contributed articles

I M A G E B Y M A R C E L C L E M E N S

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.

Public cloud providers are starting to explore ML-based resource management in production. 9, 14 For example, Google uses neural networks to optimize fan speeds and other energy knobs. 14 In academia, researchers have proposed using collaborative filtering— a common technique in recommender systems—in scheduling containers for reduced with in-server performance interference. 12 Others proposed using reinforcement learning to adjust the resources allocated to co-located VMs. 24 Later, we discuss other opportunities for ML-based management.

Despite these prior efforts and opportunities, it is currently unclear how best to integrate ML into cloud resource management. In fact, prior approaches differ in multiple dimensions. For example, in some cases, the ML technique produces insights/ predictions about the workload or infrastructure; in others, it produces actual resource management actions. In some cases, the ML is deeply integrated with the resource manager; in others, it is completely separate. In all cases, the ML addresses a single management problem; a different problem requires

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’
Toward
ML-Centric
Cloud
Platforms
DOI: 10.1145/3364684

Exploring the opportunities to use ML, the possible designs, and our experience with Microsoft Azure.

BY RICARDO BIANCHINI, MARCUS FONTOURA, ELI CORTEZ,
ANAND BONDE, ALEXANDRE MUZIO, ANA-MARIA CONSTANTIN,
THOMAS MOSCIBRODA, GABRIEL MAGALHAES,
GIRISH BABLANI, AND MARK RUSSINOVICH

key insights

˽ There are many potential uses of ML in cloud computing platforms. The challenge is in defining exactly how and where ML should be infused in these platforms.

˽ Leveraging ML-derived predictions has shown promise for many resource managers in Azure Compute. Having a general and independent ML framework/ system has been key to increasing adoption quickly.

˽ Many research challenges remain open, including how to make action-prescribing ML general enough for wide applicability in cloud platforms, how to manage (potentially partial) feedback at scale, and how to debug misbehaviors (especially when the ML is tightly integrated with resource managers).