By Ricardo Bianchini, Marcus Fontoura, Eli Cortez, Anand Bonde, Alexandre Muzio, Ana-Maria Constantin, Thomas Moscibroda, Gabriel Magalhaes, Girish Bablani, Mark Russinovich
Communications of the ACM,
Vol. 63 No. 2, Pages 50-59
Cloud platforms, such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform, are tremendously complex. For example, the Azure Compute fabric governs all the physical and virtualized resources running in Microsoft's datacenters. Its main resource management systems include virtual machine (VM) and container (hereafter we refer to VMs and containers simply as "containers") scheduling, server and container health monitoring and repairs, power and energy management, and other management functions.
Cloud platforms are also extremely expensive to build and operate, so providers have a strong incentive to optimize their use. A nascent approach is to leverage machine learning (ML) in the platforms' resource management using supervised learning techniques, such as gradient-boosted trees and neural networks, or reinforcement learning. We also discuss why ML is often preferable to traditional non-ML techniques.
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