公开(公告)号：US20220382603A1

公开(公告)日：2022-12-01

申请号：US17819190

申请日：2022-08-11

Applicant: VMware, Inc.

Inventor： YASH BHATNAGAR ,  NAINA VERMA ,  MAGESHWARAN RAJENDRAN ,  AMIT KUMAR ,  VENKATA NAGA MANOHAR KONDAMUDI

IPC: G06F9/50 ,  G06N5/04 ,  H04L41/147 ,  G06N20/00

Abstract: Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

公开(公告)号：US20210342199A1

公开(公告)日：2021-11-04

申请号：US16910115

申请日：2020-06-24

Applicant: VMWARE, INC.

Inventor： YASH BHATNAGAR ,  NAINA VERMA ,  MAGESHWARAN RAJENDRAN ,  AMIT KUMAR ,  VENKATA NAGA MANOHAR KONDAMUDI

IPC: G06F9/50 ,  G06N20/00 ,  G06N5/04 ,  H04L12/24

Abstract: Disclosed are various embodiments for generating recommended replacement host machines for a datacenter. The recommendations can be generated based upon an analysis of historical workload usage across the datacenter. Clusters can be generated that cluster workloads together that are similar. Purchase plans can be generated based upon the identified clusters and benchmark data regarding servers.

公开(公告)号：US20230230005A1

公开(公告)日：2023-07-20

申请号：US17699214

申请日：2022-03-21

Applicant: VMWARE, INC.

Inventor： YASH BHATNAGAR ,  MAGESHWARAN RAJENDRAN ,  KEERTHANAA K ,  GURU RAJ VAISHNAV AKUTHOTA ,  NEERAJ MENON S

IPC: G06Q10/06 ,  G06Q30/02 ,  G06N5/04 ,  G06N5/02

CPC classification number: G06Q10/06315 ,  G06Q10/06375 ,  G06Q30/0215 ,  G06N5/04 ,  G06N5/025

Abstract: In an example, a cloud service management node includes a knowledge base having a plurality of billing rules for a cloud computing environment, a processor, and a memory coupled to the processor. The memory may include a discount predictor module to receive an actual bill related to consumption of a cloud service in the cloud computing environment. Further, the discount predictor module may determine a variation between the actual bill and an expected cost from a public rate card by comparing the actual bill with the expected cost. Furthermore, the discount predictor module may evaluate the plurality of billing rules to predict a discount type and a discount associated with the discount type that matches the variation between the actual bill and the expected cost from the public rate card. Further, the discount predictor module may output the discount type and the discount on an interactive user interface.

公开(公告)号：US20230179485A1

公开(公告)日：2023-06-08

申请号：US18160464

申请日：2023-01-27

Applicant: VMWARE, INC.

Inventor： YASH BHATNAGAR ,  HEMANI KATYAL ,  CHANDRASHEKHAR JHA ,  MAGESHWARAN RAJENDRAN ,  RITESH JHA

IPC: H04L41/0893 ,  G06F9/50 ,  G06F11/34

CPC classification number: H04L41/0893 ,  G06F9/5077 ,  G06F11/34 ,  G06F9/5038 ,  G06F9/5083

Abstract: An example system includes memory, programmable circuitry, and machine readable instructions to program the programmable circuitry to: obtain utilization metric information corresponding to utilization metrics collected over a time interval, the utilization metrics corresponding to allocated resources utilized by containers, the containers associated with a cluster, obtain a request to generate priority classes for the containers in the cluster, the priority classes indicative of which containers have a greater priority in the cluster, and generate the priority classes for the containers based on the utilization metric information and a count of network interactions corresponding to the containers for the time interval.

公开(公告)号：US20210288882A1

公开(公告)日：2021-09-16

申请号：US17332771

申请日：2021-05-27

Applicant: VMWARE, INC.

Inventor： YASH BHATNAGAR ,  HEMANI KATYAL ,  CHANDRASHEKHAR JHA ,  MAGESHWARAN RAJENDRAN ,  RITESH JHA

IPC: H04L12/24 ,  G06F9/50 ,  G06F11/34

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.