Microsoft Technology Licensing, LLC

Beijing Didi Infinity Technology and Development Co., Ltd.

Si Qin

Thomas Moscibroda

Qingwei Lin

Karthikeyan Subramanian

Shandan Zhou

Yong Xu

Bo Qiao

Yingnong Dang

Ajay Mani

Yunus Mohammed

Gurpreet Virdi

Cong Chen

Saurabh Agarwal

Hang Dong

Nisarg Tarakkumar Sheth

Sorin Iftimie

Xinsheng Yang

Marko Lalic

Wanghai Gu

Sandeep Ramji

Paul Naveen Selvaraj

Yuwen Yang

Yi Zhang

Vivek Gupta

Chetan Bansal

John Lawrence Miller

Kamaljit S Bath

Eli Cortez

Hui Song

Inigo Goiri Presa

Yongtao Sha

Jian Zhang

Rakesh Namineni

Eli Cortez Custodio Vilarinho

Yu Kang

Jue Zhang

Shanti Kemburu

Felipe Vieira Frujeri

Yandan Wang

Mahmoud Sayed

Robert Edward Minneker

Alexander David Fischer

Preston Tapley Stephenson

Soumya Ram

Sidhanth M Panjwani

Camille Jean Couturier

Fangkai Yang

Bowen Pang

Mohit Verma

Julien Hoachuck

Namrata Jain

Jeffrey Ding He

Pooja Rani

Jimmy Wong

Christopher Cowdery

A method for dynamically adjusting a number of virtual machines for a workload, includes: receiving a probability indicator for each of a plurality of N sequential stages, where N is a natural number greater than 1, of a likelihood that a virtual machine assigned to a workload will be evicted during the N sequential stages; predicting a target number of virtual machines to configure in a current stage for a subsequent stage from among the plurality of N sequential stages based on the probability indicator, a target capacity for the workload, and a current price for maintaining a virtual machine; and configuring a number of virtual machines for the workload during the current stage based on the target number to be loaded for the workload for the subsequent stage.

System and method of dynamically adjusting virtual machines for a workload

The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

Allocating computing resources for deferrable virtual machines

Methods and systems for detecting systemwide service issues by using anomaly localization. In an example, a method includes receiving time-series monitoring data for multiple services, the time-series monitoring data including multiple dimensions and an error metric; for the monitoring data from each service, evaluating scopes within the monitoring data based on an objective function for a time-series of the error metric to identify at least one anomalous scope, each scope including at least one dimension and a value for the dimension; based on evaluating the scopes, generating a ranked list of scopes for each service based on objective function scores for the scopes; correlating the ranked lists of scopes across the multiple services to identify a cross-service anomaly; and generating an alert for the services based on the cross-service anomaly, the alert indicating at least one scope as a potential root cause for the cross-service anomaly.

Detecting systemwide service issues by using anomaly localization

The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Predicting expansion failures and defragmenting cluster resources

The present disclosure relates to systems, methods, and computer readable media for diagnosing and mitigating memory impact events, such as memory leaks, high memory usage, or other memory issues causing a host node from performing as expected on a cloud computing system. The systems described herein involve receiving locally generated memory usage data from a plurality of host nodes. The systems described herein may aggregate the memory usage data and determine a memory impact diagnosis based on a subset of the aggregated memory usage data. The systems described herein may further apply a mitigation model for mitigating the memory impact event. The systems described herein provide an end-to-end solution for diagnosing and mitigating a variety of memory issues using a dynamic and scalable system that reduces a negative impact of memory leaks and other memory issues on a cloud computing system.

Diagnosing and mitigating memory leak in computing nodes

The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Allocating cloud resources in accordance with predicted deployment growth

A speech communication system for improving speech intelligibility may comprise one or more processors; and a memory storing instructions that, when executed by the one or more processors, cause the system to perform: determining a cutoff frequency based on an estimation of a spectrum of noise, wherein the cutoff frequency defines a noise dominant region of frequency; lifting a spectrum of a speech above the noise dominant region of frequency, wherein a frequency range of the spectrum of the speech increases by the cutoff frequency; and applying an adaptive filter to the speech to achieve echo cancelation, wherein the adaptive filter is controlled by a volume of the noise.

Speech communication system and method for improving speech intelligibility

Growing community of inventors

Beijing, China

Si Qin