SCALABLE DATA PROCESSING IN AUV ONBOARD DISTRIBUTED COMPUTING SYSTEM

Abstract

Development of distributed computing systems (DCS) takes an important place in modern scientific and technical literature. Generally, only one of the most significant features of DCS is discussed in the papers, for example, performance, reliability, fault tolerance, energy efficiency and scalability. In this paper authors attempt to overall consider the problem of DCS design, based on the example of a multi-channel onboard DCS for data processing places in autonomous underwater vehicle (AUV). The aim of this paper is to formulate a unified concept of a multi-channel onboard DCS for real-time data processing. As a result, the architecture and principles of operation of a multi- channel onboard DCS are proposed, based on well-known approaches to fault tolerance and energy efficiency, taking into account the features of scalable systems. The proposed solutions can be viewed as an advancement of traditional approaches to scalable systems development. Fault tolerance is achieved by using test-based diagnostic tools. In order to reduce the complexity of these tools, redundancy is preliminarily added into each software module (SM) of the system. Then tests for the redundancies are built. It is shown that this test detects failures in the addressing of exchanges between SM blocks that implement the data processing. Based on the results of the analysis of the diagnostic tool reaction to the test, a failed software module is detected. Then failed module stops its work, and a new SM that implements the same algorithm is started execute instead. Energy efficiency proposals are suitable for the case of the presence of redundant processors in the system which could support multi-core technology. These processors could be involved in the execution process of the system SMs with a simultaneous decrease in the clock frequency and supply voltage. Since the power consumption in the DCS significantly depends on frequency and supply voltage, it decreases along with this parameter values. An optimal greedy algorithm is used to solve described problem, which assumes sequential involving of additional processors into the system. It is important that energy efficiency proposal of the DCS provides the latter additional fault tolerance capabilities. The practical importance of the proposed concept consists in the possibility of using not only in AUVs application. It also could be used in other cases of scalable multi-channel onboard DCS development with real-time data processing which have fault tolerance and energy efficiency requirements.

