BORA: A Bag Optimizer for Robotic Analysis
BORA: A Bag Optimizer for Robotic Analysis
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DOI：
10.1109/sc41405.2020.00016
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Jian Zhang-;Tao Xie;Yuzhuo Jing;Yanjie Song;Guanzhou Hu;Si Chen;Shu Yin
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文献类型：
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作者：
Jian Zhang-;Tao Xie;Yuzhuo Jing;Yanjie Song;Guanzhou Hu;Si Chen;Shu Yin
我们提出BORA（用于机器人分析的Bag Optimizer），该文件系统中间件优化了袋子的采集，该袋子是用于存储时间戳ROS（机器人操作系统）消息的特殊格式的文件。 Bora位于ROS和现有文件系统之间，以进行语义意识数据预处理。特别是，它将ROS Bag数据分为多组，每个组具有不同的标签。 BORA预测数据索引构造并通过基于哈希的标签管理方案减少文件开放时间。它还能够仅提供无需数据搜索和定位操作的数据提供ROS分析应用程序。我们实现了BORA原型，然后将其集成到三个计算平台中：单节点服务器，四节点PVFS存储群集和Tianhe-1A超级计算机存储子系统。接下来，我们使用四个现实世界的ROS应用程序在三个平台上评估Bora原型。我们的实验结果表明，与传统的行李管理方案相比，BORA将数据采集性能提高了11倍。此外，在群体机器人数据分析方案下，它提供了高达10倍的数据采集性能改进，并提供3,100倍的袋子开放改进，在该方案中，同时在多个袋子中检索了数据。
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.