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Author: search.filters.author.Sangpetch, OrathaiSubject: search.filters.subject.Cloud computing

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    Thoth: Automatic resource management with machine learning for container-based cloud platform
    (2017-01-01)
    Sangpetch, Akkarit
    ;
    Sangpetch, Orathai
    ;
    Juangmarisakul, Nut
    ;
    Warodom, Supakorn
    Platform-As-A-Service (PaaS) providers often encounter fluctuation in computing resource usage due to workload changes, resulting in performance degradation. To maintain acceptable service quality, providers may need to manually adjust resource allocation according to workload dynamics. Unfortunately, this approach will not scale well as the number of applications grows. We thus propose Thoth, a dynamic resource management system for PaaS using Docker container technology. Thoth automatically monitors resource usage and dynamically adjusts appropriate amount of resources for each application. To implement the automatic-scaling algorithm, we select three algorithms, namely Neural Network, Q-Learning and our rule-based algorithm, to study and evaluate. The experimental results suggest that Q-Learning can the best adapt to the load changes, followed by a rule-based algorithm and NN. With Q-Learning, Thoth can save computing resources by 28.95% and 21.92%, compared to Neural Network and the rule-based algorithm respectively, without compromising service quality.
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    Security context framework for distributed healthcare IoT platform
    (2016-01-01)
    Sangpetch, Orathai
    ;
    Sangpetch, Akkarit
    As Internet of Things (IoT) is entering mainstream, data privacy and security in information exchange becomes a major concern and a barrier for potential adopters, especially in healthcare regime. Information from health IoT devices and services is sensitive and confidential. While many existing works have proposed enhancements and security prospects for individual devices and components in IoT ecosystems, they still do not address the underlying challenge which is the lack of sufficient security within systems. Effective security has to be built-in, not patched upon. To efficaciously tackle the challenge in distributed IoT systems, we present a security context framework which applies adaptive security contexts to properly track data of interest. The proposed solution can achieve accountability and track information propagation, involving devices, services and parties who have responsibility and potential legal liability. This could help leverage not just technical but also policy and legal aspects to enable health IoT adoption.