Evaluating a Bio-Inspired Approach for the Design of a Grid Information System: The SO-Grid Portal

Evaluating a Bio-Inspired Approach for the Design of a Grid Information System: The SO-Grid Portal

Agostino Forestiero (Institute of High Performance Computing and Networking CNR-ICAR, Italy), Carlo Mastroianni (Institute of High Performance Computing and Networking CNR-ICAR, Italy), Fausto Pupo (Institute of High Performance Computing and Networking CNR-ICAR, Italy) and Giandomenico Spezzano (Institute of High Performance Computing and Networking CNR-ICAR, Italy)
DOI: 10.4018/978-1-59904-996-0.ch016
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Abstract

This chapter proposes a bio-inspired approach for the construction of a self-organizing Grid information system. A dissemination protocol exploits the activity of ant-inspired mobile agents to replicate and reorganize metadata information on the basis of the characteristics of the related Grid resources. Resource reorganization emerges from simple operations of a large number of agents, in a “swarm intelligence” fashion. Moreover, a discovery protocol allows Grid clients to locate useful resources on the Grid through a semi-informed approach. This chapter also describes the SO-Grid Portal, a simulation portal through which registered users can simulate and analyze the ant-based protocols. This portal can be used by researchers to perform “parameter sweep” studies, as it allows for the graphical comparison of results obtained in previous sessions. We believe that the deployment of the SO-Grid portal, along with the definition and discussion of the protocols presented in this chapter, can foster the understandi ng and use of swarm intelligence, multi-agent and bio-inspired paradigms in the field of distributed computing.
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Introduction

To support the design and execution of complex applications, modern distributed systems must provide enhanced services such as the retrieval and access to content, the creation and management of content, and the placement of content at appropriate locations. In a Grid, these services are offered by a pillar component of Grid frameworks, the information system. This chapter discusses a novel approach for the construction of a Grid information system which allows for an efficient management and discovery of information. The approach, proposed in (Forestiero et al., 2005) in its basic version, exploits the features of (1) epidemic mechanisms tailored to the dissemination of information in distributed systems (Peterson et al., 1997, Eugster & al., 2004) and (2) self organizing systems in which “swarm intelligence” emerges from the behavior of a large number of agents which interact with the environment (Bonabeau & al., 1999, Dasgupta, 2004).

The proposed ARMAP protocol (Ant-based Replication and MApping Protocol) disseminates Grid resource descriptors (i.e., metadata documents) in a controlled way, by spatially mapping these descriptors according to their semantic classification, so to achieve a logical reorganization of resources. A resource descriptor can be composed of a syntactical description of a Grid service (e.g. a Web Services Description Language - WSDL - document) and/or a semantic description of the capabilities of the service.

Descriptor reorganization results from pick and drop operations performed by a large number of agents. Each ARMAP agent travels the Grid through P2P interconnections among Grid hosts, and uses simple probability functions to decide whether or not to pick descriptors from or drop descriptors into the current Grid host. This approach is inspired by the activity of some species of ants and termites that cluster and map items within their environment (Bonabeau & al., 1999).

Furthermore, a self-organization approach based on ants’ pheromone (Van Dyke & al., 2005) enables each agent to regulate its activity, i.e. its operation mode, only on the basis of local information. Indeed, each agent initially works in the copy mode: it can generate new descriptors and disseminate them on the Grid. However, when it realizes from its own past activity that a sufficient number of replicas have been generated, it switches to the move mode: it only moves descriptors from one host to another without generating new replicas. This switch is performed when the level of a pheromone variable, which depends on agent’s activity, exceeds a given threshold.

The ARMAP protocol can effectively be used to build a Grid information system in which (1) resource descriptors are properly replicated and (2) the overall entropy is reduced. A balance between these two features is achieved by regulating the pheromone threshold, i.e., by shortening or extending the time interval in which agents operate under the copy mode.

A semi-informed discovery protocol exploits the logical resource organization achieved by ARMAP. Indeed, whenever a large number of descriptors of a specific class are accumulated in a restricted region of the Grid, it becomes convenient to drive query messages (issued by users to locate descriptors of this class) towards this region, in order to maximize the number of discovered descriptors and minimize the response time. While this chapter focuses on enhancements and performance of the ARMAP protocol, the discovery protocol, namely ARDIP (Ant-Based Resource Discovery Protocol) is here shortly discussed, whereas an extensive analysis can be found in (Forestiero & al., 2007).

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Table of Contents
Foreword
Alfonso Araque Almendros
Acknowledgment
Ana B. Porto Pazos, Alejandro Pazos Sierra, Washington Buño Buceta
Chapter 1
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Chapter 2
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Neural Mechanisms of Leg Motor Control in Crayfish: Insights for Neurobiologically-Inspired Autonomous Systems
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Chapter 3
Oscar Herreras, Julia Makarova, José Manuel Ibarz
Neurons send trains of action potentials to communicate each other. Different messages are issued according to varying inputs, but they can also mix... Sample PDF
Forward Dendritic Spikes: A Mechanism for Parallel Processing in Dendritic Subunits and Shifting Output Codes
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Chapter 4
Gheorghe Paun, Mario J. Perez-Jimenez
This chapter is a quick survey of spiking neural P systems, a branch of membrane computing which was recently introduced with motivation from neural... Sample PDF
Spiking Neural P Systems: An Overview
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Chapter 5
Juan Ramón Rabuñal Dopico, Javier Pereira Loureiro, Mónica Miguélez Rico
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Chapter 6
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Chapter 7
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A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in Evolutionary Robotics
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Chapter 9
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Chapter 10
Computing vs. Genetics  (pages 165-181)
José M. Barreiro, Juan Pazos
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Chapter 11
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Chapter 12
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Chapter 13
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Chapter 14
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Chapter 15
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Chapter 16
Agostino Forestiero, Carlo Mastroianni, Fausto Pupo, Giandomenico Spezzano
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Evaluating a Bio-Inspired Approach for the Design of a Grid Information System: The SO-Grid Portal
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Chapter 17
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Chapter 19
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