Machine learning system for workload failover in a converged infrastructure
AssigneeVMware, Inc.
InventorsAalap Desai, Anant Agarwal, Alaa Shaabana, Ravi Cherukupalli, Sourav Kumar, Vikram Nair
Life of this patent
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Abstract
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.
Claims20
A method, comprising:
- identifying, by at least one computing device, a cluster of virtual machines executed in computing environment;
- performing, by the at least one computing device, a plurality of simulations for the cluster of virtual machines, the plurality of simulations simulating a failure of one or more hosts in the computing environment, the plurality of simulations further simulating an effect on the cluster of virtual machines as a result of the failure;
- generating, by the at least one computing device, a score for respective ones of the simulations, the score representing the effect on the cluster of virtual machines;
- performing, by the at least one computing device, a clustering process on one of the simulations based upon on the score, the clustering process being trained using data from at least one other deployment;
- and
- identifying, by the at least one computing device, based on the clustering process, a most similar deployment to the cluster of virtual machines within the computing environment.
A system comprising:
- at least one computing device;
- an application executed by the at least one computing device, the application causing the at least one computing device to at least:
- identify a cluster of virtual machines executed in computing environment;
- perform a plurality of simulations for the cluster of virtual machines simulating a failure of one or more hosts in the computing environment;
- generate a score for respective ones of the simulations representing the effect on the cluster of virtual machines;
- perform a clustering process on one of the simulations based upon on the score;
- and
- identify, based on the clustering process, a most similar deployment to the cluster of virtual machines within the computing environment.
A non-transitory computer-readable medium embodying a program executed by at least one computing device, the program causing the at least one computing device to at least:
- identifying, by at least one computing device, a cluster of virtual machines executed in computing environment;
- performing, by the at least one computing device, a plurality of simulations for the cluster of virtual machines, the plurality of simulations simulating a failure of one or more hosts in the computing environment, the plurality of simulations further simulating an effect on the cluster of virtual machines as a result of the failure;
- generating, by the at least one computing device, a score for respective ones of the simulations, the score representing the effect on the cluster of virtual machines;
- performing, by the at least one computing device, a clustering process on one of the simulations based upon on the score, the clustering process being trained using data from at least one other deployment;
- and
- identifying, by the at least one computing device, based on the clustering process, a most similar deployment to the cluster of virtual machines within the computing environment.
Dependencies are inferred from the claim text.