Investigating Machine Learning Algorithms for Modeling SSD I/O Performance for Container-based Virtualization

Jean-Emile Dartois 1, 2 Jalil Boukhobza 3 Anas Knefati 1 Olivier Barais 2
2 DiverSe - Diversity-centric Software Engineering
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
3 Lab-STICC_UBO_CACS_MOCS
IBNM - Institut Brestois du Numérique et des Mathématiques, Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : One of the cornerstones of the cloud provider business is to reduce hardware resources cost by maximizing their utilization. This is done through smartly sharing processor, memory, network and storage, while fully satisfying SLOs negotiated with customers. For the storage part, while SSDs are increasingly deployed in data centers mainly for their performance and energy efficiency, their internal mechanisms may cause a dramatic SLO violation. In effect, we measured that I/O interference may induce a 10x performance drop. We are building a framework based on autonomic computing which aims to achieve intelligent container placement on storage systems by preventing bad I/O interference scenarios. One prerequisite to such a framework is to design SSD performance models that take into account interactions between running processes/containers, the operating system and the SSD. These interactions are complex. In this paper, we investigate the use of machine learning for building such models in a container based Cloud environment. We have investigated five popular machine learning algorithms along with six different I/O intensive applications and benchmarks. We analyzed the prediction accuracy, the learning curve, the feature importance and the training time of the tested algorithms on four different SSD models. Beyond describing modeling component of our framework, this paper aims to provide insights for cloud providers to implement SLO compliant container placement algorithms on SSDs. Our machine learning-based framework succeeded in modeling I/O interference with a median Normalized Root-Mean-Square Error (NRMSE) of 2.5%.
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https://hal.inria.fr/hal-02013421
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Jean-Emile Dartois, Jalil Boukhobza, Anas Knefati, Olivier Barais. Investigating Machine Learning Algorithms for Modeling SSD I/O Performance for Container-based Virtualization. IEEE transactions on cloud computing, IEEE, 2019, 14, pp.1-14. ⟨10.1109/TCC.2019.2898192⟩. ⟨hal-02013421⟩

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