EWHA Univ. SE Lab. DW Lecture 1

Hive: Distributed Agentsfor Networking Things

IEEE 2000

Nelson Minar, Matthew Gray, Oliver Roup, Raffi Krikorian and Pattie Maes

MIT Media Lab

2005. 05. 24

SE Lab.Heejin Kim

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Contents

Introduction

Conceptual Agents system The Hive architecture Hive agent implementation Hive application and experiments

Lessons from Hive Conclusion

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Introduction

What is Hive? A decentralized system for building applications by networking local system resources

Ecologies of distributed agents In an ecology of agents, an application is created

out of the interaction of multiple agents across a network

Each agent resides in a particular place and uses local resources

Agents communicate with each other to share information and access to resources

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Conceptual Agents system

Five useful ideas of conceptual agents:

1. Agents are autonomous Entrusted to carry out goals when sent into a system

2. Agents are proactive Active elements, Able to act on their own

3. Agents are self-describing An ontology of agent capabilities can describe

agents, allowing dynamic discovery of available services

4. Agents can interact Work together to complete a task

5. Agents can be mobile Provide a simple abstraction for complex,

dynamically distributed systems

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The Hive architecture

Hive is a set of Java libraries… The system Consists of roughly 280 classes

in 24,000 lines of code 50% lines : Generic infrastructure Other 50% : Specific code for approximately 30 devices and 60 agents

Uses Java RMI (Remote-method invocation) and serialization for agent communication & mobility

Uses XML for ontology support

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The Hive architectureHive’s three components

Hive has three components Cells, Shadows, Agents

Hive network is a decentralized collection of cells Cell is the analog of a Web

server Cell contains a set of shadows Each cell hosts many agents

By analogy to a conventional OS, A cell : kernel Shadows : device drivers Agents : processes

Hive’s architectureHive’s architecture

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The Hive architectureCells : Nodes in the decentralized network

Hive cells perform two primary tasks : Hosting software agents Managing access to local resources

The ideal model would place a Hive cell on every device => Impossible!! A typical cell has several devices

Input resources : motion sensor, camera, digital tag leader.. Output resources : computer display, small robot, speaker..

Hive has a location-dependent model of a distributed system Hive cells are not all the same!

Each cell is an equal peer in the network

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The Hive architectureShadows :Local resources

Shadows are nothing but an API to access a specific resource The shadow abstraction is useful for structuring a

system A system designer writes a shadow once,

encapsulating all of a device’s behavior

Shadows provide the static layer that the agents access

Shadows offer the place for Hive cell to enforce a security or resource control policy

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The Hive architectureAgents : Active computation

Hive agents embody the network interface and policy for resources

Hive agent combines… 4 pieces together create an agent,

an full-fledged autonomous process

Agents live on specific cells, accessing shadows for the resources they need and traveling on the network

s

Java object

Execution thread

Remote interface

Self-description

Hive AgentHive Agent

Shadows vs. Agentsfixed

dynamicstatic

local component networked aspect

active

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The Hive architectureUser Interface

Hive provides a graphical user interface to the distributed system Hive user interface is an agent

The interface lets users control their cells Create new agents : use drop-down menus Connect agents : by drawing lines Move or Kill agents : through pop-up menus

Interface can display multiple cells

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The Hive architectureA sample Example

Hive camera applicationHive camera application

Hive camera application Three agents

On-screen button Digital camera On-screen image display

Three agents work togetherto build this application

Easily build new applications by connecting existing agents

Flexibility and simplicity!!

System management agents

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Hive agent implementation

Hive draws techniques from many parts of agent research including… Agent autonomy Multiagent interaction Agent ontologies Agent Mobility

Agent Anatomy All Hive agents are rooted in a common agent base class Agent class is subclass of UnicastRemoteObject in RMI

RMI (Remote-Method Invocation) is the agent communication mechanism Agents are remote objects Management of agent communication must occur within RMI

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Hive agent implementation

Agent Interaction Completely ad hoc, based on simple distributed-object techniqu

es Each agent has two types:

Agent’s object, subclass of AgentImplAgentImpl Agent’s remote interface, a subtype of AgentAgent

Hive ontology Each agent is described in terms of two ontologies:

Syntactic ontology – use queryAgents() interfacequeryAgents() interface Semantic ontology - use RDF (Resource Description Frame work)

Mobility Hive mobility has two portions:

mobile code : each Hive cell to be updated dynamically mobile agent : move themselves around the Hive network

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Hive application and experiments

Things That Think, ubiquitous computing, and Hive The specific requirements of the application domain,

Things That Think (TTT)Things That Think (TTT), motivated Hive’s design Hive is a good infrastructure for TTT and TTT is a

good problem for Hive

Honey, I Shrunk the CDs, Part II Tangible Interfaces and ambient Displays Wearable Computing counter Intelligence Networking Synergy

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Lessons from Hive

1. Java, RMI and Asynchronous Messaging

Java is wonderful language for building distributed-agent systems Strong typing : syntactic ontology for free Java’s Abstract Window Toolkit (AWT) : graphical agents easy

RMI normally allows only synchronous calls, which do not work well for agent communication It means that agents are dependent on each other Having wait for each other to respond to message

Asynchronous messaging is a fundamental requirement for any distributed-agent system Added asynchronous messaging on top of RMI

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Lessons from Hive

2. Decentralization Useful strategy at many levels Ultimate advantage is scalability

However, Hive network is still small!

Organizational advantages New Hive users need no setup : simply start a cell and join the network

3. Mobility Pointed out two deep unsloved problems in mobile code

Versioning problem : Java tool’s for managing versoning are incomplete Conceptual problem : Not use a mobile agents very often

4. Ad- hoc Interaction The open nature of agent interactive might make the Hive syst

em seem Perilously unpredictable and inconsistent

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Conclusion

Hive, an implementation of an ecology of distributed agents

Agents are a good abstraction for building distributed systems

Ad-hoc agent communication along with ontologies mechanism is sufficient to build useful systems

Mobile agents are a useful abstraction, but that they are more applicable to larger distributed systems.

Agents are a fundamental building block for coherent distributed systems

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e-mail : heejinkim@ewhain.net