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.

公开(公告)号：US11665106B2

公开(公告)日：2023-05-30

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

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：US12133095B2

公开(公告)日：2024-10-29

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 ,  G06N7/01 ,  G06N20/00 ,  H04W4/50 ,  H04W24/10 ,  H04W40/12 ,  H04W48/18

CPC classification number: H04W24/02 ,  G06N7/01 ,  G06N20/00 ,  H04W4/50 ,  H04W24/10 ,  H04W40/12 ,  H04W48/18

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：US11797340B2

公开(公告)日：2023-10-24

申请号：US16874479

申请日：2020-05-14

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 ,  G06F9/3009 ,  G06F9/505 ,  G06F9/5011 ,  G06F11/3409 ,  G06F18/214 ,  G06F18/24155 ,  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：US20230275848A1

公开(公告)日：2023-08-31

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

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

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：US20220342649A1

公开(公告)日：2022-10-27

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: G06F8/61 ,  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：US10827020B1

公开(公告)日：2020-11-03

申请号：US16592560

申请日：2019-10-03

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Lianjie Cao ,  Puneet Sharma

IPC: G06F13/00 ,  H04L29/08 ,  H04L12/26 ,  H04L29/06

Abstract: Example implementations relate to assigning microservices to cluster nodes. A sidecar proxy may be deployed at a data plane of a distributed service. The sidecar proxy may monitor telemetry data between microservices of the distributed service. A communication pattern may be determined from the telemetry data of the distributed service. Each microservice of the distributed service may be assigned to a cluster node based on the communication pattern.