Microsoft Technology Licensing, LLC

Nec Corporation

Mahmoud Sayed

Qingwei Lin

Konstantinos Kanonakis

Vivek Gupta

Chetan Bansal

Si Qin

Inigo Goiri Presa

Yawei Yin

Eli Cortez Custodio Vilarinho

Jue Zhang

Yandan Wang

Felipe Vieira Frujeri

Camille Jean Couturier

Fangkai Yang

Bowen Pang

Robert Edward Minneker

Alexander David Fischer

Preston Tapley Stephenson

Soumya Ram

Sidhanth M Panjwani

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

A system and method for management of network traffic flow for one or more networks of interconnected computing devices, including predicting one or more types of network traffic data flows using a Machine Learning (ML) classifier, and updating the ML classifier according to identified changes in the network traffic data flows. Using software-defined-networking (SDN) tools and an updated ML classifier, training data is generated to formulate accurate new predictions of the one or more types of network traffic data flows. Network traffic flow is regulated based on the generated new predictions.

Network traffic flow management using machine learning

Growing community of inventors

Hyattsville, MD, United States of America

Mahmoud Sayed