Microsoft Technology Licensing, LLC

Sanjay Ramanujan

Marwan E Jubran

Karthikeyan Subramanian

Saurabh Agarwal

Thomas Moscibroda

Aleksandr Mikhailovich Gershaft

Gurpreet Virdi

Yunus Mohammed

Shandan Zhou

Weiping Hu

Ambrose Thomas Treacy

Gowtham Natarajan

Sorin Iftimie

Luke Rafael Rodriguez

Neha Keshari

Xing Wen Wang

Rostislav Sudakov

Vi Lam Nguyen

Fernando Gonzalez Todisco

Xiaoguang Fan

Andrew Tianze Wang

Muhammad Khizar Qazi

Yash Purohit

Vinayak Ramnath Karnataki

Maitreyee Ramprasad Joshi

Marwan Elias Jubran

A cloud computing capacity management system can include a fine-grained admission control layer, a policy engine, and an enforcement layer. The fine-grained admission control layer can be configured to ingest capacity signals and create a capacity mitigation policy, based at least in part on the capacity signals, to protect available capacity of a cloud computing system for prioritized users. The capacity mitigation policy can be directed to users of the cloud computing system. The policy engine can be configured to control how the capacity mitigation policy is applied to the cloud computing system. The enforcement layer can be configured to handle incoming resource requests and to enforce resource limits based on the capacity mitigation policy as applied by the policy engine.

Cloud computing capacity management system using automated fine-grained admission control

An automated root-cause analysis (RCA) system may provide a fully automated platform that provides dependency and execution order modeling for tasks included in a capacity provisioning process, anomaly detection, ticket correlation, root-cause analysis, monitoring and feedback, and data visualization. The automated RCA system may continuously collect and store data for use in determining a root cause of a blockage on a capacity provisioning process. The blockage may be identified in a ticket generated by a cloud-computing system. The automated RCA system may receive the ticket and attempt to determine the root cause of the blockage based on root causes associated with previous tickets generated by the cloud-computing system. The automated RCA system may identify a true root cause, recommend repair items based on the true root cause, identify one or more responsible teams to drive a fix, and provide an estimated time for completion.

Automatic root cause analysis and prediction for a large dynamic process execution system

To improve the reliability of nodes that are utilized by a cloud computing provider, information about the entire lifecycle of nodes can be collected and used to predict when nodes are likely to experience failures based at least in part on early lifecycle errors. In one aspect, a plurality of failure issues experienced by a plurality of production nodes in a cloud computing system during a pre-production phase can be identified. A subset of the plurality of failure issues can be selected based at least in part on correlation with service outages for the plurality of production nodes during a production phase. A comparison can be performed between the subset of the plurality of failure issues and a set of failure issues experienced by a pre-production node during the pre-production phase. A risk score for the pre-production node can be calculated based at least in part on the comparison.

Node health prediction based on failure issues experienced prior to deployment in a cloud computing system

A method for minimizing allocation failures in a cloud computing system without overprovisioning may include determining a predicted supply for a virtual machine series in a system unit of the cloud computing system during an upcoming time period. The predicted supply may be based on a shared available current capacity and a shared available future added capacity for the virtual machine series in the system unit. The method may also include predicting an available capacity for the virtual machine series in the system unit during the upcoming time period. The predicted available capacity may be based at least in part on a predicted demand for the virtual machine series in the system unit during the upcoming time period and the predicted supply. The method may also include taking at least one mitigation action in response to determining that the predicted demand exceeds the predicted supply during the upcoming time period.

Capacity management in a cloud computing system using virtual machine series modeling

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Sanjay Ramanujan