Underlying Architecture

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Mark Burgess - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Pull-Based Load Balancing for Autonomic Servers
    2008
    Co-Authors: Rémi Badonnel, Mark Burgess
    Abstract:

    The growing autonomy of servers may significantly deteriorate the performance of traditional load-balancing strategies. Indeed, the authoritative decision belongs to the load-balancer, but the autonomous servers may reject the requests on their own convenience. We propose in this paper an original load-balancing strategy for transferring this authority from the load-balancer to the autonomous servers. We describe the Underlying Architecture and evaluate our solution based on a first set of experimentations.

  • NOMS - Dynamic pull-based load balancing for autonomic servers
    NOMS 2008 - 2008 IEEE Network Operations and Management Symposium, 2008
    Co-Authors: Rémi Badonnel, Mark Burgess
    Abstract:

    The growing autonomy of servers may significantly deteriorate the performance of traditional load-balancing strategies. Indeed, the authoritative decision belongs to the load-balancer, but the autonomous servers may reject the requests on their own convenience. We propose in this paper an original load-balancing strategy for transferring this authority from the load-balancer to the autonomous servers. We describe the Underlying Architecture and evaluate our solution based on a first set of experimentations.

Rémi Badonnel - One of the best experts on this subject based on the ideXlab platform.

  • Towards Vulnerability Prevention in Autonomic Networks and Systems
    2011
    Co-Authors: Martin Barrere, Rémi Badonnel, Olivier Festor
    Abstract:

    The autonomic paradigm has been introduced in order to cope with the growing complexity of management. In that context, autonomic networks and systems are in charge of their own configuration. However, the changes that are operated by these environments may generate vulnerable configurations. In the meantime, a strong standardization effort has been done for specifying the description of configuration vulnerabilities. We propose in this paper an approach for integrating these descriptions into the management plane of autonomic systems in order to ensure safe configurations. We describe the Underlying Architecture and a set of preliminary results based on the Cfengine configuration tool.

  • Dynamic Pull-Based Load Balancing for Autonomic Servers
    2008
    Co-Authors: Rémi Badonnel, Mark Burgess
    Abstract:

    The growing autonomy of servers may significantly deteriorate the performance of traditional load-balancing strategies. Indeed, the authoritative decision belongs to the load-balancer, but the autonomous servers may reject the requests on their own convenience. We propose in this paper an original load-balancing strategy for transferring this authority from the load-balancer to the autonomous servers. We describe the Underlying Architecture and evaluate our solution based on a first set of experimentations.

  • NOMS - Dynamic pull-based load balancing for autonomic servers
    NOMS 2008 - 2008 IEEE Network Operations and Management Symposium, 2008
    Co-Authors: Rémi Badonnel, Mark Burgess
    Abstract:

    The growing autonomy of servers may significantly deteriorate the performance of traditional load-balancing strategies. Indeed, the authoritative decision belongs to the load-balancer, but the autonomous servers may reject the requests on their own convenience. We propose in this paper an original load-balancing strategy for transferring this authority from the load-balancer to the autonomous servers. We describe the Underlying Architecture and evaluate our solution based on a first set of experimentations.

Tatsuo Nakajima - One of the best experts on this subject based on the ideXlab platform.

Saad Alqithami - One of the best experts on this subject based on the ideXlab platform.

  • A Generic Encapsulation to Unravel Social Spreading of a Pandemic: An Underlying Architecture
    Computers, 2021
    Co-Authors: Saad Alqithami
    Abstract:

    Cases of a new emergent infectious disease caused by mutations in the coronavirus family, called “COVID-19,” have spiked recently, affecting millions of people, and this has been classified as a global pandemic due to the wide spread of the virus Epidemiologically, humans are the targeted hosts of COVID-19, whereby indirect/direct transmission pathways are mitigated by social/spatial distancing People naturally exist in dynamically cascading networks of social/spatial interactions Their rational actions and interactions have huge uncertainties in regard to common social contagions with rapid network proliferations on a daily basis Different parameters play big roles in minimizing such uncertainties by shaping the understanding of such contagions to include cultures, beliefs, norms, values, ethics, etc Thus, this work is directed toward investigating and predicting the viral spread of the current wave of COVID-19 based on human socio-behavioral analyses in various community settings with unknown structural patterns We examine the spreading and social contagions in unstructured networks by proposing a model that should be able to (1) reorganize and synthesize infected clusters of any networked agents, (2) clarify any noteworthy members of the population through a series of analyses of their behavioral and cognitive capabilities, (3) predict where the direction is heading with any possible outcomes, and (4) propose applicable intervention tactics that can be helpful in creating strategies to mitigate the spread Such properties are essential in managing the rate of spread of viral infections Furthermore, a novel spectra-based methodology that leverages configuration models as a reference network is proposed to quantify spreading in a given candidate network We derive mathematical formulations to demonstrate the viral spread in the network structures © 2021 by the authors Licensee MDPI, Basel, Switzerland

Tyler S. Greenway - One of the best experts on this subject based on the ideXlab platform.