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1093 条记录 (耗时 0.025 秒)

    DYNAMICALLY SCALED HYPERCONVERGED SYSTEM
    71.
    发明公开
    DYNAMICALLY SCALED HYPERCONVERGED SYSTEM 审中-公开

    公开(公告)号：EP3588296A1

    公开(公告)日：2020-01-01

    申请号：EP19183385.4

    申请日：2019-06-28

    申请人： Hewlett Packard Enterprise Development LP

    发明人： HEALEY, Michael ,  GLADE, Bradford B ,  RICHARDS, Gareth David ,  CORBETT, Peter F

    IPC分类号： G06F9/50

    摘要： Cluster groups define a relationship between clusters in an HCl system. New cluster groups may be created, and a cluster may be added or removed from an existing cluster group. A method and system are disclosed to support a dynamically scaled hyperconverged system with non-overlapping and overlapping cluster groups. A dynamically scalable hyperconverged system may allow for piece-wise upgrades while supporting inter-operable and non-interoperable components having a secure intercommunication capability. A system is disclosed for scaling a single managed hyperconverged infrastructure (HCl) where all components are allowed to run a compatible communication protocol (e.g., interoperable communication protocol) but are not required to operate an interoperable software version. Further, methods are disclosed to a) illustrate consolidation of backups for a cluster of HCl nodes running interoperable and non-interoperable data exchange protocols; and b) dynamically organize groups of clusters to enforce interoperability requirements and simplify network configuration among these clusters.

    SYSTEM AND METHOD OF DECENTRALIZED MANAGEMENT OF DEVICE ASSETS OUTSIDE A COMPUTER NETWORK
    75.
    发明公开
    SYSTEM AND METHOD OF DECENTRALIZED MANAGEMENT OF DEVICE ASSETS OUTSIDE A COMPUTER NETWORK 审中-公开

    公开(公告)号：EP3564883A1

    公开(公告)日：2019-11-06

    申请号：EP18177566.9

    申请日：2018-06-13

    申请人： Hewlett Packard Enterprise Development LP

    发明人： MANAMOHAN, Sathyanarayana ,  SHASTRY, Krishnaprasad Lingadahalli ,  PANDEY, Avinash Chandra ,  SARVESWARA, Ravi

    IPC分类号： G06Q20/06 ,  H04L9/32 ,  H04L29/06

    摘要： The disclosure relates to decentralized management of edge nodes operating outside an enterprise network using blockchain technology. A management node may operate within a firewall of the enterprise to manage the edge nodes operating outside the firewall using blockchain technology. The management node may coordinate management by writing change requests to a decentralized ledger. The edge nodes may read the change requests from its local copy of the distributed ledger and implement the change requests. Upon implementation, an edge node may broadcast its status to the blockchain network. The management node may mine the transactions from the edge nodes into the distributed ledger, thereby creating a secure and scalable way to coordinate management and record the current and historical system state. The system also provides the edge nodes with a cryptographically secured, machine-to-machine maintained, single version of truth, enabling them to take globally valid decision based on local data.

    SYSTEM AND METHOD OF DECENTRALIZED MACHINE LEARNING USING BLOCKCHAIN
    76.
    发明公开
    SYSTEM AND METHOD OF DECENTRALIZED MACHINE LEARNING USING BLOCKCHAIN 审中-公开

    公开(公告)号：EP3564873A1

    公开(公告)日：2019-11-06

    申请号：EP18183265.0

    申请日：2018-07-12

    申请人： Hewlett Packard Enterprise Development LP

    发明人： MANAMOHAN, Sathyanarayanan ,  SHASTRY, Krishnaprasad Lingadahalli ,  GARG, Vishesh

    IPC分类号： G06N20/20 ,  H04L29/06

    摘要： Decentralized machine learning to build models is performed at nodes where local training datasets are generated. A blockchain platform may be used to coordinate decentralized machine learning over a series of iterations. For each iteration, a distributed ledger may be used to coordinate the nodes. Rules in the form of smart contracts may enforce node participation in an iteration of model building and parameter sharing, as well as provide logic for electing a node that serves as a master node for the iteration. The master node obtains model parameters from the nodes and generates final parameters based on the obtained parameters. The master node may write its state to the distributed ledger indicating that the final parameters are available. Each node, via its copy of the distributed ledger, may discover the master node's state and obtain and apply the final parameters to its local model, thereby learning from other nodes.