SERVICE MANAGEMENT FOR THE INFRASTRUCTURE OF BLOCKCHAIN NETWORKS

    公开(公告)号:US20190268407A1

    公开(公告)日:2019-08-29

    申请号:US15905015

    申请日:2018-02-26

    Abstract: Techniques facilitating service management for the infrastructure of blockchain networks are provided. A system comprises a memory and a processor that executes computer executable components stored in the memory. The computer executable components can comprise an allocation component, a grouping component, and an implementation component. The allocation component can assign, within a blockchain network, a first group of nodes of a first node type to a first set of operation slots and a second group of nodes of a second node type, different than the first node type, to a second set of operation slots. The grouping component can aggregate the second group of nodes assigned to the second set of operation slots with the first group of nodes within the first set of operation slots. The implementation component can execute a service management operation. A consensus algorithm can be satisfied during an execution of the service management operation.

    Compliance aware application scheduling

    公开(公告)号:US11954524B2

    公开(公告)日:2024-04-09

    申请号:US17330583

    申请日:2021-05-26

    CPC classification number: G06F9/4881 G06F9/5005 G06F2209/5011 G06F2209/503

    Abstract: A method for scheduling services in a computing environment includes receiving a service scheduling request corresponding to the computing environment and identifying a resource pool and a set of compliance requirements corresponding to the computing environment. The method continues by identifying target resources within the resource pool, wherein target resources are resources which meet the set of compliance requirements, and subsequently identifying a set of available target resources, wherein available target resources are target resources with scheduling availability. The method further includes analyzing the set of available target resources to determine a risk score for each available target resource and selecting one or more of the set of available target resources according to the determined risk scores. The method continues by scheduling a service corresponding to the service scheduling request on the selected one or more available target resources.

    COMPLIANCE AWARE APPLICATION SCHEDULING

    公开(公告)号:US20220382583A1

    公开(公告)日:2022-12-01

    申请号:US17330583

    申请日:2021-05-26

    Abstract: A method for scheduling services in a computing environment includes receiving a service scheduling request corresponding to the computing environment and identifying a resource pool and a set of compliance requirements corresponding to the computing environment. The method continues by identifying target resources within the resource pool, wherein target resources are resources which meet the set of compliance requirements, and subsequently identifying a set of available target resources, wherein available target resources are target resources with scheduling availability. The method further includes analyzing the set of available target resources to determine a risk score for each available target resource and selecting one or more of the set of available target resources according to the determined risk scores. The method continues by scheduling a service corresponding to the service scheduling request on the selected one or more available target resources.

    Maximizing system scalability while guaranteeing enforcement of service level objectives

    公开(公告)号:US11930073B1

    公开(公告)日:2024-03-12

    申请号:US17971084

    申请日:2022-10-21

    CPC classification number: H04L67/1012 G06F18/217 H04L67/1008

    Abstract: A computer-implemented method, system and computer program product for maximizing system scalability while guaranteeing enforcement of service level objectives. A request is received to access a backend database in a hierarchy of backend databases that includes heterogenous computing resources with a dynamic range of performance. Upon receiving the request, a reinforcement learning based filter determines if the request's frequency of access exceeds a cutoff frequency. If the received request is not filtered, but instead, is passed through the filter, then one of the backend databases in the hierarchy is selected. Such a selection is made by a load balancer that is trained using reinforcement learning to select the optimal backend database taking into consideration the storage size and speed of the backend databases as well as taking into consideration the user-specified service level objective to be met by the request to guarantee enforcement of such a service level objective.

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