Bio-Networking: Biology Inspired Approach for Development of Adaptive Network Applications 21 May 2005Ognen Paunovski Bio-Networking: Biology Inspired

21 May 2005 Ognen Paunovski

Bio-Networking: Biology Inspired Approach for Development of Adaptive Network Applications

Bio-Networking: Biology Inspired Approach for Development of Global Network Applications

Presented by:

Ognen [email protected]



Presentation Goals

• What is an Agent?

• What is Multiagent System?

• What are Mobile Agents?

• What is Biology Inspired Computing?

• What is Bio-Networking?

• What Bio-Networking architecture looks like?

• What is Cyber Entity?

• Which biological principles Bio-Networking follows?

• Is Bio-Inspired approach the next evolutionary step in development of global network applications !?

Introduction

Main focus



What is an Agent?

• Autonomy: agents operate without the direct intervention of humans or others, and have some kind of control over their actions and internal state.

• Reactivity: agents perceive their environment, and respond in a timely fashion to changes that occur in it.

• Pro-activeness: agents do not simply act in response to their environment, they are able to exhibit goal-directed behavior by taking the initiative.

• Social Ability: agents interact with other agents (and possibly humans) via some kind of agent-communication language

Agent is software application that has following characteristics:

(Wooldridge & Jennings 1995)



Multi Agent Systems and Mobile Agents

"There's no such thing as a single agent system"Slogan of the multiagent community

A Multi-Agent System (MAS) is a system designed and implemented as several interacting agents that cooperate, coordinate and/or negotiate.

Characteristics of MAS:

• Each agent has incomplete capabilities.

• There is no global system control (decentralized data and control).

MAS Environments:

• They are typically without centralized designer (possibly open).

• Agents in the environment may be self-interested or cooperative.

• Agents must be able to find each other and must be able to interact.

(Jennings et al., 1998)



Multi Agent Systems and Mobile Agents (con’t)

“Agents capable of transmitting themselves, their program and their state across computer network and recommencing execution

at a remote site are known as Mobile Agents”(Wooldridge, 2001)

AgentOrganizationalrelationship I nteraction Environm ent

M igration process

Sphere of influence



Biology-Inspired Computing

The concept of introducing ideas from biological systems and organisms into computer science.



What is Bio-Networking ?

• Example of Biology Inspired Approach for development of decentralized adaptive network applications.

• It is both paradigm as well as middleware.

• The project was developed by Department of Information and Computer Science, University of California, Irvine.

• Sponsored by : [http://netresearch.ics.uci.edu/bionet/]

– National Science Foundation, DARPA, Air Force Office of Scientific Research, Hitachi America, Fujitsu, etc.

• Motivation:– Challenges faced by future network applications have already been

overcome in large scale biological systems.

(Wang & Suda, 2000)



Bio-Networking Architecture

• Cyber Entity (CE) – mobile agent designed to follow biological principles and “live” in the Bionet environment.

– Attributes that describe the CE: ID, energy level, parent, etc.

– Behaviors: Decision making, reproduce, migrate, relationship, spend energy, etc.

• Bio-net platform – environment where CE exist, in network device with JVM and Bio-Networking platform software.

– Resource control, CE scheduling, System Services, Information Services.

(netresearch.ics.uci.edu/bionet/, Suzuki)



Biological principles in Bio-Networking

• Emergence– Biology: characteristics of the large scale biological system emerge

from a group of interacting biological entities.

– Bio-Networking: characteristics of the Bio-Networking applications emerge from multiple interacting CEs.

• Autonomous actions based on local information and local interactions– Biology: biological entities in large scale biological systems act

autonomously.

– Bio-Networking: CE are autonomous agents following goal driven behavior.

• Birth and Death as Expected events– Biology: biological entities are born and die.

– Bio-Networking: CE can crash or die, CE can produce another CE



Biological principles in Bio-Networking (con’t)

• Energy and Adaptation

– Biology: biological entities adopt to the environment in order to maximize their energy gain while minimize their energy expenditure

– Bio-Networking: Introduces the concept of energy level for each CE. CE acquire and spend energy depending on their actions and interactions. CE without energy dies.

• Natural Selection and Evolution

– Biology: evolution occurs as a result of genetic diversity and natural selection

– Bio-Networking: CE combine behavior and parameters when reproducing. Natural selection is based on the energy maximization policy.



Future of Global Network Applications

• must be able to scale to billions of nodes and users• must be able to adapt to diverse and dynamic network conditions. • must be secure and highly available• should require minimal human configuration and management

Requirements for future network applications:

• Scalable – CE can multiply sufficiently to accommodate high service demand or die to reduce the total population number.

• Adaptive – CE adopts to the environment to maximize energy gain (migration, birth and death, natural selection, evolution).

• Available – CE can migrate to location best suited to satisfy service demand

• Survivable – The system maintains minimum population on distributed nodes. There is no central authority, loss of any part of the population can easily be replaced.

Bio-Networking properties:



THANK YOU !

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Bio-Networking: Biology Inspired Approach for Development of Adaptive Network Applications 21 May 2005Ognen Paunovski Bio-Networking: Biology Inspired