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公开(公告)号：US11593210B2
公开(公告)日：2023-02-28
申请号：US17136563
申请日：2020-12-29
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Diman Zad Tootaghaj , Puneet Sharma , Faraz Ahmed , Michael Zayats
Abstract: Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.
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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.
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13.发明公开公开(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
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.
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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
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.
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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.
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Patent Agency Ranking
公开(公告)号：US20220206900A1
公开(公告)日：2022-06-30
申请号：US17136563
申请日：2020-12-29
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Diman Zad Tootaghaj , Puneet Sharma , Faraz Ahmed , Michael Zayats
Abstract: Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.
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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
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.
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公开(公告)号：US11983074B2
公开(公告)日：2024-05-14
申请号：US18175091
申请日：2023-02-27
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Diman Zad Tootaghaj , Puneet Sharma , Faraz Ahmed , Michael Zayats
IPC: G06F11/30 , G06F9/50 , G06F11/14 , G06F18/23213 , G06F11/18
CPC classification number: G06F11/1425 , G06F9/5072 , G06F9/5077 , G06F9/5083 , G06F18/23213 , G06F11/187 , G06F2209/505 , G06F2209/508
Abstract: Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.
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19.发明授权公开(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.
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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.
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