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公开(公告)号：US20220229707A1
公开(公告)日：2022-07-21
申请号：US17248315
申请日：2021-01-20
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Klaus-Dieter Lange , Nishant Rawtani , Supriya Kamthania
IPC: G06F9/50
Abstract: Examples described herein relate to a management node and a method for managing migration of workload resources. The management node may assign a capability tag to each of a plurality of member nodes hosting workload resources. Further, the management node may determine a resource requirement classification of each workload resource of the workload resources based on analysis of runtime performance data of each workload resource. Furthermore, the management node may determine a temporal usage pattern classification of each workload resource. Moreover, the management node may determine a migration plan for a candidate workload resource of the workload resources based on the capability tag of each of the plurality of member nodes, the resource requirement classification and the temporal usage pattern classification of each workload resource.
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Patent Agency Ranking
公开(公告)号：US20210365302A1
公开(公告)日：2021-11-25
申请号：US16878238
申请日：2020-05-19
Applicant: Hewlett Packard Enterprise Development LP
Abstract: An Adaptive and Distributed Tuning System (ADTS) includes a distributed framework for full-stack performance tuning of workloads. Given a large search space, the framework leverages domain-specific contextual information, in the form of probabilistic models of the system behavior, to make informed decisions about which configurations to evaluate and, in turn, distribute across multiple nodes to converge rapidly to best possible configurations.
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公开(公告)号：US11755955B2
公开(公告)日：2023-09-12
申请号：US17225897
申请日：2021-04-08
Applicant: Hewlett Packard Enterprise Development LP
Inventor： Klaus-Dieter Lange , Mukund Kumar , Prateek Bhatnagar , Nalamati Sai Rajesh , Nishant Rawtani , Craig Allan Estepp
IPC: G06N20/00 , G06F11/30 , G06F11/32 , G06F11/34 , G06F18/214
CPC classification number: G06N20/00 , G06F11/3006 , G06F11/3075 , G06F11/327 , G06F11/3428 , G06F18/214
Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
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公开(公告)号：US11647037B2
公开(公告)日：2023-05-09
申请号：US16776881
申请日：2020-01-30
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Suhas Shivanna , Supriya Kamthania , Nishant Rawtani , Anoop Chandra Bidugalu Nagesh , Ragashree Mysuru Chandrashekar
CPC classification number: H04L63/1433 , G06F21/577 , H04L63/1416 , G06F2221/034
Abstract: In some examples, a system receives information traffic communicated over a network by or with a system under test (SUT), and analyzes the information traffic to identify a potential attack point in the SUT and a technology used by the SUT. The system determines a collection of penetration tests for testing a stack comprising a plurality of layers associated with the SUT based on the identified potential attack point and the identified technology, and further based on a dynamic knowledge base that includes information relating to vulnerabilities and threats.
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公开(公告)号：US20230376855A1
公开(公告)日：2023-11-23
申请号：US18361511
申请日：2023-07-28
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Klaus-Dieter Lange , Mukund Kumar , Prateek Bhatnagar , Nalamati Sai Rajesh , Nishant Rawtani , Craig Allan Estepp
IPC: G06N20/00 , G06F11/34 , G06F11/30 , G06F11/32 , G06F18/214
CPC classification number: G06N20/00 , G06F11/3428 , G06F11/3075 , G06F11/327 , G06F11/3006 , G06F18/214
Abstract: Systems and methods are provided for detecting anomalies on multiple layers of a computer system, such as a compute server. For example, the system can detect anomalies from the lower firmware layer up to the upper application layer of the compute server. The system collects train data from the computer system that is under testing. The train data includes features that affect performance metrics, as defined by a selected benchmark. This train data is used in training machine learning (ML) models. The ML models create a train snapshot corresponding to the selected benchmark. Additionally with every new release, a test snapshot can be created corresponding to the selected benchmark or workload. The system can detect an anomaly based on the train snapshot and the test snapshot. Also, the system can recommend tunings for a best set of features based upon data collected over generations of compute server.
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公开(公告)号：US20210243216A1
公开(公告)日：2021-08-05
申请号：US16776881
申请日：2020-01-30
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor： Suhas Shivanna , Supriya Kamthania , Nishant Rawtani , Anoop Chandra Bidugalu Nagesh , Ragashree Mysuru Chandrashekar
Abstract: In some examples, a system receives information traffic communicated over a network by or with a system under test (SUT), and analyzes the information traffic to identify a potential attack point in the SUT and a technology used by the SUT. The system determines a collection of penetration tests for testing a stack comprising a plurality of layers associated with the SUT based on the identified potential attack point and the identified technology, and further based on a dynamic knowledge base that includes information relating to vulnerabilities and threats.
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