Metadata prefetching and data placement play a critical role in enhancing access performance for file systems operating over wide-area networks.However,developing effective strategies for metadata prefetching in envir...Metadata prefetching and data placement play a critical role in enhancing access performance for file systems operating over wide-area networks.However,developing effective strategies for metadata prefetching in environments with concurrent workloads and for data placement across distributed networks remains a significant challenge.This study introduces novel and efficient methodologies for metadata prefetching and data placement,leveraging fine-grained control of prefetching strategies and variable-sized data fragment writing to optimize the I/O bandwidth of distributed file systems.The proposed metadata prefetching technique employs dynamic workload analysis to identify dominant workload patterns and adaptively refines prefetching policies,thereby boosting metadata access efficiency under concurrent scenarios.Meanwhile,the data placement strategy improves write performance by storing data fragments locally within the nearest data center and transmitting only the fragment location metadata to the remote data center hosting the original file.Experimental evaluations using real-world system traces demonstrate that the proposed approaches reduce metadata access times by up to 33.5%and application data access times by 17.19%compared to state-of-the-art techniques.展开更多
The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
基金funded by the National Natural Science Foundation of China under Grant No.62362019the Hainan Provincial Natural Science Foundation of China under Grant No.624RC482.
文摘Metadata prefetching and data placement play a critical role in enhancing access performance for file systems operating over wide-area networks.However,developing effective strategies for metadata prefetching in environments with concurrent workloads and for data placement across distributed networks remains a significant challenge.This study introduces novel and efficient methodologies for metadata prefetching and data placement,leveraging fine-grained control of prefetching strategies and variable-sized data fragment writing to optimize the I/O bandwidth of distributed file systems.The proposed metadata prefetching technique employs dynamic workload analysis to identify dominant workload patterns and adaptively refines prefetching policies,thereby boosting metadata access efficiency under concurrent scenarios.Meanwhile,the data placement strategy improves write performance by storing data fragments locally within the nearest data center and transmitting only the fragment location metadata to the remote data center hosting the original file.Experimental evaluations using real-world system traces demonstrate that the proposed approaches reduce metadata access times by up to 33.5%and application data access times by 17.19%compared to state-of-the-art techniques.
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
