Transactional TF: Transform Library with Concurrency and Correctness

Yushi OGIWARA; Ayanori YOROZU; Akihisa OHYA; Hideyuki KAWASHIMA

doi:10.1587/transinf.2023PAP0006

Transactional TF: Transform Library with Concurrency and Correctness

Yushi OGIWARA, Ayanori YOROZU, Akihisa OHYA, Hideyuki KAWASHIMA

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In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.

Publication: IEICE TRANSACTIONS on Information Vol.E106-D No.12 pp.1951-1959

Publication Date: 2023/12/01

Publicized: 2023/06/22

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2023PAP0006

Type of Manuscript: Special Section PAPER (Special Section on Forefront Computing)

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Authors

Yushi OGIWARA
  Keio University
Ayanori YOROZU
  University of Tsukuba
Akihisa OHYA
  University of Tsukuba
Hideyuki KAWASHIMA
  Keio University

Keyword

TF, ROS, concurrency control, transaction processing, Silo

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Yushi OGIWARA, Ayanori YOROZU, Akihisa OHYA, Hideyuki KAWASHIMA, "Transactional TF: Transform Library with Concurrency and Correctness" in IEICE TRANSACTIONS on Information, vol. E106-D, no. 12, pp. 1951-1959, December 2023, doi: 10.1587/transinf.2023PAP0006.
Abstract: In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023PAP0006/_p

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@ARTICLE{e106-d_12_1951,
author={Yushi OGIWARA, Ayanori YOROZU, Akihisa OHYA, Hideyuki KAWASHIMA, },
journal={IEICE TRANSACTIONS on Information},
title={Transactional TF: Transform Library with Concurrency and Correctness},
year={2023},
volume={E106-D},
number={12},
pages={1951-1959},
abstract={In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.},
keywords={},
doi={10.1587/transinf.2023PAP0006},
ISSN={1745-1361},
month={December},}

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TY - JOUR
TI - Transactional TF: Transform Library with Concurrency and Correctness
T2 - IEICE TRANSACTIONS on Information
SP - 1951
EP - 1959
AU - Yushi OGIWARA
AU - Ayanori YOROZU
AU - Akihisa OHYA
AU - Hideyuki KAWASHIMA
PY - 2023
DO - 10.1587/transinf.2023PAP0006
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2023
AB - In the Robot Operating System (ROS), a major middleware for robots, the Transform Library (TF) is a mandatory package that manages transformation information between coordinate systems by using a directed forest data structure and providing methods for registering and computing the information. However, the structure has two fundamental problems. The first is its poor scalability: since it accepts only a single thread at a time due to using a single giant lock for mutual exclusion, the access to the tree is sequential. Second, there is a lack of data freshness: it retrieves non-latest synthetic data when computing coordinate transformations because it prioritizes temporal consistency over data freshness. In this paper, we propose methods based on transactional techniques. This will allow us to avoid anomalies, achieve high performance, and obtain fresh data. These transactional methods show a throughput of up to 429 times higher than the conventional method on a read-only workload and a freshness of up to 1276 times higher than the conventional one on a read-write combined workload.
ER -