公开(公告)号：US11914982B2

公开(公告)日：2024-02-27

申请号：US18328287

申请日：2023-06-02

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Lianjie Cao ,  Anu Mercian ,  Diman Zad Tootaghaj ,  Faraz Ahmed ,  Puneet Sharma

IPC: G06F8/60 ,  G06F8/61 ,  G06F40/30 ,  H04L67/12

CPC classification number: G06F8/61 ,  G06F40/30 ,  H04L67/12

Abstract: Embodiments described herein are generally directed to an edge-CaaS (eCaaS) framework for providing life-cycle management of containerized applications on the edge. According to an example, declarative intents are received indicative of a use case for which a cluster of a container orchestration platform is to be deployed within an edge site that is to be created based on infrastructure associated with a private network. A deployment template is created by performing intent translation on the declarative intents and based on a set of constraints. The deployment template identifies the container orchestration platform selected by the intent translation. The deployment template is then executed to deploy and configure the edge site, including provisioning and configuring the infrastructure, installing the container orchestration platform on the infrastructure, configuring the cluster within the container orchestration platform, and deploying a containerized application or portion thereof on the cluster.

公开(公告)号：US20240004710A1

公开(公告)日：2024-01-04

申请号：US18469695

申请日：2023-09-19

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Lianjie Cao ,  Faraz Ahmed ,  Puneet Sharma

IPC: G06F9/50 ,  G06F9/30 ,  G06N20/00 ,  G06F11/34 ,  G06F18/214 ,  G06F18/2415

CPC classification number: G06F9/5005 ,  G06F9/505 ,  G06F9/5011 ,  G06F18/24155 ,  G06N20/00 ,  G06F11/3409 ,  G06F18/214 ,  G06F9/3009

Abstract: Systems and methods are provided for optimally allocating resources used to perform multiple tasks/jobs, e.g., machine learning training jobs. The possible resource configurations or candidates that can be used to perform such jobs are generated. A first batch of training jobs can be randomly selected and run using one of the possible resource configuration candidates. Subsequent batches of training jobs may be performed using other resource configuration candidates that have been selected using an optimization process, e.g., Bayesian optimization. Upon reaching a stopping criterion, the resource configuration resulting in a desired optimization metric, e.g., fastest job completion time can be selected and used to execute the remaining training jobs.

公开(公告)号：US11010205B2

公开(公告)日：2021-05-18

申请号：US15608248

申请日：2017-05-30

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Puneet Sharma ,  Lianjie Cao ,  Vinay Saxena

IPC: G06F15/173 ,  G06F9/50 ,  H04L12/24 ,  H04L12/725 ,  H04L12/911 ,  H04L12/713 ,  G06F9/455 ,  H04L29/08

Abstract: Examples allocating resources to virtual network functions (VNFs). Some examples include monitoring information associated with a set of VNFs that includes a set of VNF instances. A resource allocation event may be predicted for a VNF instance based on the monitored information and a resource flexing model that is developed using a capacity metric of the VNF instance. A resource flexing plan may be generated based on the resource allocation event and an order of the set of VNFs in a service function chain.

公开(公告)号：US12141608B2

公开(公告)日：2024-11-12

申请号：US18469695

申请日：2023-09-19

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Lianjie Cao ,  Faraz Ahmed ,  Puneet Sharma

IPC: G06F9/50 ,  G06F9/30 ,  G06F11/34 ,  G06F18/214 ,  G06F18/2415 ,  G06N20/00

Abstract: Systems and methods are provided for optimally allocating resources used to perform multiple tasks/jobs, e.g., machine learning training jobs. The possible resource configurations or candidates that can be used to perform such jobs are generated. A first batch of training jobs can be randomly selected and run using one of the possible resource configuration candidates. Subsequent batches of training jobs may be performed using other resource configuration candidates that have been selected using an optimization process, e.g., Bayesian optimization. Upon reaching a stopping criterion, the resource configuration resulting in a desired optimization metric, e.g., fastest job completion time can be selected and used to execute the remaining training jobs.

公开(公告)号：US20240289421A1

公开(公告)日：2024-08-29

申请号：US18654953

申请日：2024-05-03

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Lianjie Cao ,  Faraz Ahmed ,  Puneet Sharma ,  Ali Tariq

IPC: G06F18/214 ,  G06F9/50 ,  G06F11/34 ,  G06F18/2415 ,  G06N20/00

CPC classification number: G06F18/214 ,  G06F9/5022 ,  G06F9/5027 ,  G06F9/505 ,  G06F9/5061 ,  G06F11/3414 ,  G06F18/24155 ,  G06N20/00

Abstract: Systems and methods can be configured to determine a plurality of computing resource configurations used to perform machine learning model training jobs. A computing resource configuration can comprise: a first tuple including numbers of worker nodes and parameter server nodes, and a second tuple including resource allocations for the worker nodes and parameter server nodes. At least one machine learning training job can be executed using a first computing resource configuration having a first set of values associated with the first tuple. During the executing the machine learning training job: resource usage of the worker nodes and parameter server nodes caused by a second set of values associated with the second tuple can be monitored, and whether to adjust the second set of values can be determined. Whether a stopping criterion is satisfied can be determined. One of the plurality of computing resource configurations can be selected.

