Vmware, Inc.

Alaa Shaabana

Anant Agarwal

Aalap Desai

Ravi Kant Cherukupalli

Vikram Nair

Sourav Kumar

Pawan Saxena

Rahul Chandrasekaran

Gregory Jean-Baptiste

Systems and methods for analyzing the usage of a set of workloads in a hyper-converged infrastructure are disclosed. A neural network model is trained based upon historical usage data of the set of workloads. The neural network model can make usage predictions of future demands on the set of workloads to minimize over-allocation or under-allocation of resources to the workloads.

Neural network model for predicting usage in a hyper-converged infrastructure

Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.

Machine learning system for workload failover in a converged infrastructure

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Palo Alto, CA, United States of America

Alaa Shaabana