Authors

References

1. Ramírez I. S., Bernalte Sánchez P. J., Papaelias M., Márquez F. P. G. Autonomous underwater
vehicles and field of view in underwater operations, Journal of Marine Science and Engineering,
2021, Vol. 9, No. 3, pp. 277.
2. Yang Y., Xiao Y., Li T. A survey of autonomous underwater vehicle formation: Performance,
formation control, and communication capability, IEEE Communications Surveys & Tutorials,
2021, Vol. 23, No. 2, pp. 815-841.
3. Sahoo A., Dwivedy S.K., Robi P.S. Advancements in the field of autonomous underwater vehicle,
Ocean Engineering, 2019, Vol. 181, pp. 145-160.
4. Inzartsev A.V., Kiselev L.V., Kostenko V.V., Matvienko Yu.V., Pavin A.M., Shcherbatyuk A.F.
Podvodnye robototekhnicheskie kompleksy: sistemy, tekhnologii, primenenie [Underwater robotic
complexes: systems, technologies, application]. Vladivostok: Institut problem morskikh
tekhnologiy Dal'nevostochnogo otdeleniya Rossiyskoy akademii nauk, 2018, 368 p.
5. Kolesov N.V., Tolmacheva M.V., Yukhta P.V. Sistemy real'nogo vremeni. Planirovanie, analiz,
diagnostirovanie [Real-Time Systems: Planning, Analysis, Diagnostics]. St. Petersburg:
OAO "Kontsern "TSNII "Elektropribor", 2014, 185 p.
6. Kshemkalyani A. D., Singhal M. Distributed computing: principles, algorithms, and systems.
Cambridge University Press, 2011, 731 p.
7. Toporkov V.V. Modeli raspredelennykh vychisleniy [Models of Distributed Computing]. Moscow:
Fizmatlit, 2011. – 320 s.
8. Voevodin V.V., Voevodin Vl.V. Parallel'nye vychisleniya [Parallel computing]. St. Petersburg:
BKhV-Peterburg, 2002, 608 p.
9. Kapitonova Y.V., Kovalenko N.S., Pavlov P.A. Optimality of systems of identically distributed
competing processes, Cybernetics and Systems Analysis, 2005, Vol. 41, No. 6, pp. 793-799.
10. Pavlov P.A. Effektivnost' raspredelennykh vychisleniy v masshtabiruemykh sistemakh [Efficiency
of distributed computing in scalable systems], Informatika, telekommunikatsii i upravlenie [Informatics,
telecommunications and management], 2010, Vol. 93, No. 1, pp. 83-89.
11. Gruzlikov A.M., Kolesov N.V. Diskretno-sobytiynaya diagnosticheskaya model' raspredelennoy
vychislitel'noy sistemy. Nezavisimye tsepi [Discrete-event diagnostic model for a distributed
computational system. Independent chains], Avtomatika i telemekhanika [Automation and
Remote Control], 2016, No. 10, pp. 140-155.
12. Gruzlikov A.M., Kolesov N.V., Lukoyanov E.V., Tolmacheva M.V. Diagnosticheskaya model'
dlya raspredelennoy vychislitel'noy sistemy real'nogo vremeni Diagnostic model for a distributed
real-time computing system], Izvestiya Rossiyskoy akademii nauk. Teoriya i sistemy
upravleniya [Proceedings of the Russian Academy of Sciences. Theory and control systems],
2020, No. 5, pp. 44-55.
13. Burdonov I.B., Kosachev A.S., Kulyamin V.V. Ispol'zovanie konechnykh avtomatov dlya
testirovaniya programm [The use of finite automata for program testing], Programmirovanie
[Programming], 2000, Vol. 26, No. 2, pp. 61-73.
14. Cassandras C.G., Lafortune S. Introduction to discrete event systems. Boston, MA: Springer
US, 2008, 848 p.
15. Zaytoon J., Lafortune S. Overview of fault diagnosis methods for discrete event systems, Annual
Reviews in Control, 2013, Vol. 37, No. 2, pp. 308-320.
16. Preparata F. P., Metze G., Chien R. T. On the connection assignment problem of diagnosable
systems, IEEE Transactions on Electronic Computers, 1967, No. 6, pp. 848-854.
17. Gruzlikov A.M., Kolesov N.V., Kostygov D.V., Tolmacheva M.V. A real-time fault-tolerant and
power-efficient multicore system on chip, 2019 IEEE 13th International Symposium on Embedded
Multicore/Many-core Systems-on-Chip (MCSoC). IEEE, 2019, pp. 354-361.
18. Moldovanova O.V. Adaptivnyy algoritm detsentralizovannoy samodiagnostiki
raspredelennykh vychislitel'nykh sistem razlichnykh topologiy [Adaptive algorithm for decentralized
self-diagnostics of distributed computing systems of various topologies], Vestnik
SibGUTI [Vestnik SibGUTI], 2013, Vol. 22, No. 2, pp. 22-30.
19. Panda P.R., Shrivastava A., Silpa B.V.N., Gummidipudi K. Power-efficient system design.
Springer Science & Business Media, 2010, 253 p.
20. Rubavani R., Saranraj S., Saranya S., Ranjani Devi R. Power efficient scheduling for network
on chip applications on multicore processor, International Journal of Applied Engineering
Research, 2016, Vol. 11, No. 7, pp. 4751-4757.
21. Gruzlikov A.M., Kolesov N.V., Kostygov D.V., Oshuev V.V. Energoeffektivnoe planirovanie v
raspredelennykh vychislitel'nykh sistemakh real'nogo vremeni [Energy-efficient planning in
real-time distributed computing systems], Izvestiya Rossiyskoy akademii nauk. Teoriya i
sistemy upravleniya [News of the Russian Academy of Sciences. Theory and control systems],
2019, No. 3, pp. 66-76.

Скачивания

Published:

2023-04-10

Issue:

Section:

SECTION V. TECHNICAL VISION

Keywords:

Fault tolerance, energy efficiency, distributed computing systems, scalable systems, autonomous uninhabited underwater vehicles