Bionet provides a new paradigm to build network
  services and applications.
• Inspired by biology
• Bionet Platform is in design/early implementation
  stages
• Most of the elements are in place
• Several simulation results have supported our
  direction.
• Bionet Simulator is available at
• http//netresearch.ics.uci.edu/bionet/
• Bionet Platform will be available in the middle
  of this year.

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Acknowledgements

• This work has been supported by
• the National Science Foundation through grants
  ANI-0083074 and ANI-9903427,
• DARPA through Grant MDA972-99-1-0007,
• AFOSR through Grant MURI F49620-00-1-0330,
• grants from the University of California MICRO
  Program,
• Hitachi,
• Hitachi America,
• Standard Microsystems Corporation,
• Canon,
• Canon USA,

• Novell,
• Tokyo Electric Power Company,
• Nippon Telegraph and Telephone Corporation (NTT),
• NTT Docomo,
• Fujitsu,
• Nippon Steel Information and Communication
  Systems Incorporated (ENICOM),
• Matsushita Electric Industrial company.