@article{karabetian_environmentally-sustainable_2022,
	title = {An {Environmentally}-sustainable {Dimensioning} {Workbench} towards {Dynamic} {Resource} {Allocation} in {Cloud}-computing {Environments}},
	doi = {10.1109/IISA56318.2022.9904367},
	abstract = {With the exponential growth in data generated every year, Big Data has become one of the core research subjects in the overall computing domain. But when considering big data scenarios in a cloud centric environment, the need for a resource management mechanism is of vital importance. Under those circumstances, intelligent allocation of resources can have a direct and noticeable impact on application performance. The aim of this paper is to present a solution on dynamic resource allocation for efficient cloud scalability. This is made possible by using machine learning algorithms as well as user feedback, in order to generate an adequate resource forecasting model. The efficiency of the tool is evaluated by repeatedly executing extensive analysis of various datasets provided by the end users, exploiting the cloud computing paradigm for their analytic purposes. The given solution is able to learn and enhance its knowledge graph considering user feedback, as well as previously executed processes in our cloud environment. To this extent, the forecasting model will attempt to estimate optimal resource allocation for each user scenario.},
	journal = {13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022},
	author = {Karabetian, Andreas and Kiourtis, Athanasios and Voulgaris, Konstantinos and Karamolegkos, Panagiotis and Poulakis, Yannis and Mavrogiorgou, Argyro and Kyriazis, Dimosthenis},
	year = {2022},
	note = {Publisher: IEEE
ISBN: 9781665463904},
	keywords = {big data, cloud computing, horizontal scaling, infrastructure as a service, knowledge-based systems, optimal allocation},
	pages = {1--4},
}

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Under those circumstances, intelligent allocation of resources can have a direct and noticeable impact on application performance. The aim of this paper is to present a solution on dynamic resource allocation for efficient cloud scalability. This is made possible by using machine learning algorithms as well as user feedback, in order to generate an adequate resource forecasting model. The efficiency of the tool is evaluated by repeatedly executing extensive analysis of various datasets provided by the end users, exploiting the cloud computing paradigm for their analytic purposes. The given solution is able to learn and enhance its knowledge graph considering user feedback, as well as previously executed processes in our cloud environment. 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But when considering big data scenarios in a cloud centric environment, the need for a resource management mechanism is of vital importance. Under those circumstances, intelligent allocation of resources can have a direct and noticeable impact on application performance. The aim of this paper is to present a solution on dynamic resource allocation for efficient cloud scalability. This is made possible by using machine learning algorithms as well as user feedback, in order to generate an adequate resource forecasting model. The efficiency of the tool is evaluated by repeatedly executing extensive analysis of various datasets provided by the end users, exploiting the cloud computing paradigm for their analytic purposes. The given solution is able to learn and enhance its knowledge graph considering user feedback, as well as previously executed processes in our cloud environment. 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