公开(公告)号：US20180121222A1

公开(公告)日：2018-05-03

申请号：US15339574

申请日：2016-10-31

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Puneet Sharma ,  Lianjie Cao ,  Vinay Saxena ,  Vasu Sasikanth Sankhavaram ,  Badrinath Natarajan

IPC: G06F9/455 ,  G06F9/50

CPC classification number: G06F9/45558 ,  G06F9/5077 ,  G06F2009/45562 ,  G06F2009/4557 ,  G06F2009/45595

Abstract: Examples relate to determining virtual network function configurations. In one example, a computing device may receive a virtual network function specifying a particular function to be performed by at least one virtual machine; identify a particular performance metric for the virtual network function; determine, using the particular performance metric and a default resource configuration, a first infrastructure configuration specifying a value for each of a plurality of infrastructure options, each of the plurality of infrastructure options specifying a feature of the at least one virtual machine; and determine, using the particular performance metric and the first infrastructure configuration, a first resource configuration specifying a value for each of a plurality of virtualized hardware resources for the at least one virtual machine.

公开(公告)号：US12132668B2

公开(公告)日：2024-10-29

申请号：US18311430

申请日：2023-05-03

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Ali Tariq ,  Lianjie Cao ,  Faraz Ahmed ,  Puneet Sharma

IPC: H04L47/78 ,  H04L43/0882 ,  H04L43/16 ,  H04L47/70 ,  H04L47/762 ,  H04L47/80

CPC classification number: H04L47/803 ,  H04L43/0882 ,  H04L43/16 ,  H04L47/762 ,  H04L47/781 ,  H04L47/822

Abstract: Systems and methods are provided for updating resource allocation in a distributed network. For example, the method may comprise allocating a plurality of resource containers in a distributed network in accordance with a first distributed resource configuration. Upon determining that a processing workload value exceeds a stabilization threshold of the distributed network, determining a resource efficiency value of the plurality of resource containers in the distributed network. When a resource efficiency value is greater than or equal to the threshold resource efficiency value, the method may generate a second distributed resource configuration that includes a resource upscaling process, or when the resource efficiency value is less than the threshold resource efficiency value, the method may generate the second distributed resource configuration that includes a resource outscaling process. The resource allocation may transmit the second to update the resource allocation.

公开(公告)号：US20230325166A1

公开(公告)日：2023-10-12

申请号：US18328287

申请日：2023-06-02

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Lianjie Cao ,  Anu Mercian ,  Diman Zad Tootaghaj ,  Faraz Ahmed ,  Puneet Sharma

IPC: G06F8/61 ,  G06F40/30 ,  H04L67/12

CPC classification number: G06F8/61 ,  H04L67/12 ,  G06F40/30

Abstract: Embodiments described herein are generally directed to an edge-CaaS (eCaaS) framework for providing life-cycle management of containerized applications on the edge. According to an example, declarative intents are received indicative of a use case for which a cluster of a container orchestration platform is to be deployed within an edge site that is to be created based on infrastructure associated with a private network. A deployment template is created by performing intent translation on the declarative intents and based on a set of constraints. The deployment template identifies the container orchestration platform selected by the intent translation. The deployment template is then executed to deploy and configure the edge site, including provisioning and configuring the infrastructure, installing the container orchestration platform on the infrastructure, configuring the cluster within the container orchestration platform, and deploying a containerized application or portion thereof on the cluster.

公开(公告)号：US20230123074A1

公开(公告)日：2023-04-20

申请号：US17503232

申请日：2021-10-15

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Faraz Ahmed ,  Lianjie Cao ,  Puneet Sharma

IPC: H04W24/02 ,  H04W24/10 ,  H04W40/12 ,  H04W48/18 ,  H04W4/50 ,  G06N7/00 ,  G06N20/00

Abstract: Systems, methods, and computer-readable media are described for employing a machine learning-based approach such as adaptive Bayesian optimization to learn over time the most optimized assignments of incoming network requests to service function chains (SFCs) created within network slices of a 5G network. An optimized SFC assignment may be an assignment that minimizes an unknown objective function for a given set of incoming network service requests. For example, an optimized SFC assignment may be one that minimizes request response time or one that maximizes throughput for one or more network service requests corresponding to one or more network service types. The optimized SFC for a network request of a given network service type may change over time based on the dynamic nature of network performance. The machine-learning based approaches described herein train a model to dynamically determine optimized SFC assignments based on the dynamically changing network conditions.

公开(公告)号：US20230071281A1

公开(公告)日：2023-03-09

申请号：US17468517

申请日：2021-09-07

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： ALI TARIQ ,  Lianjie Cao ,  Faraz Ahmed ,  Puneet Sharma

IPC: H04L12/927 ,  H04L12/911 ,  H04L12/923 ,  H04L12/26

Abstract: Systems and methods are provided for updating resource allocation in a distributed network. For example, the method may comprise allocating a plurality of resource containers in a distributed network in accordance with a first distributed resource configuration. Upon determining that a processing workload value exceeds a stabilization threshold of the distributed network, determining a resource efficiency value of the plurality of resource containers in the distributed network. When a resource efficiency value is greater than or equal to the threshold resource efficiency value, the method may generate a second distributed resource configuration that includes a resource upscaling process, or when the resource efficiency value is less than the threshold resource efficiency value, the method may generate the second distributed resource configuration that includes a resource outscaling process. The resource allocation may transmit the second to update the resource allocation.