Orchestrating Edge- and Cloud-based Predictive Analytics Services

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dc.contributor.author Chintapalli, Venkatarami Reddy
dc.contributor.author Kondepu, Koteswararao
dc.contributor.author Sgambelluri, Andrea
dc.contributor.author Franklin A., Antony
dc.contributor.author Tamma, Bheemarjuna Reddy
dc.contributor.author Castoldi, Piero
dc.contributor.author Valcarenghi, Luca
dc.date.accessioned 2020-11-06T13:03:48Z
dc.date.available 2020-11-06T13:03:48Z
dc.date.issued 2020-09-20
dc.identifier.bibliographicCitation Chintapalli, V. R., Kondepu, K., Sgambelluri, A., Franklin, A., Tamma, B.R., Castoldi, P. y Valcarenghi, L.(2020). Orchestrating Edge- and Cloud-based Predictive Analytics Services. In Proceedings of the 2020 European Conference on Networks and Communications (EuCNC).
dc.identifier.isbn 978-1-7281-4355-2
dc.identifier.uri http://hdl.handle.net/10016/31341
dc.description This paper has been presented at the 2020 European Conference on Networks and Communications (EuCNC)
dc.description.abstract In the Zero-touch network and service management (ZSM) architecture, devised by ETSI, making predictions on the observed data is among the functions provided by the analytics block of the control loop cycle. Prediction performance depends on several parameters, such as the utilized computational resources, the leveraged prediction techniques, the deployment location of the prediction tools with respect to the data.This paper proposes a Hybrid Forecast Framework (HFF) running at the network edge or in the cloud to provide fore-casting with the performance required by the control loop cycle. Forecasting at the edge might shorten the control loop cycle if resources shall be made available locally where data is collected. However, in general, edge computational resources are less abundant than the cloud ones, thus causing longer time to perform the prediction. On the opposite, forecasting in the cloud might require more time for the data to reach the utilized tools but more computational resources could be exploited. The HFF is based on utilizing traditional time series analysis prediction algorithms to minimize the utilized resources and energy at the edge while it exploits AI/ML tools to make predictions in the cloud.Results show that for short lead time (i.e., the time, in the future, at which the status of the considered parameter is predicted) edge-based prediction exploiting time series analysis provides better accuracy, requires less resources and time (thus energy) than cloud-based prediction. However, if the lead time is long, cloud-based prediction exploiting Artificial Intelligence/Machine Learning (AI/ML) provides better accuracy. Thus, if the lead time is long, it is preferable because the long lead time compensates for the higher time for prediction, mainly due to data transfer.
dc.description.sponsorship This work has been partially supported by the project "Scheme for Promotion of Academic and Research Collaboration (SPARC)", MHRD, Govt. of India and the EU Commission through the 5GROWTH project (grant agreement no. 856709).
dc.format.extent 6
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2020 IEEE.
dc.subject.other Forecasting
dc.subject.other AI/ML
dc.subject.other Time series analysis
dc.subject.other Edge
dc.subject.other Cloud
dc.title Orchestrating Edge- and Cloud-based Predictive Analytics Services
dc.type bookPart
dc.type conferenceObject
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/EuCNC48522.2020.9200902
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/856709
dc.type.version acceptedVersion
dc.relation.eventdate 15-18 June 2020
dc.relation.eventplace Dubrovnik, Croatia
dc.relation.eventtitle 2020 European Conference on Networks and Communications (EuCNC)
dc.relation.eventtype proceeding
dc.identifier.publicationtitle Proceedings of the 2020 European Conference on Networks and Communications (EuCNC)
dc.contributor.funder European Commission
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