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1.
公开(公告)号:EP4432084A1
公开(公告)日:2024-09-18
申请号:EP24151138.5
申请日:2024-01-10
发明人: SAHU, Pankaj Kumar , MONDAL, Sutapa , GHAROTE, Mangesh Sharad , LODHA, Sachin Premsukh , KUMAR, Rishab , ROY, Shubhro Shovan
IPC分类号: G06F9/48 , G06F9/50 , H04L41/5009 , H04L41/5019 , H04L41/5025 , H04L41/50 , H04L43/0852 , H04L43/0876
CPC分类号: H04L41/5019 , H04L41/5009 , H04L41/5025 , H04L43/0852 , G06F9/5083 , G06F9/5072 , G06F9/5033 , G06F9/4875 , G06F2209/501520130101 , G06F2209/50620130101 , G06F2209/50820130101 , H04L41/5096 , H04L41/0897
摘要: The embodiments of present disclosure herein address the need of minimizing web services relocation and user centers reallocation to comply with data residency regulations and change in latency threshold for web services based on user demands. The method and system provide a framework that assists enterprises in migrating web services and user center allocation to different data centers with lower additional operational cost from its current configurations and minimal changes (migrations). In case of change in data regulations and invocation frequency from users' demand, non-compliant users are allocated to compliant data centers with minimal changes in the original configuration. Though the key decision is to serve the customers effectively, web services need to be deployed across a finite number of servers, there are multiple sub-problems such as minimizing latency and reduction in operational cost that needs to be addressed.
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公开(公告)号:EP4374253A1
公开(公告)日:2024-05-29
申请号:EP22768323.2
申请日:2022-08-18
发明人: BUSS, Keno , PORSCH, Roland
CPC分类号: G06F2209/501920130101 , G06F9/5027 , G06F2209/50620130101
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公开(公告)号:EP4413462A1
公开(公告)日:2024-08-14
申请号:EP22798374.9
申请日:2022-10-06
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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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5.
公开(公告)号:EP4396678A1
公开(公告)日:2024-07-10
申请号:EP22753614.1
申请日:2022-07-14
申请人: Robert Bosch GmbH
发明人: ACHTZEHN, Andreas
CPC分类号: G06F9/4881 , G06F9/5038 , G06F2209/48520130101 , G06F2209/50620130101 , G06F2209/50420130101
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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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