AutoMate : Enabling Autonomic Computational Applications on the Grid M' Agarwal, V' Bhat, Z' Li, H'

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AutoMate Enabling Autonomic Computational
Applications on the GridM. Agarwal, V. Bhat, Z.
Li, H. Liu, V. Matossian, V. Putty, C. Schmidt,
G. Zhang, M. Parashar, B. Khargharia and S.
HaririThe State University of New Jersey,
University of Arizona 5th Annual International
Active Middleware Services Workshop (AMS2003),
Jan. 2003
Date Oct. 24 2007 Chang-Keun
Park pck1982_at_postech.ac.kr DPNM Lab., Dept. of
CSE, POSTECH
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Content

• Background
• Grid Computing and AutoMate
• Introduction
• Motivation
• Goal
• The architecture of AutoMate
• The key components of AutoMate
• ACCORD
• RUDDER
• SESAME
• Pawn
• SQUID
• Conclusion

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Grid Computing

• Computing model that provides the ability to
  perform higher throughput computing
• Distributed computing such as virtual
  supercomputer" by using a network of computers
  which is like a grid
• Functionally, there are several types of grids
• Computational grids
• Data grids
• Open Grid Service Architecture (OSGA)
• an architecture for a service-oriented grid
  computing environment for business and scientific
  use
• developed within the Global Grid Forum (GGF).
• based on several other Web service technologies
  such as WSDL and SOAP

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AutoMate

• Enabling Autonomic Applications on the Grid
• The State University of New Jersey
• TASSL (The Applied Software Systems Laboratory)
• Director Manish Parashar
• http//automate.rutgers.edu
• Research
• Autonomic Computing
• Parallel and Distributed Computing
• Grid Computing
• Peer-to-Peer Computing
• Distributed Computational Collaboratories
• Adaptive Computational Engines and Runtime
  Systems
• Adaptive QoS Management
• Scientific Computing
• Collaboration Groupware

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Introduction

• The emergence of computational Grids has made it
  possible to conceive a new generation of
  realistic, scientific and engineering simulations
  of complex physical phenomena
• Existing Problem
• the phenomenon being modeled by Grid applications
  is multi-phased, dynamic and heterogeneous
  requiring large numbers of software components
  and dynamic compositions and interactions between
  these components
• the underlying Grid infrastructure is also
  heterogeneous and dynamic, globally aggregating
  large numbers of independent computing and
  communication resources, data stores and sensor
  networks
• These application development, configuration and
  management complexities break current paradigms
  based on passive components and static
  compositions

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Motivation

• A need for a fundamental change in how grid
  applications are formulated, composed and managed
  has been increasing
• In fact, our programming environments and
  infrastructure are becoming unmanageable and
  insecure because of complexity, heterogeneity,
  and dynamism
• This has led researchers to consider alternative
  programming paradigms and management techniques
  of biological systems to deal with complexity,
  heterogeneity and uncertainty
• The approach is autonomic computing

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Goal

• Investigate key technologies to enable the
  development of autonomic Grid applications that
  are context aware and are capable of
  self-configuring, self-composing, self-optimizing
  and self-adapting
• Specific Issues
• Definition of Autonomic Components
• definition of programming abstractions and
  supporting infrastructure that will enable the
  definition of autonomic components
• Dynamic Composition of Autonomic Applications
• mechanisms and supporting infrastructure to
  enable autonomic applications to be dynamically
  composed from autonomic components
• compositions will be based on context information
  such as policies and constraints and their
  current states
• Autonomic Middleware Services
• design, development, and deployment of key
  services on top of the Grid middleware
  infrastructure to support autonomic applications
• a key requirements for autonomic behavior and
  dynamic compositions is the ability of the
  components, applications and resources to
  interact as peers

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AutoMate Architecture
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The key components of AutoMate

