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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：US20210392041A1
公开(公告)日：2021-12-16
申请号：US17304073
申请日：2021-06-14
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Aboubacar Diare
Abstract: Embodiments described herein are generally directed to a creation of an HA private cloud gateway based on a two-node HCI cluster with a self-hosted HMS. According to an example, a request to register a private cloud to be supported by on-premises infrastructure is received by a SaaS portal, which causes a base station to discover servers within the on-premises infrastructure. The base station is then instructed to prepare a server as a deployment node for use in connection with creation of a cluster of two HCI nodes of the servers to represent the HA private cloud gateway, including installing a seed HMS on the deployment node. The base station is further instructed to cause the seed HMS to create the cluster, install a self-hosted HMS within the cluster to manage the cluster, register the cluster to the self-hosted HMS, and finally delete the seed HMS from the deployment node.
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5.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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Patent Agency Ranking
公开(公告)号：US11038752B1
公开(公告)日：2021-06-15
申请号：US16902423
申请日：2020-06-16
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Aboubacar Diare
Abstract: Embodiments described herein are generally directed to a creation of an HA private cloud gateway based on a two-node HCI cluster with a self-hosted HMS. According to an example, a request to register a private cloud to be supported by on-premises infrastructure is received by a SaaS portal, which causes a base station to discover servers within the on-premises infrastructure. The base station is then instructed to prepare a server as a deployment node for use in connection with creation of a cluster of two HCI nodes of the servers to represent the HA private cloud gateway, including installing a seed HMS on the deployment node. The base station is further instructed to cause the seed HMS to create the cluster, install a self-hosted HMS within the cluster to manage the cluster, register the cluster to the self-hosted HMS, and finally delete the seed HMS from the deployment node.
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公开(公告)号：US11561835B2
公开(公告)日：2023-01-24
申请号：US16887001
申请日：2020-05-29
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Jyoti Ranjan , Prabhu Murthy , Paul Murray
Abstract: A system to facilitate a container orchestration cloud service platform is described. The system includes a controller to manage Kubernetes cluster life-cycle operations created by each of a plurality of providers. The controller includes one or more processors to execute a controller micro service to discover a provider plugin associated with each of the plurality of providers, and perform the cluster life-cycle operations at a container orchestration platform as a broker for each of a plurality of providers.
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8.发明申请公开(公告)号：US20210234877A1
公开(公告)日：2021-07-29
申请号：US16775665
申请日：2020-01-29
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Murthy Prabhu , Jyoti Ranjan , Anusha Chaparala
Abstract: Example implementations relate to proactively protecting service endpoints based on deep learning of user location and access patterns. A machine-learning model is trained to recognize anomalies in access patterns relating to endpoints of a cloud-based service by capturing metadata associated with user accesses. The metadata for a given access includes information regarding a particular user that initiated the given access, a particular device utilized, a particular location associated with the given access and specific workloads associated with the given access. An anomaly relating to an access by a user to a service endpoint is identified by monitoring the access patterns and applying the machine-learning model to metadata associated with the access. Based on a degree of risk to the cloud-based service associated with the identified anomaly, a mitigation action is determined. The cloud-based service is proactively protected by programmatically applying the determined mitigation action.
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公开(公告)号：US20210232440A1
公开(公告)日：2021-07-29
申请号：US17073319
申请日：2020-10-17
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Prabhu Murthy , Siddhartha Singh
Abstract: Example techniques for execution of functions by clusters of computing nodes are described. In an example, if a cluster does not have resources available for executing a function for handling a service request, the cluster may request another cluster for executing the function. A result of execution of the function may be received by the cluster and used for handling the service request.
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公开(公告)号：US11748487B2
公开(公告)日：2023-09-05
申请号：US16856702
申请日：2020-04-23
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Jyoti Ranjan , Prabhu Murthy , Ajay Kumar Bajaj
CPC classification number: G06F21/577 , G06F9/547 , G06F11/0766 , G06F11/3636 , G06F11/3664 , G06F11/3688 , G06F21/6227
Abstract: Embodiments described herein are generally directed to testing a microservice to determine whether the microservice leaks sensitive information. According to an example, prior to deployment of a microservice within a production environment, a test suite for the microservice is generated based at least in part on a specification of an application programming interface (API) of the microservice defining operations supported by the API and information regarding parameters of each of the operations. The microservice is subjected to the test suite. A potential security leak by the microservice is then detected by analyzing a dataset to which the microservice outputs information, including applying security rules to the dataset.
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