This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.

Demo: AIML-as-a-Service for SLA management of a Digital Twin Virtual Network Service / Baranda, J.; Mangues-Bafalluy, J.; Zeydan, E.; Casetti, C.; Chiasserini, C. F.; Malinverno, M.; Puligheddu, C.; Groshev, M.; Guimaraes, C.; Tomakh, K.; Kucherenko, D.; Kolodiazhnyi, O.. - STAMPA. - (2021). ((Intervento presentato al convegno IEEE INFOCOM 2021 - Demo Session tenutosi a Virtual conference due to COVID-19 nel May 2021 [10.1109/INFOCOMWKSHPS51825.2021.9484610].

Demo: AIML-as-a-Service for SLA management of a Digital Twin Virtual Network Service

C. Casetti;C. F. Chiasserini;M. Malinverno;C. Puligheddu;
2021

Abstract

This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2871096