• ACCORD
• Autonomic component framework
• programming framework for enabling the
  development and management of autonomic
  applications
• RUDDER
• Decentralized deductive engine
• coordination middleware with intelligent
  deductive capabilities for Autonomic Applications
• SESAME
• Dynamic role-based access control engine
• provides dynamic context-aware control
• Pawn
• Peer-to-Peer messaging infrastructure
• provides high-level messaging abstractions in a
  decentralized environment
• SQUID
• Decentralized discovery service
• provide efficient information discovery

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ACCORD - Autonomic components

• Autonomic components export information and
  policies about their behavior, resource
  requirement and so on

• AutoMate components provide theirs profiles or
  contracts that encapsulate functional,
  operational, and control aspects
• functional aspects abstracts component
  functionality
• operational aspects abstracts a component's
  operational behavior
• control aspects describes the adaptability of the
  component and defines sensors/actuators and
  policies for management

• Access agent
• is a part of the AutoMate access control engine
• manages access to the component based on its
  current context and state
• Rule agent
• is part of RUDDER, the AutoMate deductive engine
• manages local rule definition, evaluation and
  execution

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ACCORD - Autonomic Compositions
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RUDDER - The Deductive Engine

• a decentralized deductive engine composed of
  distributed specialized agents
• component rule agents, composition agents,
  context agents and system agents
• Objective
• provide mechanisms for dynamically defining,
  configuring, deploying rules, and rule conflicts
  management
• provide the core capabilities for supporting
  autonomic compositions, adaptations, and
  optimizations

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RUDDER - The Deductive Engine

• Rules can be dynamically injected into the system
  and are routed by the messaging substrate to the
  appropriate agents.
• the agents may hierarchically decompose a rule
  and distribute it to peer agents

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RUDDER - Rules

• use an XML rule schema and consists of the
  following tags
• ltRULE identifiergt, ltprioritygt, ltON eventsgt, ltUSE
  component/servicegt, ltIF conditionsgt, ltTHEN
  actionsgt, and ltELSE default actionsgt
• Sample RUDDER Rule

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SESAME

• Dynamic role-based access control engine
• provide dynamic access control to users,
  applications, services, components and resources
• extend Role Based Access Control (RBAS) to make
  access control decision based on dynamic context
  information
• dynamically adjust Role Assignments and
  Permission Assignments based on context
• Objective
• support dynamic, seamless and secure interactions
  between the participating entities (i.e.
  components, services, application, data,
  instruments, resources and users)

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SESAME - Dynamic Access Control Model

• Subject
• the entity which requests service from another
  entity
• user, application, service or component
• context agent
• collect an entitys current context information
  such as the state and current execution
  environment of a component or an application
• Based on this context information, the access
  control agent dynamically adjusts the user-role
  and role- permission relationships

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SESAME - Operation

• Access control agent
• maintains the role state machine for each
  component and defines its active role based on
  its current context

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PAWN

• a peer-to-peer messaging substrate that builds on
  project JXTA
• provides a stateful and guaranteed messaging to
  enable key application-level interactions
• synchronous/asynchronous communication, dynamic
  data injection, and remote procedure calls

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SQUID

• Decentralized discovery service
• supports decentralized information discovery in
  AutoMate
• Motivation
• Efficient information discovery in the absence of
  global knowledge of naming conventions
• The overall architecture of SQUID is a
  distributed hash table (DHT), similar to typical
  data lookup systems
• Key innovation
• locality preserving, dimension reducing indexing
  scheme that effectively maps the multidimensional
  information space to physical peers

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Conclusion

• Autonomic applications is necessary to address
  scale, complexity, heterogeneity, dynamism and
  reliability challenges
• AutoMate addresses fundamental issues and
  provides key solutions in the autonomic
  formulation, composition, and runtime management
  of applications on the Grid
• ACCORD Autonomic application framework
• RUDDER Decentralized deductive engine
• SESAME Dynamic access control engine
• Pawn P2P messaging substrate
• SQUID P2P discovery service