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公开(公告)号:US20190268407A1
公开(公告)日:2019-08-29
申请号:US15905015
申请日:2018-02-26
Applicant: International Business Machines Corporation
Inventor: Sai Zeng , Jun Duan , Alexei Karve , Neeraj Asthana , Vugranam C. Sreedhar , Nerla Jean-Louis
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.
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公开(公告)号:US11954524B2
公开(公告)日:2024-04-09
申请号:US17330583
申请日:2021-05-26
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor: Braulio Gabriel Dumba , Jun Duan , Nerla Jean-Louis , Muhammed Fatih Bulut , Sai Zeng
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.
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公开(公告)号:US20220382583A1
公开(公告)日:2022-12-01
申请号:US17330583
申请日:2021-05-26
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor: BRAULIO GABRIEL DUMBA , Jun Duan , Nerla Jean-Louis , Muhammed Fatih Bulut , Sai ZENG
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.
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公开(公告)号:US20210334677A1
公开(公告)日:2021-10-28
申请号:US16858657
申请日:2020-04-26
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor: Sai Zeng , Braulio Gabriel Dumba , Jun Duan , Matthew Staffelbach , Emrah Zarifoglu , Umar Mohamed Iyoob , Manish Mahesh Modh
Abstract: A machine learning assessment system is provided. The system identifies multiple datasets and multiple machine learning (ML) modeling algorithms based on the client profile. The system assesses a cost of data collection for each dataset of the multiple datasets. The system assesses a performance metric for each ML modeling algorithm of the multiple modeling algorithms. The system recommends a dataset from the multiple datasets and an ML modeling algorithm from the multiple ML modeling algorithm based on the assessed costs of data collection for the multiple datasets and the assessed performance metrics for the multiple ML modeling algorithms.
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公开(公告)号:US11930073B1
公开(公告)日:2024-03-12
申请号:US17971084
申请日:2022-10-21
Applicant: International Business Machines Corporation
Inventor: Jun Duan , Braulio Gabriel Dumba , Andrew John Anderson
IPC: G06F15/173 , G06F18/21 , H04L67/1008 , H04L67/1012
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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公开(公告)号:US11625272B2
公开(公告)日:2023-04-11
申请号:US16994586
申请日:2020-08-15
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor: Braulio Gabriel Dumba , Ubaid Ullah Hafeez , Abdulhamid Adebayo , Jun Duan , Alexei Karve , Sai Zeng
Abstract: A computer-implemented method for managing one or more operations of a workload includes selecting a resource type for workload management on a platform. One or more operations of the selected resource to be managed are identified. A reconciliation time for execution of each of the identified operations is determined. A reconciliation period between two consecutive reconciliations is determined for each of the identified operations. A minimum number of processes for workload management of a given set of the operations on resources is calculated, and the determined minimum number of processes is deployed to manage the workload.
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