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.
Keywords
- Cloud bursting
- Proactive control
- Application metrics
- Workload orchestration
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References
Data Science on Prometheus Metrics. https://github.com/AICoE/prometheus-data-science
Understanding LSTM Networks. https://colah.github.io/posts/2015-08-Understanding-LSTMs/
Acknowledgments
This work has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 815141 (DECENTER: Decentralised technologies for orchestrated Cloud-to-Edge intelligence) and internal funding from Konica Minolta by providing support for the LightEdge project in collaboration with FBK.
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Faticanti, F. et al. (2020). Distributed Cloud Intelligence: Implementing an ETSI MANO-Compliant Predictive Cloud Bursting Solution Using Openstack and Kubernetes. In: Djemame, K., Altmann, J., Bañares, J.Á., Agmon Ben-Yehuda, O., Stankovski, V., Tuffin, B. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2020. Lecture Notes in Computer Science(), vol 12441. Springer, Cham. https://doi.org/10.1007/978-3-030-63058-4_8
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DOI: https://doi.org/10.1007/978-3-030-63058-4_8
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