21 Fractal Analysis and Programming of Elastic Systems …
319
• Integration CCM with the box-counting fractal analysis method and with fractal
control based on dynamic sampling of the workload makes it possible to introduce
fractal control of elastic system.
• Scheduling the distributed algorithm computation is implemented by setting a
capacity curve.
Future work concerns the problems of analysis and design different distributed
algorithms, patterns of organizing the data processing, scheduling the distributed
algorithm computation, and investigation of elastic system with fractal control.
References
1. Semenov, A.S.: Essentials of fractal programming. In: Jain, L.C., Favorskaya, M.N., Nikitin,
I.S., Reviznikov, D.L. (eds.) Advances in Theory and Practice of Computational Mechanics:
Proceedings of the 21st International Conference on Computational Mechanics and Modern
Applied Software Systems, SIST, vol. 173, pp. 373–386. Springer, Singapore. (2020)
2. Semenov, A.S.: Prototype based programming with fractal algebra. AIP Conf. Proc. 2181,
020009 (2019)
3. Tel, G.: Introduction to distributed algorithms. Cambridge University Press, Cambridge (2000)
4. Santoro, N.: Design and Analysis of Distributed Algorithms. Wiley Inc., New Jersey (2007)
5. Raynal, M.: Distributed Algorithms for Message-Passing Systems. Springer, Berlin Heidelberg
(2013)
6. Raynal, M.: Fault-Tolerant Message-Passing Distributed Systems. An Algorithmic Approach.
Springer, Berlin (2018)
7. Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Publishers, Inc., San Francisco,
California (1996)
8. Zhongkui, L, Zhisheng, D.: Cooperative Control of Multi-Agent Systems a Consensus Region
Approach. Taylor & Francis Group (2015)
9. Rastgoftar, H.: Continuum Deformation of Multi-Agent Systems. Springer International
Publishing AG (2016)
10. Erciyes, K.: Distributed Graph Algorithms for Computer Networks. Springer, London (2013)
11. Reisig, W.: Elements of Distributed Algorithms: Modeling and Analysis with Petri Nets.
Springer Science & Business Media (2013)
12. Semenov, A.S.: Fractal Petri nets. In: 4th International Conference on Control, Decision and
Information Technologies. Barcelona, Spain, pp. 1174–1179 (2017)
13. Oussous, A., Benjelloun, F., Lahcen, A., Belfkih, S.: Big data technologies: a survey. J. King
Saud Univ. Comput. Inf. Sci. 30(4), 431–448 (2018)
14. Harrington, P.: Machine Learning in Action. Manning Publications (2013)
15. Yanga, C., Huangb, Q., Lic, Z., Liua, K., Hua, F.: Big Data and cloud computing: innovation
opportunities and challenges. Int. J. Digit. Earth 10(1), 13–53 (2017)
16. Guo, H., Goodchild, M., Annoni, A. (eds.): Manual of Digital Earth. Springer, Singapore (2016)
17. Herbst, N. R., Kounev, S., Reussner, R.: Elasticity in cloud computing: What it is, and what it
is not. In: 10th International Conference on Autonomic Computing San Jose, CA, pp. 23–27
(2013)
18. Becker, S., Brataas, G., Lehrig, S. (eds.): Engineering Scalable, Elastic, and Cost-Efficient
Cloud Computing Applications. The CloudScale Method. Springer, Cham (2017)
19. Peitgen, H., Jurgens, H., Saupe, D.: Chaos and Fractals. New Frontiers of Science. Springer
New York, Inc, New York (2004)
20. Crownover, R.: Introduction to Fractals and Chaos. Jones and Bartlett Publishers, Inc. (1995)
319
• Integration CCM with the box-counting fractal analysis method and with fractal
control based on dynamic sampling of the workload makes it possible to introduce
fractal control of elastic system.
• Scheduling the distributed algorithm computation is implemented by setting a
capacity curve.
Future work concerns the problems of analysis and design different distributed
algorithms, patterns of organizing the data processing, scheduling the distributed
algorithm computation, and investigation of elastic system with fractal control.
References
1. Semenov, A.S.: Essentials of fractal programming. In: Jain, L.C., Favorskaya, M.N., Nikitin,
I.S., Reviznikov, D.L. (eds.) Advances in Theory and Practice of Computational Mechanics:
Proceedings of the 21st International Conference on Computational Mechanics and Modern
Applied Software Systems, SIST, vol. 173, pp. 373–386. Springer, Singapore. (2020)
2. Semenov, A.S.: Prototype based programming with fractal algebra. AIP Conf. Proc. 2181,
020009 (2019)
3. Tel, G.: Introduction to distributed algorithms. Cambridge University Press, Cambridge (2000)
4. Santoro, N.: Design and Analysis of Distributed Algorithms. Wiley Inc., New Jersey (2007)
5. Raynal, M.: Distributed Algorithms for Message-Passing Systems. Springer, Berlin Heidelberg
(2013)
6. Raynal, M.: Fault-Tolerant Message-Passing Distributed Systems. An Algorithmic Approach.
Springer, Berlin (2018)
7. Lynch, N.A.: Distributed Algorithms. Morgan Kaufmann Publishers, Inc., San Francisco,
California (1996)
8. Zhongkui, L, Zhisheng, D.: Cooperative Control of Multi-Agent Systems a Consensus Region
Approach. Taylor & Francis Group (2015)
9. Rastgoftar, H.: Continuum Deformation of Multi-Agent Systems. Springer International
Publishing AG (2016)
10. Erciyes, K.: Distributed Graph Algorithms for Computer Networks. Springer, London (2013)
11. Reisig, W.: Elements of Distributed Algorithms: Modeling and Analysis with Petri Nets.
Springer Science & Business Media (2013)
12. Semenov, A.S.: Fractal Petri nets. In: 4th International Conference on Control, Decision and
Information Technologies. Barcelona, Spain, pp. 1174–1179 (2017)
13. Oussous, A., Benjelloun, F., Lahcen, A., Belfkih, S.: Big data technologies: a survey. J. King
Saud Univ. Comput. Inf. Sci. 30(4), 431–448 (2018)
14. Harrington, P.: Machine Learning in Action. Manning Publications (2013)
15. Yanga, C., Huangb, Q., Lic, Z., Liua, K., Hua, F.: Big Data and cloud computing: innovation
opportunities and challenges. Int. J. Digit. Earth 10(1), 13–53 (2017)
16. Guo, H., Goodchild, M., Annoni, A. (eds.): Manual of Digital Earth. Springer, Singapore (2016)
17. Herbst, N. R., Kounev, S., Reussner, R.: Elasticity in cloud computing: What it is, and what it
is not. In: 10th International Conference on Autonomic Computing San Jose, CA, pp. 23–27
(2013)
18. Becker, S., Brataas, G., Lehrig, S. (eds.): Engineering Scalable, Elastic, and Cost-Efficient
Cloud Computing Applications. The CloudScale Method. Springer, Cham (2017)
19. Peitgen, H., Jurgens, H., Saupe, D.: Chaos and Fractals. New Frontiers of Science. Springer
New York, Inc, New York (2004)
20. Crownover, R.: Introduction to Fractals and Chaos. Jones and Bartlett Publishers, Inc. (1995)