Methods and apparatus to determine container priorities in virtualized computing environments

    公开(公告)号:US11575576B2

    公开(公告)日:2023-02-07

    申请号:US17332771

    申请日:2021-05-27

    Applicant: VMWARE, INC.

    Abstract: An example apparatus includes memory, and at least one processor to execute instructions to assign first containers to a first cluster and second containers to a second cluster based on the first containers including first allocated resources that satisfy a first threshold number of allocated resources and the second containers including second allocated resources that satisfy a second threshold number of allocated resources, determine a representative interaction count value for a first one of the first containers, the representative interaction count value based on a first network interaction metric corresponding to an interaction between the first one of the first containers and a combination of at least one of the first containers and at least one of the second containers, and generate a priority class for the first one of the first containers based on the representative interaction count value.

    Systems and methods for recommending optimized virtual-machine configurations

    公开(公告)号:US11216295B2

    公开(公告)日:2022-01-04

    申请号:US16368901

    申请日:2019-03-29

    Applicant: VMWARE, INC.

    Abstract: An example method is provided for recommending VM configurations, including one or more servers upon which one or more VMs can run. A user wishing to run these VMs can request a recommendation for an appropriate server or set of servers. The user can indicate a category corresponding to the type of workload that pertains to the VMs. The system can receive the request and identify a pool of servers available to the user. Using industry specifications and benchmarks, the system can classify the available servers into multiple categories. Within those categories, similar servers can be clustered and then ranked based on their levels of optimization. The sorted results can be displayed to the user, who can select a particular server (or group of servers) and customize the deployment as needed. This process allows a user to identify and select an optimized setup quickly and accurately.

    SYSTEMS AND METHODS FOR OPTIMIZING THE NUMBER OF SERVERS IN A CLUSTER

    公开(公告)号:US20210297316A1

    公开(公告)日:2021-09-23

    申请号:US17343085

    申请日:2021-06-09

    Applicant: VMWARE, INC.

    Abstract: Examples described herein include systems and methods for optimizing the number of servers in a cluster. In one example, a number of application servers, a number of backend servers, and a first disk throughput of a backend server to be included in the cluster are determined. The first disk throughput is determined based on the storage capacity of the backend server and a first round trip time. Example systems and method can also include validating the number of application servers based on a cluster throughput and one of a network interface card bandwidth of an application server to be included in the cluster and a load bearing capacity of the application server. The systems and methods can further include determining a second disk throughput of the backend server and increasing the number of backend servers if the second disk throughput is less than the second disk throughput.

    Workload tenure prediction for capacity planning

    公开(公告)号:US11562299B2

    公开(公告)日:2023-01-24

    申请号:US16444190

    申请日:2019-06-18

    Applicant: VMware, Inc.

    Abstract: Disclosed are various embodiments for automating the prediction of workload tenures in datacenter environments. In some embodiments, parameters are identified for a plurality of workloads of a software defined data center. A machine learning model is trained to determine a predicted tenure based on parameters of the workloads. A workload for the software defined data center is configured to include at least one workload parameter. The workload is processed using the trained machine learning model to determine the predicted tenure. An input to the machine learning model includes the at least one workload parameter.

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