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公开(公告)号:EP4411542A2
公开(公告)日:2024-08-07
申请号:EP23218807.8
申请日:2019-11-25
IPC分类号: G06F9/50
CPC分类号: G06F9/5044 , G06F2209/50320130101 , G06F9/5061
摘要: Systems, methods, and non-transitory computer readable media are provided for managing assignment of modules. A job from a client may be received. The job may be inserted into a job queue. The job within the job queue may be compared with a set of cooldown modules to determine whether a compatible cooldown module is available. The job may be assigned to the compatible cooldown module responsive to the compatible cooldown module being available.
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公开(公告)号:EP4409407A1
公开(公告)日:2024-08-07
申请号:EP22793280.3
申请日:2022-09-23
IPC分类号: G06F9/50
CPC分类号: G06F9/505 , G06F9/5044 , G06F2209/50320130101
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公开(公告)号:EP4372563A1
公开(公告)日:2024-05-22
申请号:EP23208274.3
申请日:2023-11-07
IPC分类号: G06F9/50
CPC分类号: G06F9/5044 , G06F9/505 , G06F2209/50120130101 , G06F2209/50320130101
摘要: A method may include performing, at a computational storage device, using first data stored at the computational storage device, a first computational task of a workload, wherein the performing the first computational task of the workload may include generating second data, transferring, from the computational storage device to a computational device, using an interconnect fabric, the second data, and performing, at the computational device, using the second data, a second computational task of the workload. The transferring the second data may include transferring the second data using a root complex of the interconnect fabric. The transferring the second data may include transferring the second data using a switch of the interconnect fabric. The transferring the second data may include performing a peer-to-peer transfer. The transferring the second data may include performing a direct memory access.
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公开(公告)号:EP4409408A1
公开(公告)日:2024-08-07
申请号:EP22793912.1
申请日:2022-09-23
发明人: HERZ, William , TIKHOSTOUP, Dmitri , WONG, Daniel Waihim , SINGER, Mitchell H. , STEFANIZZI, Bruno
IPC分类号: G06F9/50
CPC分类号: G06F9/5044 , G06F2209/50820130101 , G06F2209/50320130101 , G06F2209/50920130101 , G06F9/5072
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公开(公告)号:EP4396679A1
公开(公告)日:2024-07-10
申请号:EP22755367.4
申请日:2022-07-28
发明人: ABDOLLAHIAN NOGHABI, Shadi , CHANDRA, Ranveer , BADAM, Anirudh , PISHORI, Riyaz Mohamed , KALYANARAMAN, Shivkumar , IYENGAR, Srinivasan
CPC分类号: G06F9/5094 , G06F9/5072 , G06F9/5027 , G06F9/4875 , G06F2209/50420130101 , G06F2209/50320130101 , G06F2209/50620130101 , Y02D10/00
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6.
公开(公告)号:EP4383074A1
公开(公告)日:2024-06-12
申请号:EP22851871.8
申请日:2022-07-19
发明人: XU, Shili , FU, Yabin , ZHONG, Bingwu , HU, Yulin , LU, Yanhui , MA, Xiaohu
IPC分类号: G06F9/50
CPC分类号: G06F2209/501720130101 , G06F9/5066 , G06F2209/50120130101 , G06F9/505 , G06F9/5072 , G06F2209/50320130101
摘要: Disclosed in embodiments of the present invention are a service processing method and apparatus, a server, a storage medium, and a computer program product, relating to resource scheduling in cloud technology. The method comprises: determining a first computing power resource required to execute an offline task; determining a number N of edge servers used to execute the offline task, a cloud application running on the N edge servers; idle computing power resources of the N edge servers being greater than the first computing power resource, and the idle computing power resources of the N edge servers being the sum of the idle computing power resources of each edge server of the N edge servers; and scheduling the offline task to the N edge servers in a distributed manner, so that each edge server of the N edge servers performs the offline task using the idle computing power resources of each edge server, while ensuring the cloud application runs normally. Using the embodiments of the present invention, a resource utilization rate of an edge server is increased.
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7.
公开(公告)号:EP4435602A1
公开(公告)日:2024-09-25
申请号:EP23218406.9
申请日:2023-12-20
IPC分类号: G06F9/50
CPC分类号: G06F9/5027 , G06F9/5044 , G06F9/505 , G06F2209/50320130101
摘要: A decentralized scheduler node (212) for an unstable network of heterogenous nodes collects status information including a resource capacity of each of a network of nodes with which it is in communication. For each current computation job in its input queue, the scheduler node (212) determines which connected nodes have suitable resource capacity to execute the current job and a reliability of each suitable node based on statistical evaluation of its historical availability for job deployment. From the set of suitably powerful and reliable candidate nodes, the scheduler node (212) selects a candidate node of sufficient resource capacity for deployment of the current job based on the probability of a subsequent job appearing during the execution time of the current job, the subsequent job having an equivalent or greater resource requirement and thus at risk of waiting in the input queue if the selected node is required for executing the subsequent job.
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公开(公告)号:EP4430522A1
公开(公告)日:2024-09-18
申请号:EP22889569.4
申请日:2022-11-03
申请人: R-Stealth Ltd
发明人: BOREN, Ben , BOREN, Harel , LIN, Amit
CPC分类号: G06F9/5044 , G06F9/5066 , G06F2209/50320130101 , G06F2209/50920130101 , G06F2209/50120130101 , G06F9/5072 , G06F9/541 , G06F9/5077 , G06F9/505 , G06N3/0985 , G06N3/09 , G06N3/0442 , G06N3/0464 , G06N3/047 , G06N3/0475 , G06N5/01 , G06N20/20 , G06N3/084 , G06N3/098
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公开(公告)号:EP4357917A1
公开(公告)日:2024-04-24
申请号:EP22840991.8
申请日:2022-04-18
发明人: LING, Neiwen , WANG, Kai , XIE, Daqi
IPC分类号: G06F9/48
CPC分类号: G06F2209/50120130101 , G06F2209/501720130101 , G06F2209/50320130101 , G06F2209/50620130101 , G06F2209/48520130101 , G06F9/4881 , G06F9/5066 , G06F9/505 , G06F9/4887 , G06N3/096 , G06N3/0464 , G06N3/0495 , G06N3/082 , G06N3/063
摘要: This application discloses a task execution method and apparatus, and belongs to the field of resource scheduling technologies. The method includes: determining a plurality of deep learning tasks to be concurrently executed and an artificial intelligence model for implementing each deep learning task; obtaining an execution policy of each deep learning task, where the execution policy indicates a scheduling mode and a used model variant of the deep learning task, and the model variant of the deep learning task is obtained according to the artificial intelligence model for implementing the deep learning task; and executing a corresponding deep learning task according to the execution policy of each deep learning task. In this application, execution performance of a deep learning task can be improved in terms of a scheduling mode of the deep learning task, and can also be improved in terms of a model for implementing the deep learning task, to effectively improve the execution performance of the deep learning task.
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公开(公告)号:EP4423607A1
公开(公告)日:2024-09-04
申请号:EP22813854.1
申请日:2022-10-28
发明人: AHMAD, Arsalan , VAN DEN DUNGEN, Martinus Petrus Lambertus , GUPTA, Lokesh , NAGARAJA, Girish , VAISHNAVI, Nikhil Yograj
IPC分类号: G06F9/50
CPC分类号: G06F9/5027 , G06F2209/50320130101 , G06F9/5088
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