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1.发明授权公开(公告)号：US11561842B2
公开(公告)日：2023-01-24
申请号：US16779358
申请日：2020-01-31
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Prabhu Murthy , Jyoti Ranjan , Abhishek Kumar
Abstract: Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.
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公开(公告)号：US20210279111A1
公开(公告)日：2021-09-09
申请号：US16811313
申请日：2020-03-06
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Ajay Kumar Bajaj , Abhishek Kumar
Abstract: Example implementations relate to a upgrade of a host that hosts application units of a container-based application. According to an example, monitoring is performed to identify new system software component availability for the cluster. When a new system software component is available, a historical workload pattern of the cluster is analyzed to identify an upgrade window for each host of the cluster. When the upgrade window arrives for a host, it is determined whether reconfiguration of an application is to be performed based on a capacity of the cluster. When the determination is affirmative, a reconfiguration option for the application is identified and a configuration of the application is adjusted accordingly. The host may then be drained, removed from the cluster, upgraded, added back into the cluster and any application configuration changes can be reversed.
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公开(公告)号：US10915349B2
公开(公告)日：2021-02-09
申请号：US15959697
申请日：2018-04-23
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Unmesh Gurjar , Abhishek Kumar , Anudeep Chandra Thatipalli , Ajay Kumar Bajaj
Abstract: In some examples, a method includes: (a) reading a manifest file containing information regarding an application running on one or more Virtual Machines (VMs), wherein the information includes application topology, credentials, and configuration details; (b) receiving instructions to re-deploy the application from the one or more VMs to a container environment; (c) discovering, based on information in the manifest file, application consumption attributes including attributes of storage, computer, and network resources consumed by a workload of the application; (d) deploying the application on the container environment to produce a containerized application; (e) copying configuration details from the manifest file to the containerized application; (f) migrating, based on information in the manifest file and the discovered application consumption attributes, stateful data to the containerized application; and (g) validating the containerized application functionality.
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公开(公告)号：US11483384B2
公开(公告)日：2022-10-25
申请号：US16820557
申请日：2020-03-16
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Jyoti Ranjan , Abhishek Kumar , Unmesh Gurjar , Ajay Kumar Bajaj
IPC: H04L29/08 , H04L12/24 , H04L67/1095 , H04L41/14
Abstract: In some examples, a system may include a processing resource and a memory resource. The memory resource may store machine-readable instructions to cause the processing resource to create a migration plan, defining characteristics of a migration of an application from a native computing data center to a computing cloud of a plurality of distinct candidate computing clouds, based on: a first migration constraint for the application determined from an analysis of historical behavior of the application executed on the native computing data center; a second migration constraint for the application determined from an analysis of administrator cloud migration preferences for the application; and a cloud computing characteristic of each of the plurality of distinct candidate computing clouds.
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公开(公告)号：US20210240459A1
公开(公告)日：2021-08-05
申请号：US17073323
申请日：2020-10-17
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Prabhu Murthy , Abhishek Kumar
Abstract: Example techniques for selection of deployment environments for applications are described. The deployment environments may be container-based deployment environments. In an example, the selection may be performed based on a historical behavior of an application.
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Patent Agency Ranking
公开(公告)号：US20200304383A1
公开(公告)日：2020-09-24
申请号：US16357742
申请日：2019-03-19
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Ajay Kumar Bajaj , Prabhu Murthy , Abhishek Kumar
IPC: H04L12/24
Abstract: Examples of generation of templates for cloud computing platforms are disclosed. In an example, a plurality of service provider specific parameters corresponding to a selected parameter is identified. Each of the plurality of service provider specific parameters is associated with a respective cloud service provider. A plurality of service provider specific templates for deployment and management of the computing entity over the plurality of cloud computing platforms is generated, based on the plurality of service provider specific parameters. Each of the plurality of service provider specific templates includes a corresponding service provider specific parameter, from the plurality of service provider specific parameters.
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7.发明申请公开(公告)号：US20210240539A1
公开(公告)日：2021-08-05
申请号：US16779358
申请日：2020-01-31
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Prabhu Murthy , Jyoti Ranjan , Abhishek Kumar
Abstract: Example implementations relate to determining and implementing a feasible resource optimization plan for public cloud consumption. Telemetry data over a period of time is obtained for a current deployment of virtual infrastructure resources within a current data center of a cloud provider that supports an existing service and an application deployed on the virtual infrastructure resources. Information regarding a set of constraints to be imposed on a resource optimization plan is obtained. Indicators of resource consumption relating to the currently deployed virtual infrastructure resources during the period of time are identified by applying a deep learning algorithm to the telemetry data. A resource optimization plan is determined that is feasible within the set of constraints based on a costing model associated with resources of an alternative data center of the cloud provider, the indicators of resource consumption and costs associated with the current deployment.
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公开(公告)号：US10897509B2
公开(公告)日：2021-01-19
申请号：US16240726
申请日：2019-01-05
Applicant: Hewlett Packard Enterprise Development LP
Abstract: The present disclosure discloses a method and network device for dynamic detection of inactive virtual private network clients. Specifically, a network device receives periodic messages from a first device at a first interval, and determines a timeout value for the first device based at least on the first interval, at which the periodic messages are received from the first device. Subsequent to determining the timeout value, the network device detects that a message has not been received from the first device for a period of time corresponding to the timeout value for the first device. The network device then terminates at least one connection with the first device responsive to determining that no message has been received from the first device for the period of time corresponding to the timeout value for the first device.
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公开(公告)号：US20200304571A1
公开(公告)日：2020-09-24
申请号：US16820557
申请日：2020-03-16
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Jyoti Ranjan , Abhishek Kumar , Unmesh Gurjar , Ajay Kumar Bajaj
Abstract: In some examples, a system may include a processing resource and a memory resource. The memory resource may store machine-readable instructions to cause the processing resource to create a migration plan, defining characteristics of a migration of an application from a native computing data center to a computing cloud of a plurality of distinct candidate computing clouds, based on: a first migration constraint for the application determined from an analysis of historical behavior of the application executed on the native computing data center; a second migration constraint for the application determined from an analysis of administrator cloud migration preferences for the application; and a cloud computing characteristic of each of the plurality of distinct candidate computing clouds.
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公开(公告)号：US20190324786A1
公开(公告)日：2019-10-24
申请号：US15959697
申请日：2018-04-23
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Unmesh Gurjar , Abhishek Kumar , Anudeep Chandra Thatipalli , Ajay Kumar Bajaj
Abstract: In some examples, a method includes: (a) reading a manifest file containing information regarding an application running on one or more Virtual Machines (VMs), wherein the information includes application topology, credentials, and configuration details; (b) receiving instructions to re-deploy the application from the one or more VMs to a container environment; (c) discovering, based on information in the manifest file, application consumption attributes including attributes of storage, computer, and network resources consumed by a workload of the application; (d) deploying the application on the container environment to produce a containerized application; (e) copying configuration details from the manifest file to the containerized application; (f) migrating, based on information in the manifest file and the discovered application consumption attributes, stateful data to the containerized application; and (g) validating the containerized application functionality.
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