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公开(公告)号:US10922276B2
公开(公告)日:2021-02-16
申请号:US15521370
申请日:2015-01-15
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor: Santigopal Mondal , Anand Andaneppa Ganjihal , Anoop Kumar Raveendran , Sandya Srivilliputtur Mannarswamy
IPC: G06F16/00 , G06F16/17 , G06F16/13 , G06F16/182
Abstract: Storage space may be allocated from a non-reserved zone of a file system when the file system is not undergoing an online file system check. When the file system is undergoing an online file system check, storage space is allocated from a soft-reserved zone.
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公开(公告)号:US20170316027A1
公开(公告)日:2017-11-02
申请号:US15521370
申请日:2015-01-15
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor: Santigopal Mondal , Anand Andaneppa Ganjihal , Anoop Kumar Raveendran , Sandya Srivilliputtur Mannarswamy
IPC: G06F17/30
CPC classification number: G06F16/1727 , G06F16/13 , G06F16/134 , G06F16/182
Abstract: Storage space may be allocated from a non-reserved zone of a file system when the file system is not undergoing an online file system check. When the file system is undergoing an online file system check, storage space is allocated from a soft-reserved zone.
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公开(公告)号:US12235795B2
公开(公告)日:2025-02-25
申请号:US17816276
申请日:2022-07-29
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor: Sagar Venkappa Nyamagouda , Smitha Jayaram , Hiro Rameshlal Lalwani , Rachit Gupta , Sherine Jacob , Anand Andaneppa Ganjihal
Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.
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公开(公告)号:US20210357366A1
公开(公告)日:2021-11-18
申请号:US17301908
申请日:2021-04-19
Applicant: Hewlett Packard Enterprise Development LP
Inventor: Anand Andaneppa Ganjihal , Ankit Gupta
IPC: G06F16/16 , G06F16/182 , G06F16/17 , G06F8/658 , G06F11/07
Abstract: Aspects for remote analysis of file system metadata are described. In an example, a computer-readable file from a client system is received. The computer-readable file comprises file system metadata of a file system, and corresponding source location of the file system metadata on a volume of the client system. Thereafter, a target location on a target volume is identified, wherein the target location corresponds to the source location on the volume of the client system. In an example, the file system metadata is replicated onto the target location based on the computer-readable file, for analysis.
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公开(公告)号:US12066980B2
公开(公告)日:2024-08-20
申请号:US17301908
申请日:2021-04-19
Applicant: Hewlett Packard Enterprise Development LP
Inventor: Anand Andaneppa Ganjihal , Ankit Gupta
IPC: G06F16/16 , G06F11/07 , G06F16/182
CPC classification number: G06F16/164 , G06F11/0772 , G06F11/0793 , G06F16/184
Abstract: Aspects for remote analysis of file system metadata are described. In an example, a computer-readable file from a client system is received. The computer-readable file comprises file system metadata of a file system, and corresponding source location of the file system metadata on a volume of the client system. Thereafter, a target location on a target volume is identified, wherein the target location corresponds to the source location on the volume of the client system. In an example, the file system metadata is replicated onto the target location based on the computer-readable file, for analysis.
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公开(公告)号:US20240037067A1
公开(公告)日:2024-02-01
申请号:US17816276
申请日:2022-07-29
Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
Inventor: Sagar Venkappa Nyamagouda , Smitha Jayaram , Hiro Rameshlal Lalwani , Rachit Gupta , Sherine Jacob , Anand Andaneppa Ganjihal
CPC classification number: G06F16/13 , G06F11/3414
Abstract: In some examples, a system receives workload information of a workload collection, and applies a machine learning model on the workload information, the machine learning model trained using training information including features of different types of workloads. The system produces, by the machine learning model, an identification of a first file system from among different types of file systems, the machine learning model producing an output value corresponding to the first file system that is a candidate for use in storing files of the workload collection.
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