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1. 发明申请

US20230129969A1 PLUG-IN FOR ENHANCING RESOURCE ELASTIC SCALING OF DISTRIBUTED DATA FLOW AND METHOD FOR ENHANCING PLUG-IN FOR ENHANCING RESOURCE ELASTIC SCALING OF DISTRIBUTED DATA FLOW 有权
公开(公告)号：US20230129969A1
公开(公告)日：2023-04-27
申请号：US17791484
申请日：2021-10-20
申请人： Tsinghua University
发明人： Lijie Wen , Zan Zong
IPC分类号： G06F9/50 , G06F9/445 , G06N20/00
摘要： The present disclosure relates to a plug-in for enhancing resource elastic scaling of a distributed data flow and a method for enhancing a plug-in for enhancing resource elastic scaling of a distributed data flow. The plug-in is connected to a scaling controller used for resource elastic scaling of a distributed data flow. The plug-in includes a decision maker, a decision model, and a scaling operation sample library. The scaling controller registers a data flow to the plug-in through a first interface. The scaling controller sends an optimal decision of resource scaling in each status to the plug-in through a second interface. The scaling operation sample library is configured to record the optimal decision of resource scaling in each status. The decision model is configured to predict a received data flow based on the optimal decision recorded in the scaling operation sample library, to generate a prediction decision.

2. 发明申请

US20230042890A1 METHOD AND SYSTEM FOR OPTIMIZING PARAMETER CONFIGURATION OF DISTRIBUTED COMPUTING JOB 有权
公开(公告)号：US20230042890A1
公开(公告)日：2023-02-09
申请号：US17791472
申请日：2021-10-20
申请人： Tsinghua University
发明人： Lijie Wen , Zan Zong
IPC分类号： G06F9/50
摘要： The present disclosure relates to a method and system for optimizing a parameter configuration of a distributed computing job. The method includes: obtaining job programs of different distributed computing jobs, and determining a key parameter configuration set; obtaining a cluster status during execution of the distributed computing job, randomly generating a sample data set based on the key parameter configuration set and the cluster status, and establishing a performance prediction model; correcting the performance prediction model by using a multi-objective genetic algorithm and an optimization module configured with an optimal configuration selection strategy; obtaining a job program of a to-be-optimized distributed computing job and a cluster status during execution of the to-be-optimized distributed computing job, and determining a to-be-optimized key parameter configuration item combination; and inputting, to the performance prediction model, the to-be-optimized key parameter configuration item combination and the cluster status during execution of the to-be-optimized distributed computing job, and outputting a key parameter configuration item combination with a shortest execution time. The present disclosure can rapidly and effectively optimize the key parameter configuration.

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