Béat Hirsbrunner

Exploring decentralized dynamic scheduling for grids and clouds using the community-aware scheduling algorithm

Job scheduling strategies have been studied for decades in a variety of scenarios. Due to the new characteristics of the emerging computational systems, such as the grid and cloud, metascheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Equally, to overcome issues such as bottleneck, single point failure, and impractical unique administrative management, which are normally led by conventional centralized or hierarchical schemes, the decentralized scheduling scheme is emerging as a promising approach because of its capability with regards to scalability and flexibility.In this work, we introduce a decentralized dynamic scheduling approach entitled the community-aware scheduling algorithm (CASA). The CASA is a two-phase scheduling solution comprised of a set of heuristic sub-algorithms to achieve optimized scheduling performance over the scope of overall grid or cloud, instead of individual participating nodes. The extensive experimental evaluation with a real grid workload trace dataset shows that, when compared to the centralized scheduling scheme with BestFit as the metascheduling policy, the use of CASA can lead to a 30%-61% better average job slowdown, and a 68%-86% shorter average job waiting time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes. Highlights? We introduce a decentralized scheduling algorithm without requiring detailed node information. ? Our algorithm is able to adapt to the changes in grids through time by rescheduling. ? Comparisons with the known BestFit algorithm within a centralized scheduling scheme are made. ? Our algorithm leads to a 30%-61% better average job slowdown. ? Our algorithm leads to a 68%-86% shorter average job waiting time.

Community detection in complex networks using collaborative evolutionary algorithms

Scientific researchers from computer science, communication and as well from sociology and epidemiology reveal a strong interest in the study of networks. One important feature studied in complex network is the community structure. A new evolutionary technique for community detection in complex networks is proposed in this paper. The new algorithm is based on an information sharing mechanism between the individuals of a population. A real-world network is considered for numerical experiments.

Context aware service provisioning

In this paper, we present our initiative on a new direction of research for bringing into the common fold context-aware computing and Web services-oriented computing. Our long term research objective is to allow users to satisfy their needs regardless of their location and the resources that are considered in the performance of the services. Our contribution is the enhancement of the service discovery and composition by taking into account the available contextual information. As a first step towards this goal we are in the process of designing a new service description based on context.

Learning Invariant Sensorimotor Behaviors: A Developmental Approach to Imitation Mechanisms

This paper examines the interest of a developmental approach applied to the design of autonomous robots and the understanding of adaptive behaviors, such as imitation. The proposed model is a neural network architecture that learns and uses associations between vision and arm movements, even if the problem is ill posed (in the case of mapping problems between the visual space and the joints space of the arm). The central part of the model is a visuo-motor map able to represent the arm end point’s position in an ego-centered space (constrained by the vision) according to motor information (the proprioception). Sensorimotor behaviors such as tracking, pointing, spontaneous imitating, and sequences learning can then be obtained as the consequence of different internal dynamics computed on neural fields triggered by the visuo-motor map. The readout mechanism also explains how an apparently complex behavior can be generated and controlled from one simple internal dynamics and how at the same time the learning problems can be simplified. While highlighting the generic aspect of our model, we show that our robot can autonomously imitate and learn more complex sequences of gestures after the online learning of the visual and proprioceptive control of its hand extremity. Finally, we defend the idea of a co-development of imitative and sensorimotor capabilities, allowing the acquisition and the building of increasingly complex behavioral capabilities.

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.

Bounded diameter overlay construction: A self organized approach

This paper describes a distributed algorithm to construct and maintain a peer-to-peer network overlay with bounded diameter. The proposed approach merges a bio-inspired self-organized behavior with a pure peer-to-peer approach, in order to adapt the overlay to underlying changes in the network topology. Ant colonies are used to collect and spread information across all peers, whereas pheromone trails help detecting crashed nodes. Construction of the network favors balanced distribution of links across all peers, so that the resulting topology does not exhibit large hubs. Fault resilience and recovery mechanisms have also been implemented to prevent network partition in the event of node crashes. Validation has been conducted through simulations of different network scenarios.

Enabling efficient information discovery in a self-structured grid

One of the key success factors enabling the deployment of large scale grid systems is the existence of efficient resource discovery mechanisms. Accordingly, the main issues to be addressed by such a grid information system are those of scalability and minimal network overhead. In this respect, we propose a solution based on proactive information caching supported by a self-structured overlay topology. The proposed approach features a fully distributed ant-inspired self-organized overlay construction that maintains a bounded diameter overlay, and a selective flooding-based discovery algorithm that exploits local caches to reduce the number of visited nodes. To improve the caching scheme while retaining minimal bandwidth consumption, cache contents are periodically exchanged between neighboring nodes using an epidemic replication mechanism that is based on a gossiping algorithm, thus allowing nodes to have a more general view of the network and its resources. Extensive experimentation provides evidence that the average number of hops required to efficiently locate resources is limited and that our framework performs well with respect to hit rate and network overhead.

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.