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：US20250110883A1

公开(公告)日：2025-04-03

申请号：US18477557

申请日：2023-09-29

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Lianjie Cao ,  Zhen Lin ,  Faraz Ahmed ,  Puneet Sharma

IPC: G06F12/0891 ,  G06F12/06

Abstract: In certain embodiments, a computer-implemented method includes: receiving, by a caching system plugin, a request to create a persistent volume for a container application instance; configuring, by the caching system plugin, a local cache volume on a host computing device; configuring, by the caching system plugin, a remote storage volume on a remote storage device; selecting, by a policy manager of the caching system plugin, a cache policy for the container application instance; creating, by the caching system plugin and from a cache manager, a virtual block device associated with the local cache volume, the remote storage volume, and the cache policy; and providing the virtual block device for use by the container application instance as the persistent volume.

公开(公告)号：US20240289180A1

公开(公告)日：2024-08-29

申请号：US18175411

申请日：2023-02-27

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： FARAZ AHMED ,  Lianjie Cao ,  Puneet Sharma

IPC: G06F9/50

CPC classification number: G06F9/5083 ,  G06F9/5016 ,  G06F9/5027

Abstract: Systems and methods are provided for optimizing a serverless workflow. Given a directed acyclic graph (“DAG”) defining functional relationships and a gamma tuning factor to indicate a preference between cost and performance, a serverless workflow corresponding to the DAG may be optimized. The optimization is carried out in accordance with the gamma tuning factor, and is carried out in sub-segments of the DAG called stages. In addition, systems for allowing disparate types of storage media to be utilized by a serverless platform to store data are disclosed. The serverless platforms maintain visibility of the storage media types underlying persistent volumes, and may store data in partitions across disparate types of storage media. For instance, one item of data may be stored partially at a byte addressed storage media and partially at a block addressed storage media.

公开(公告)号：US12001511B2

公开(公告)日：2024-06-04

申请号：US17199294

申请日：2021-03-11

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

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

IPC: G06F18/214 ,  G06F9/50 ,  G06F11/30 ,  G06F11/34 ,  G06F18/2415 ,  G06N3/0464 ,  G06N3/063 ,  G06N3/0985 ,  G06N7/01 ,  G06N20/00 ,  G06V40/16

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：US11698780B2

公开(公告)日：2023-07-11

申请号：US17236884

申请日：2021-04-21

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.