Method of Increasing the Autonomy of Complex Distributed Computing Systems
Abstract
This paper considers the problem of increasing the autonomy of the functioning of cluster distributed computing systems (CDCS). The goal of this research work is to increase the autonomy of functioning of cluster distributed computing systems. In accordance with the goal, the article solves the following scientific task: taking into account the analysis of the application of existing cluster distributed computing systems, their architectures and features, as well as the analysis of development trends of domestic developments in this area, develop a methodology for determining and setting the main operational requirements for promising cluster distributed computing systems that increase their autonomy. Initially, the paper describes the current state of the issue of autonomy of systems, including consideration of the existing criteria for the compliance of a computer system with the concept of autonomy, as well as the classification of systems according to the degree of their autonomy, and determines the necessary list of tasks for autonomous functioning. On the basis of this, a formalized apparatus is constructed, 2 groups of tasks are identified that are necessary to solve the issue of autonomous functioning of the CDCS, a definition of an autonomous computer system is given through the probability of operability and functionality of technical, software and information support, as well as the definition of an elementary and complex object. A feature function affecting the performance of the target task of a complex object and characterizing the intensity of malfunctions and functional failures in a unit of time is presented. The function of the coefficient of autonomy of an elementary object is introduced, on the basis of which the function of the coefficient of autonomy of a complex object is determined. On the basis of the introduced definitions and functions the formalized solution of the task of increasing autonomy is realized by maximizing the complex indicator of autonomy, based on predicting the period of autonomous task execution without external interference in the process of its functioning.
References
2. Kortenkamp D., Bonasso R.P., Ryan D., Schreckenghost D. Traded Control with Autonomous Robots as Mixed Initiative Interaction. In: Papers from the 1997 AAAI Spring Symposium. AAAI; 1997. p. 89-94. Available at: https://cdn.aaai.org/Symposia/Spring/1997/SS-97-04/SS97-04-016.pdf (accessed 11.08.2022).
3. Nicolescu M.N., Matarić M.J. A hierarchical architecture for behavior-based robots. In: Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1 (AAMAS '02). New York, NY, USA: Association for Computing Machinery; 2002. p. 227-233. doi: https://doi.org/10.1145/544741.544798
4. Nowostawski M., Purvis M. The Concept of Autonomy in Distributed Computation and Multi-agent Systems. In: 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'07). Fremont, CA, USA: IEEE Computer Society; 2007. p. 420-423. doi: https://doi.org/10.1109/IAT.2007.23
5. Pellkofer M., Luetzeler M., Dickmanns E.D. Interaction of perception and gaze control in autonomous vehicles. In: Casasent D.P., Hall E.L. (eds.) Proceedings of SPIE. Vol. 4572. Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision. SPIE; 2001. p. 219-230. doi: http://dx.doi.org/10.1117/12.444186
6. Tianfield H., Unland R. Towards autonomic computing systems. Engineering Applications of Artificial Intelligence. 2004;17(7):689-699. doi: https://doi.org/10.1016/j.engappai.2004.08.029
7. Neema S., Bapty T., Shetty S., Nordstrom S. Autonomic fault mitigation in embedded systems. Engineering Applications of Artificial Intelligence. 2004;17(7):711-725. doi: https://doi.org/10.1016/j.engappai.2004.08.031
8. Sterritt R. Autonomic networks: engineering the self-healing property. Engineering Applications of Artificial Intelligence. 2004;17(7):727-739. doi: https://doi.org/10.1016/j.engappai.2004.08.028
9. Jin X., Liu J. Characterizing autonomic task distribution and handling in grids. Engineering Applications of Artificial Intelligence. 2004;17(7):809-823. doi: https://doi.org/10.1016/j.engappai.2004.08.034
10. Fisher M., Ferrando A., Cardoso R.C. Increasing confidence in autonomous systems. In: Proceedings of the 5th ACM International Workshop on Verification and mOnitoring at Runtime EXecution (VORTEX 2021). New York, NY, USA: Association for Computing Machinery; 2021. p. 1-4. doi: https://doi.org/10.1145/3464974.3468452
