Microsoft Technology Licensing, LLC

Murali Mohan Chintalapati

Ken Hsieh

Yingnong Dang

Pankaj K Singh

Sree Krishna Chaitanya Vadrevu

Ze Li

Arun Upadhyaya Kishan

Zhangwei Xu

Qingwei Lin

Oren Rosenbloom

Todd F Pfleiger

Karthikeyan Subramanian

Pritesh Patwa

Nicholas Anthony Swanson

Govind Ramaswamy

Anupama Vedapuri

Ravikiran Janardhan Reddy

Vincent Joseph Blas

Chunxiao Jia

Shuishi Yang

Youjiang Wu

Daniele Maso

Najam Shahid

Randolph Yao

Bing Hu

Peiling Wu

Chaturanan Damodar Mishra

Igor Figlin

Robert Gerard Boyce

Xiaohan Yan

Systems, methods, and devices are described for intelligent outage evaluation and insight management for monitoring and incident management systems. A set of incident logs associated with a cloud computing service are received by a cloud computing service provider system. Each incident log includes impact data corresponding to a respective incident that occurred during an evaluation period. The impact data includes an interruption count representative of a number of interruptions on resources caused by the respective incident. A total active resource time representative of resource activity during the evaluation period is determined. An interruption rate is determined for the evaluation period by summing interruption counts to generate a total interruption count and dividing the total interruption count by the total active resource time. An actionable insight applicable to the cloud computing service is generated based on the interruption rate and the incident logs.

Intelligent outage evaluation and insight management for monitoring and incident management systems

The present disclosure relates to systems, methods, and computer-readable media for determining optimal index configurations for intelligently managing updates of virtual machines in an offline manner in a cloud computing system. For instance, a virtual machine (VM) update system can efficiently determine when to apply updates to virtual machines in an intelligent manner that prevents the updates from interfering with the deallocation of virtual machines. In addition, the VM update system can utilize the operating system (OS) disk image snapshots to automatically provide safeguards and ensure that updates do not degrade the performance of the virtual machines, or in the case of an update failure, that the virtual machines are restored to their previous state without the data loss.

Managing updates on virtual machine systems

The present disclosure relates to systems, methods, and computer-readable media for identifying anomalies of failure events on a cloud computing system and determining cross-component and cross-layer correlation between change events that occur on the cloud computing system and the failure events associated with the anomalies. In particular, this disclosure describes a system that receives telemetry related to change events and failure events across any number of computing layers of a distributed computing environment (e.g., a cloud computing system) and detects anomalies based on counts of failure events that are manifested over discrete periods of time. Based on these detected anomalies, the anomaly correlation system can determine cross-layer and cross-component correlations between selective change events and the detected anomalies of failure events. The anomaly correlation system may further generate and provide recommendations related to mitigating or otherwise addressing the anomalies based on the determined correlations.

Detecting and mitigating cross-layer impact of change events on a cloud computing system

Methods, systems and computer program products are described for obtaining deployment signals comprising information relating to deployments of software components to a plurality of computing devices, obtaining fault signals comprising information relating to faults encountered by the plurality of computing devices, and obtaining device type information that describes a device type of each of the plurality of computing devices. Based on the deployment signals, fault signals, and device type information, a correlation score for each combination of software component, fault, and device type is obtained. Based on the correlation scores, attribution level decisions, fault type level decisions and device type level decisions are made. Based on these decisions, an overall decision is rendered whether to proceed or not proceed with a future deployment of the software component.

Quality assessment and decision recommendation for continuous deployment of cloud infrastructure components

A system may include a node historical state data store having historical node state data, including a metric that represents a health status or an attribute of a node during a period of time prior to a node failure. A node failure prediction algorithm creation platform may generate a machine learning trained node failure prediction algorithm. An active node data store may contain information about computing nodes in a cloud computing environment, including, for each node, a metric that represents a health status or an attribute of that node over time. A virtual machine assignment platform may then execute the node failure prediction algorithm to calculate a node failure probability score for each computing node based on the information in the active node data store. As a result, a virtual machine may be assigned to a selected computing node based at least in part on node failure probability scores.

Computing node failure and health prediction for cloud-based data center

A distributed computing system. The system separates management and worker functionality into different clusters thus relieving the worker clusters of management overhead. In particular, the system includes a management cluster. The management cluster is configured to perform service deployment of worker nodes, service maintenance of worker nodes, service upgrade and roll-back of worker nodes, service monitoring of worker nodes, and service reporting of worker nodes. The system further includes one or more worker clusters coupled the management cluster, but separate from the management cluster. Each of the worker clusters includes one or more worker nodes that are configured to provide one or more of processing power, storage, and network connectivity but do not exercise one or more of the management configured functions.

Building large scale infrastructure using hybrid clusters

Growing community of inventors

Bellevue, WA, United States of America

Murali Mohan Chintalapati