Machine-learning-based replenishment of interruptible workloads in cloud environment

US12056521B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Inigo GOIRI PRESA · Redmond, US
• Rakesh AKKERA · Wautauga Beach, US
• Eli Cortez Custodio Vilarinho · Redmond, US
• Felipe VIEIRA FRUJERI · Kirkland, US
• Yunus Mohammed · Redmond, US
• Thomas Moscibroda · Beijing, CN
• Gurpreet Virdi · Redmond, US
• Sandeep Kumta VISHNU · Bellevue, US
• Yandan WANG · Kirkland, US

Key dates

• Filing date: Sep 3, 2021
• Grant date: Aug 6, 2024
• Expiry date: Feb 2, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F2209/5019
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Systems and methods are provided for scheduling a virtual machine (VM) to host a workload in a cloud system. In particular, the disclosed technology schedules an evicted VM for redeploying an interruptible workload. The scheduling is based on capacity prediction and inference data associated with a type of the evicted VM. Capacity signal predictor generates training data for training a machine learning model using capacity signal history data of the cloud system. The machine-learning model, once trained, predicts capacity including a rate of evictions for the types of the evicted VM. The predicted data is based on at least the current status of available computing resources. Upon receiving a notice associated with a workload interruption, the intelligent scheduler prioritizes the evicted VM for scheduling and determines whether to defer redeploying the evicted VM based on the rate of eviction for the type of the evicted VM.