While solutions for cloud bursting already exist and are commercially available, they often rely on a limited set of metrics that are monitored and acted upon when user-defined thresholds are exceeded. In this paper, we present an ETSI MANO compliant approach that performs proactive bursting of applications based on infrastructure and application metrics. The proposed solution implements Machine Learning (ML) techniques to realise a proactive offloading of tasks in anticipation of peak utilisation that is based on pattern recognition from historical data. Experimental results comparing several forecasting algorithms show that the proposed approach can improve upon reactive cloud bursting solutions by responding quicker to system load changes. This approach is applicable to both traditional datacentres and applications as well as 5G telco infrastructures that run Virtual Network Functions (VNF) at the edge.

Distributed Cloud Intelligence: Implementing an ETSI MANO-Compliant Predictive Cloud Bursting Solution Using Openstack and Kubernetes / Faticanti, F.; Zormpas, J.; Drozdov, S.; Rausch, K.; Garcia, O. A.; Sardis, F.; Cretti, S.; Amiribesheli, M.; Siracusa, D.. - 12441:(2020), pp. 80-85. ((Intervento presentato al convegno 17th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020 tenutosi a svn nel 2020 [10.1007/978-3-030-63058-4_8].

Distributed Cloud Intelligence: Implementing an ETSI MANO-Compliant Predictive Cloud Bursting Solution Using Openstack and Kubernetes

Faticanti F.;Siracusa D.
2020

Abstract

While solutions for cloud bursting already exist and are commercially available, they often rely on a limited set of metrics that are monitored and acted upon when user-defined thresholds are exceeded. In this paper, we present an ETSI MANO compliant approach that performs proactive bursting of applications based on infrastructure and application metrics. The proposed solution implements Machine Learning (ML) techniques to realise a proactive offloading of tasks in anticipation of peak utilisation that is based on pattern recognition from historical data. Experimental results comparing several forecasting algorithms show that the proposed approach can improve upon reactive cloud bursting solutions by responding quicker to system load changes. This approach is applicable to both traditional datacentres and applications as well as 5G telco infrastructures that run Virtual Network Functions (VNF) at the edge.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
svn
Springer Science and Business Media Deutschland GmbH
978-3-030-63057-7
978-3-030-63058-4
Faticanti, F.; Zormpas, J.; Drozdov, S.; Rausch, K.; Garcia, O. A.; Sardis, F.; Cretti, S.; Amiribesheli, M.; Siracusa, D.
Distributed Cloud Intelligence: Implementing an ETSI MANO-Compliant Predictive Cloud Bursting Solution Using Openstack and Kubernetes / Faticanti, F.; Zormpas, J.; Drozdov, S.; Rausch, K.; Garcia, O. A.; Sardis, F.; Cretti, S.; Amiribesheli, M.; Siracusa, D.. - 12441:(2020), pp. 80-85. ((Intervento presentato al convegno 17th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2020 tenutosi a svn nel 2020 [10.1007/978-3-030-63058-4_8].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/297101
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