Conference paper Open Access

Alfa-OMC: cost-aware deep learning for mobile network resource orchestration

Bega, Dario; Gramaglia, Marco; Fiore, Marco; Banchs, Albert; Costa-Perez, Xavier


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    <subfield code="a">© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or  reuse of any copyrighted component of this work in other works.</subfield>
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    <subfield code="a">Alfa-OMC: cost-aware deep learning for mobile network resource orchestration</subfield>
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    <subfield code="a">5G Mobile Network Architecture for diverse services, use cases, and applications in 5G and beyond</subfield>
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    <subfield code="a">&lt;p&gt;Orchestrating resources in 5G and beyond-5G systems will be substantially more complex than it used to be in previous generations of mobile networks. In order to take full advantage of the unprecedented possibilities for dynamic reconfiguration offered by network softwarization and virtualization technologies, operators have to embed intelligence in network resource orchestrators. We advocate that the automated, data-driven decisions taken by orchestrators must be guided by considerations on the cost that such decisions involve for the operator. We show that such a strategy can be implemented via a deep learning architecture that forecasts capacity rather than plain traffic, thanks to a novel loss function named alfa-OMC. We investigate the convergence properties of alfa-OMC, and provide preliminary results on the performance of the learning process in case studies with real-world mobile network traffic.&lt;/p&gt;</subfield>
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