BORA: A Bag Optimizer for Robotic Analysis
发布时间:2020-12-28        浏览次数:10

报告题目:BORA: A Bag Optimizer for Robotic Analysis

报告人:殷树 上海科技大学

主持人:石亮 教授

报告时间:20201229日 星期二 10:00-12:00



        We present BORA (Bag Optimizer for Robotic Analysis), a file system middleware that optimizes the acquisition of bags, which are specially formatted files used to store timestamped ROS (robot operating system) messages. BORA sits between ROS and an existing file system to conduct semantic-aware data pre-processing. In particular, it categorizes ROS bag data into multiple groups with each having a distinct label. BORA predigests data index constructions and reduces file open time via a hash-based label management scheme. It is also capable of providing ROS analytic applications with only data needed without a sequence of data searching and locating operations. We implement a BORA prototype, which is then integrated into three computing platforms: a single-node server, a four-node PVFS storage cluster, and a Tianhe-1A Supercomputer storage subsystem. Next, we evaluate the BORA prototype on the three platforms using four real-world ROS applications. 

        Our experimental results show that compared to a traditional bag management scheme BORA improves data acquisition performance by up to 11x. In addition, it offers up to 10x data acquisition performance improvement and 3,100x bags open improvement under a swarm robotics data analysis scenario where data is retrieved across multiple bags simultaneously.


        殷树,上海科技大学助理教授、研究员、博导。2012年获得美国奥本大学计算机科学博士学位,同年加入湖南大学任助理教授,2014年于国防科技大学高性能计算国家重点实验室从事博士后工作,2015年获得国家自然科学基金资助,同年晋升为副教授,2016年全职加入上海科技大学信息科学与技术学院。殷树博士的研究领域包括并行与分布式系统、高性能文件系统、文件系统节能及可靠性分析,在IEEE SC, ICDCS, ICPP, TPDS, TDSC等会议和期刊上发表论文30余篇。

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