FRINK Linked Data Fragments server

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• W2893013545 abstract "Abstract Regardless of their implementation aspects and distribution elements, i.e. centralized or distributed, service-based environments such as cloud computing and edge/fog infrastructures, enable the provisioning of services addressing a wide range of application domains. The key requirement for users and consumers of such services refers to the corresponding levels of quality, which is affected both by the real-world dynamics – given the non-deterministic use of services, and by the underlying resources state – given the typically virtualized sharing nature of the resources. In this paper, an approach is presented that aims at estimating the evolvement of services and resources state in order to provide insights for runtime adaptations, as required to ensure services quality. The state refers to different metrics/parameters such as memory, number of users, throughput, etc, and can be extended and applied to different ones. The proposed approach exploits polynomial regression and prediction to identify the aforementioned state evolvement by mapping the two first monitoring data points for each metric/parameter to the corresponding function that depicts their evolvement. The latter provides added value in different cases, including among others the adaptation of monitoring time intervals, the estimation of the potential breach of quality thresholds, and the prediction of the time for runtime adaptations and scaling decisions. The effectiveness of the implemented approach is demonstrated and evaluated through a set of different scenarios." @default.
• W2893013545 created "2018-10-05" @default.
• W2893013545 creator A5069674161 @default.
• W2893013545 date "2019-05-01" @default.
• W2893013545 modified "2023-09-27" @default.
• W2893013545 title "Modelling and prediction of resources and services state evolvement for efficient runtime adaptations" @default.
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• W2893013545 doi "https://doi.org/10.1016/j.future.2018.09.035" @default.
• W2893013545 hasPublicationYear "2019" @default.
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