11. McKee D.W., Clement S.J., Almutairi J., Xu J. Survey of advances and challenges in intelligent autonomy for distributed cyber-physical systems. CAAI Transactions on Intelligence Technology. 2018;3(2):75-82. doi: https://doi.org/10.1049/trit.2018.0010
12. Shang J., Chen P., Wang Q., Lu L. Information System Reliability Quantitative Assessment Method and Engineering Application. In: 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). Lisbon, Portugal: IEEE Computer Society; 2018. p. 182-186. doi: https://doi.org/10.1109/QRS-C.2018.00044
13. Talmale G., Shrawankar U. Cluster Based Real Time Scheduling for Distributed System. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. 2021;10(2):137-156. doi: https://doi.org/10.14201/ADCAIJ2021102137156
14. Li J., Woodside M., Chinneck J., Litiou M. Adaptive Cloud Deployment Using Persistence Strategies and Application Awareness. IEEE Transactions on Cloud Computing. 2017;5(2):277-290. doi: https://doi.org/10.1109/TCC.2015.2409873
15. Häring I. Models for Hardware and Software Development Processes. In: Technical Safety, Reliability and Resilience. Singapore: Springer; 2021. p. 179-192. doi: https://doi.org/10.1007/978-981-33-4272-9_10
16. Bouquet F., Grandpierre C., Legeard B., Peureux F. A test generation solution to automate software testing. In: Proceedings of the 3rd international workshop on Automation of software test (AST '08). New York, NY, USA: Association for Computing Machinery; 2008. p. 45-48. doi: https://doi.org/10.1145/1370042.1370052
17. Thayer R.H. Software System Engineering: A Tutorial. Computer. 2002;35(4):68-73. doi: https://doi.org/10.1109/MC.2002.993773
18. Yamada S., Tamura Y. Software Reliability. In: OSS Reliability Measurement and Assessment. Springer Series in Reliability Engineering. Cham: Springer; 2016. p. 1-13. doi: https://doi.org/10.1007/978-3-319-31818-9_1
19. Pavlov N., Iliev A., Rahnev A., Kyurkchiev N. Some Transmuted Software Reliability Models. Journal of Mathematical Sciences and Modelling. 2019;2(1):64-70. doi: http://dx.doi.org/10.33187/jmsm.434277
20. Pham H. System Software Reliability. In: Springer Series in Reliability Engineering. London: Springer; 2006. 440 p. doi: https://doi.org/10.1007/1-84628-295-0
21. Pham H. A new software reliability model with Vtub-shaped fault-detection rate and the uncertainty of operating environments. Optimization. 2014;63(10):1481-1490. doi: https://doi.org/10.1080/02331934.2013.854787
22. In Hong Chang, Hoang Pham, Seung Woo Lee, Kwang Yoon Song. A testing-coverage software reliability model with the uncertainty of operating environments. International Journal of Systems Science: Operations & Logistics. 2014;1(4):220-227. doi: https://doi.org/10.1080/23302674.2014.970244
23. Hidaka S., Hu Z., Litoiu M., Liu L., Martin P., Peng X., Wang G., Yu Y. Design and Engineering of Adaptive Software Systems. In: Yu Y., Bandara A., et al. (eds.) Engineering Adaptive Software Systems. Singapore: Springer; 2019. p. 1-33. doi: https://doi.org/10.1007/978-981-13-2185-6_1
24. Narendra K.S., Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks. 1990;1(1):4-27. doi: https://doi.org/10.1109/72.80202
25. Timofeev R.A., Shlychkov V.V., Nestulaeva D.R. Methods of economic reliability assessment for industrial enterprise in the market economy conditions. SHS Web of Conferences. 2017;35:01125. doi: https://doi.org/10.1051/shsconf/20173501125
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication policy of the journal is based on traditional ethical principles of the Russian scientific periodicals and is built in terms of ethical norms of editors and publishers work stated in Code of Conduct and Best Practice Guidelines for Journal Editors and Code of Conduct for Journal Publishers, developed by the Committee on Publication Ethics (COPE). In the course of publishing editorial board of the journal is led by international rules for copyright protection, statutory regulations of the Russian Federation as well as international standards of publishing.
Authors publishing articles in this journal agree to the following: They retain copyright and grant the journal right of first publication of the work, which is automatically licensed under the Creative Commons Attribution License (CC BY license). Users can use, reuse and build upon the material published in this journal provided that such uses are fully attributed.
