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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Distributed Cloud Intelligence: Implementing an ETSI MANO-Compliant Predictive Cloud Bursting Solution Using Openstack and Kubernetes
verfasst von : Francescomaria Faticanti, Jason Zormpas, Sergey Drozdov, Kewin Rausch, Orlando Avila García, Fragkiskos Sardis, Silvio Cretti, Mohsen Amiribesheli, Domenico Siracusa
Erschienen in: Economics of Grids, Clouds, Systems, and Services
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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.