Abstract
Cloud computing offers an online, on-demand and pay-as-you-go access to computing resources. The cloud enables users to adjust their consumption to their needs. Users deploy their application code, libraries and operating systems on the provider’s hardware. The resources can be allocated under the form of virtual machines (VMs). Predicting the runtime of VMs can be useful to optimize the resource allocation. We propose a formulation of this objective as a multi-class classification problem by using as much features as available when launching a VM. Experimentation carried out on real traces from the public cloud provider Outscale show that the inclusion of features extracted from tags, which are freely-typed pieces of text used to describe VMs for human operators, improve the model performance.
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Perennou, L., Chiky, R. (2019). Applying Supervised Machine Learning to Predict Virtual Machine Runtime for a Non-hyperscale Cloud Provider. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_58
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