公开(公告)号：US20220368758A1

公开(公告)日：2022-11-17

申请号：US17568806

申请日：2022-01-05

Applicant: VMware, Inc.

Inventor： Saurav Suri ,  Sambit Kumar Das ,  Shyam Sundar Govindaraj ,  Sumit Kalra

IPC: H04L67/1017 ,  H04L61/4511

Abstract: Some embodiments provide a method of performing load balancing for a group of machines that are distributed across several physical sites. The method of some embodiments iteratively computes (1) first and second sets of load values respectively for first and second sets of machines that are respectively located at first and second physical sites, and (2) uses the computed first and second sets of load values to distribute received data messages that the group of machines needs to process, among the machines in the first and second physical sites. The iterative computations entail repeated calculations of first and second sets of weight values that are respectively used to combine first and second load metric values for the first and second sets of machines to repeatedly produce the first and second sets of load values for the first and second sets of machines. The repeated calculation of the weight values automatedly and dynamically adjusts the load prediction at each site without user adjustment of these weight values. As it is difficult for a user to gauge the effect of each load metric on the overall load, some embodiments use machine learned technique to automatedly adjust these weight values.

公开(公告)号：US20240007522A1

公开(公告)日：2024-01-04

申请号：US18369809

申请日：2023-09-18

Applicant: VMware, Inc.

Inventor： Saurav Suri ,  Sambit Kumar Das ,  Shyam Sundar Govindaraj ,  Sumit Kalra

IPC: H04L67/1017 ,  H04L61/4511

CPC classification number: H04L67/1017 ,  H04L61/4511

Abstract: Some embodiments provide a method of performing load balancing for a group of machines that are distributed across several physical sites. The method of some embodiments iteratively computes (1) first and second sets of load values respectively for first and second sets of machines that are respectively located at first and second physical sites, and (2) uses the computed first and second sets of load values to distribute received data messages that the group of machines needs to process, among the machines in the first and second physical sites. The iterative computations entail repeated calculations of first and second sets of weight values that are respectively used to combine first and second load metric values for the first and second sets of machines to repeatedly produce the first and second sets of load values for the first and second sets of machines. The repeated calculation of the weight values automatedly and dynamically adjusts the load prediction at each site without user adjustment of these weight values. As it is difficult for a user to gauge the effect of each load metric on the overall load, some embodiments use machine learned technique to automatedly adjust these weight values.

公开(公告)号：US11811861B2

公开(公告)日：2023-11-07

申请号：US17568806

申请日：2022-01-05

Applicant: VMware, Inc.

Inventor： Saurav Suri ,  Sambit Kumar Das ,  Shyam Sundar Govindaraj ,  Sumit Kalra

IPC: H04L67/1017 ,  H04L61/4511

CPC classification number: H04L67/1017 ,  H04L61/4511

Abstract: Some embodiments provide a method of performing load balancing for a group of machines that are distributed across several physical sites. The method of some embodiments iteratively computes (1) first and second sets of load values respectively for first and second sets of machines that are respectively located at first and second physical sites, and (2) uses the computed first and second sets of load values to distribute received data messages that the group of machines needs to process, among the machines in the first and second physical sites. The iterative computations entail repeated calculations of first and second sets of weight values that are respectively used to combine first and second load metric values for the first and second sets of machines to repeatedly produce the first and second sets of load values for the first and second sets of machines. The repeated calculation of the weight values automatedly and dynamically adjusts the load prediction at each site without user adjustment of these weight values. As it is difficult for a user to gauge the effect of each load metric on the overall load, some embodiments use machine learned technique to automatedly adjust these weight values.