Michèle Courant

SmartGRID: A Fully Decentralized Grid Scheduling Framework Supported by Swarm Intelligence

Resource management and scheduling has proven to be one of the key topics for grid computing. Nowadays, the resource management field is subdivided into low-level and high-level approaches. While low-level resource management systems normally concern the scheduling activities within a single virtual organization, high-level schedulers focus on the large scale resources utilization with unstable resource availability, low reliability networks, multi-policies, multi-administrative domains, etc. In this paper, we propose a decentralized framework named SmartGRID to tackle high-level grid resource management and scheduling. Within the SmartGRID framework, swarm intelligence algorithms are used for resource discovery and monitoring, standard protocols and schemes are adopted for scheduler interoperability, and an embedded plugin mechanism is provided to utilize multi-type external scheduling strategies. With a clearly decoupled layered architecture, SmartGRID has been designed to be a generic and modular environment to support intelligent and interoperable grid resource management upon a volatile, dynamics, and heterogeneous grid computing infrastructure.

Walking-pad: a step-in-place locomotion interface for virtual environments

This paper presents a new locomotion interface that provides users with the ability to engage in a life-like walking experience using stepping in place. Stepping actions are performed on top of a flat platform that has an embedded grid of switch sensors that detect footfalls pressure. Based on data received from sensors, the system can compute different variables that represent users walking behavior such as walking direction, walking speed, standstill, jump, and walking. The overall platform status is scanned at a rate of 100Hz with which we can deliver real-time visual feedback reaction to user actions. The proposed system is portable and easy to integrate into major virtual environment with large projection feature such as CAVE and DOME systems. The overall weight of the Walking-Pad is less than 5 Kg and can be connected to any computer via USB port, which make it even controllable via a portable computer.

Towards an Integrated Vision across Inter-cooperative Grid Virtual Organizations

Much work has been done to exploit the benefit brought by allowing job execution on distributed computational resources. Nodes are typically able to share jobs only within the same virtual organization, which is inherently bounded by various reasons such as the adopted information system or other agreed constraints. The problem raised by such limitation is thus related to finding a way to enable interoperation between nodes from different virtual organizations. We introduce a novel technique for integrating visions from both resource users and providers, allowing to serve multiple virtual organizations as a whole. By means of snapshot data stored within each grid node, such as processing and interacting history, we propose a demand-centered heuristic scheduling approach named Critical Friend Community (CFC). To this end, a set of simplified community scheduling targeted algorithms and processing workflows are described. A prototype of our scheduling approach is being implemented within the SmartGRID project.

Using Metadata Snapshots for Extending Ant-Based Resource Discovery Service in Inter-cooperative Grid Communities

Much work is under way within the resource management community on issues associated with grid scheduling upon dynamically discovered information. In this paper we tackle the problem by exploiting a bio-inspired resource discovery mechanism, where information is provided by ant-based lightweight mobile agents traveling across a grid network and collecting data from each visited node. We start by providing the current state of the adopted grid scheduler, which is the result of an existing collaborative project named SmartGRID, and its underlying architecture constructed by ant-based mobile agents. We consider the problem of discovering resources in specific grid communities, which are bounded due to different shared community policies, such as diverse ant colonies, different resource discovery approaches, or other issues. Several issues have been raised during the design and implementation of such infrastructure. A notable issue, namely how grid schedulers from various bounded grid communities could be used in a manner which would extend current SmartGRID functionality is identified. Our shared view is that by utilizing already discovered and stored grid nodes’s metadata snapshots in the first instance we can facilitate a more convenient and efficient resource discovery operation next time. With this in mind, our paper goes on describing our shared vision with regard to this extended functionality as well as discussing the new conceptual basis and its model architecture.

Solenopsis: A Framework for the Development of Ant Algorithms

Network resources management issues in complex and dynamic scenarios require decentralized solutions and adaptive systems to face critical and unattended situations. Bio-inspired techniques such as swarm intelligence algorithms, have proved to be robust and suitable for managing tasks like routing, load-balancing or resource discovery. In this paper we describe Solenopsis, a framework for the development, simulation and deployment of ant-algorithms, which is aimed at supporting network management middlewares. The system provides a modular and scalable environment that can be distributed over a network. Ants are coded using a simple programming language, and are able to migrate across nodes. Two basic load-balancing algorithms are presented and evaluated, as an example of how this tool works and can be used in practice.

MaGate Simulator: A Simulation Environment for a Decentralized Grid Scheduler

This paper presents a simulator for of a decentralized modular grid scheduler named MaGate. MaGates design emphasizes scheduler interoperability by providing intelligent scheduling serving the grid community as a whole. Each MaGate scheduler instance is able to deal with dynamic scheduling conditions, with continuously arriving grid jobs. Received jobs are either allocated on local resources, or delegated to other MaGates for remote execution. The proposed MaGate simulator is based on GridSim toolkit and Alea simulator, and abstracts the features and behaviors of complex fundamental grid elements, such as grid jobs, grid resources, and grid users. Simulation of scheduling tasks is supported by a grid network overlay simulator executing distributed ant-based swarm intelligence algorithms to provide services such as group communication and resource discovery. For evaluation, a comparison of behaviors of different collaborative policies among a community of MaGates is provided. Results support the use of the proposed approach as a functional ready grid scheduler simulator.

A context-aware middleware for multimodal dialogue applications with context tracing

This paper presents a context-aware middleware for multimodal dialogue applications. The middleware has the context tracing feature, which is the possibility of the middleware to explain why and how a situation occurs (or occurred). The middleware consists of several agents communicating with each other and an ontology is used to describe various concepts such as resources, situations, plans, structure of context history, and data to be exchanged between the agents.

Grounding Agents in EMud Artificial Worlds

This paper suggests that in the context of autonomous agents and generation of intelligent behavior for such agents, a more important focus should be held on the symbolic context that forms the basis of computer programs. Basically, software agents are symbolic entities living in a symbolic world and this has an effect on how one should think about designing frameworks for their evolution or learning. We will relate the problem of symbol grounding to that of sensory information available to agents. We will then introduce an experimental environment based on virtual worlds called EMuds, where both human and artificial agents can interact. Next, we show how it can be applied in the framework of multi-agent systems to address emergence based problems and report preliminary results. We then conclude with some ongoing and future work.

A Generic Coordination Model for Pervasive Computing Based on Semantic Web Languages

Human interaction occurs always in a specific context and in a particular environment, and a common knowledge base about them is essential for understanding each other. By immersing computational system into physical world, pervasive computing bring us from traditional desktop computing interaction, to a new paradigm of interaction closer to Humans one’s in term of context and environment dependency, and knowledge sharing.

MaGate: An Interoperable, Decentralized and Modular High-Level Grid Scheduler

This work presents the design and architecture of a decentralized grid scheduler named MaGate, which is developed within the SmartGRID project and focuses on grid scheduler interoperation. The MaGate scheduler is modular structured, and emphasizes the functionality, procedure and policy of delegating local unsuited jobs to appropriate remote MaGates within the same grid system. To avoid an isolated solution, web services and several existing and emerging grid standards are adopted, as well as a series of interfaces to both publish MaGate capabilities and integrate functionalities from external grid components. Meanwhile, a specific swarm intelligence solution is employed as a critical complementary service for MaGate to maintain an optimized peer-to-peer overlay that supports efficient resource discovery. Regarding evaluation, the effectiveness brought by job sharing within a physically connected grid community with the use of the MaGate has been illustrated by means of experiments on communities of different scale, and under various scenarios.