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Scalable and Flexible IoT data analytics: when Machine Learning meets SDN and Virtualization

Authors: Serra, Jordi; Sanabria-Russo, Luis; Pubill, David; Verikoukis, Christos;

Scalable and Flexible IoT data analytics: when Machine Learning meets SDN and Virtualization

Abstract

This paper deals with Internet of Things (IoT) data analytics in a collaborative platform where computing resources are available both at the network edge and at the backend cloud. Thereby, the requirements of both low-latency and delaytolerant IoT applications can be met. Moreover, this platform faces the challenging heterogeneous features of IoT data, i.e. its high dimensionality or its geo-distributed and streaming data nature. The proposed approach relies on two pillars. On the one hand, recent advances of machine learning (ML) techniques are leveraged to describe how the IoT data analytics can be performed in our platform. On the other hand, the virtualization, centralized management, global view and programmability of the computing and network resources is considered to fulfill the requirements of the ML methods. Unlike the related work, herein the interplay and synergies between those two pillars is explained. Also the ML methods for this collaborative platform are described in more detail.

© 2018 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.

Subjects by Vocabulary

Microsoft Academic Graph classification: Edge device Computer science Cloud computing Machine learning computer.software_genre Server business.industry Virtualization Scalability Data analysis Artificial intelligence High dimensionality business Internet of Things computer

Keywords

IoT, online learning, Edge-Cloud computing, SDN, Large-Scale Machine Learning, NFV, MANO

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    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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