On prediction of resource consumption of service requests in cloud environments

Derakhshan, Fariborz; Roessler, Horst; Schefczik, Peter; Randriamasy, Sabine

doi:10.1109/icin.2017.7899406

Cited by 4 publications

(1 citation statement)

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“…The author in [7] proposed offloading scheme, where the task data submitted by the user is uploaded to its nearby fog node, then the fog node will take a decision for offloading the task to its neighbor fog node or remote cloud to satisfy the delay deadline received from the end user. In [8] the authors used the machine learning as a prediction model to predict cloud processing resource consumption. The proposed model uses an artificial intelligence algorithm for future request prediction by dividing the incoming request into multiple consumption classes depending on their consumption amount, then the request is distributed among processing unites.…”

Section: Related Workmentioning

confidence: 99%

Resource Selection Service Based on Neural Network in Fog Environment

Mostafa¹

2020

Adv. sci. technol. eng. syst. j.

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As an emergent technology in Internet of Things (IoT), the ultimate target of fog computing is to provide a widely distributed computational resources and data repository closer to the network edge providing heterogeneous systems both in terms of software and hardware. The fog system must have the capability to deal with huge number of resources and users at the same time, such increased size sometimes presents the issue of performance degradation. Therefore, the fog should be able to support adaptability, scalability, and extensibility to avoid such degradation by adopting efficiently and effectively an optimal resource selection and allocation model. As many fog users have limited interest in, or knowledge of fog middleware issues, it follows that in a large fog environment the best approach would be to have an automated system for resource selection and allocation. Such an approach eliminates the need for user intervention. This paper proposes a fog resource selection service based on neural network to perform resource selection tasks by coordinating with the metascheduler. Five different selection algorithm were used to evaluate the prediction model for resource selection. In addition to introducing a history update and management algorithm to manage and control the storage of the history log records.

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Section: Related Workmentioning

confidence: 99%