公开(公告)号：US20240045715A1

公开(公告)日：2024-02-08

申请号：US18229615

申请日：2023-08-02

Applicant: Cisco Technology, Inc.

Inventor： Rohit Bahl ,  Stephen Williams ,  Debashish Ghosh

IPC: G06F9/48 ,  G06F9/50 ,  G06N3/126

CPC classification number: G06F9/4881 ,  G06F9/5038 ,  G06N3/126 ,  G06N3/086

Abstract: Various techniques are used to schedule computing jobs for execution by a computing resource. In an example method, a schedule is generated by selecting, for a first slot in the schedule, a first computing job based on a first priority of the first computing job with respect to a first characteristic. A second computing job is selected for a second slot in the schedule based on a second priority of the second computing job with respect to a second characteristic. The second slot occurs after the first slot in the schedule, and the second characteristic is different than the first characteristic. The first characteristic or the second characteristic includes an execution frequency. The computing jobs are executed based on the schedule.

公开(公告)号：US11635995B2

公开(公告)日：2023-04-25

申请号：US16513510

申请日：2019-07-16

Applicant: Cisco Technology, Inc.

Inventor： Rohit Bahl ,  Paul Clyde Sherrill ,  Stephen Joseph Williams

IPC: G06F9/50 ,  G06F9/38 ,  G06N20/00 ,  G06F9/455

Abstract: A multi-cloud service mesh orchestration platform can receive a request to deploy an application as a service mesh application. The platform can tag the application with governance information (e.g., TCO, SLA, provisioning, deployment, and operational criteria). The platform can partition the application into its constituent components, and tag each component with individual governance information. For first time steps, the platform can select and perform a first set of actions for deploying each component to obtain individual rewards, state transitions, and expected returns. The platform can determine a reinforcement learning policy for each component that maximizes a total reward for the application based on the individual rewards, state transitions, and expected returns of each first set of actions selected and performed for each component. For second time steps, the platform can select and perform a second set of actions for each component based on the reinforcement learning policy for the component.

公开(公告)号：US11570271B2

公开(公告)日：2023-01-31

申请号：US16380872

申请日：2019-04-10

Applicant: Cisco Technology, Inc.

Inventor： Rohit Bahl ,  Ramesh Yeevani-Srinivas

IPC: H04L67/567 ,  G06N20/00 ,  H04L67/10

Abstract: Differentiated sidecars in a service mesh may be provided. A first routing rule includes a first plurality of weights to be associated with a first plurality of data paths of a first microservice instance may be received. Next, first mapping between a first set of features associated with the first microservice instance and the first plurality of weights may be determined. Then a second microservice instance may be detected and a second set of features associated with the second microservice instance may be detected. A second routing rule comprising a second plurality of weights to be associated with a second plurality of data paths of the second microservice instance may be determined. The second plurality of weights may be determined such that a second mapping between the second set of features and the second plurality of weights imitates the first mapping.

公开(公告)号：US20170366611A1

公开(公告)日：2017-12-21

申请号：US15187594

申请日：2016-06-20

Applicant: CISCO TECHNOLOGY, INC.

Inventor： Rohit Bahl ,  Stephen Williams ,  Harsh Parandekar

IPC: H04L29/08 ,  H04L12/24 ,  H04L12/26 ,  G06N99/00

CPC classification number: H04L67/1097 ,  G06N20/00 ,  H04L41/16 ,  H04L43/0852 ,  H04L43/16 ,  H04L67/325

Abstract: In one embodiment, a method includes receiving at a network device comprising a data transfer optimization module, input identifying a source directory comprising data and a target directory at a storage device, splitting the data into a plurality of data groups for transfer to the storage device, transmitting the data groups concurrently to the storage device on data paths wherein transfer times of the data groups are monitored, receiving at the data transfer optimization module, identification of a data group with a longest transfer time, splitting the data group with the longest transfer time, and placing the data from the data group into at least two of the data groups for transfer to the storage device. An apparatus and logic are also disclosed herein.