INTEGRATED MODEL FOR SOLVING THE PROBLEM OF REQUEST DISPATCHING
Abstract
The paper considers the problem of scheduling. The paradigm of organization of distributed
computing based on Grid-computing is considered. The classification of task scheduling systems is
given. Various approaches to solving the scheduling problem are described. A model of the task of
servicing applications based on the principles of the theory of queuing systems is presented. The
task statement is formulated on the basis of Grid-scheduling. The concept of a resource rectangle
is proposed. The environment for scheduling resource rectangles is defined. A model is proposed
that allows formalizing the user's request for service by the concept of a resource (non-Euclidean)
rectangle. Instead of the principle of optimization based on the machine search for the best distribution
of the array of resource rectangles, a heuristic principle was proposed, which made it possible
to reduce the amount of necessary calculations. The proposed heuristic scheduling algorithm
makes it possible to take into account the properties of the array and evaluate the quality of solutions.
Models of the demand environment in the form of single cubic faces are constructed.
The model of cubic faces is generalized to the experiment of cubic layers. The description of the
demand model used is given. A model of the resource supply environment in the form of a canonical
pyramid is constructed and the concept of a canonical demand-supply experiment for model
homogeneous resource elements is introduced. A truncation of the supply-demand experiment has
been introduced. A hybrid model based on a combination of evolutionary search principles and
fuzzy control methods is proposed. To solve scheduling problems, it is proposed to use evolutionary
algorithms. A modified solution coding technique and new modifications of genetic operators
for solving scheduling problems have been developed. A block diagram of the algorithm for solving
the problem under consideration is presented, taking into account the use of a fuzzy logic controller.
Computer simulation has been performed and the results of computational experiments
have been presented. The features of the proposed method are revealed, its advantages and disadvantages
are formulated.
References
Kaufmann Publishers Inc., USA, 1998.
2. Cafaro M., Aloisio G. (eds.). Grids, clouds and virtualization, Computer Communications and
Networks. Springer London, 2011.
3. Yaqin Liu, Chubo Liu, Jing Liu, Yikun Hu, Kenli Li, Keqin Li Mobility-Aware and Code-
Oriented Partitioning Computation Offloading in Multi-Access Edge Computing, Journal of Grid
Computing, 2022, Vol. 20, Article number 11. Available at: https://doi.org/10.1007/s10723-022-
09599-x.
4. Jacob B., Brown M., Fukui K., Trivedi N. Introduction to Grid Computing. ibm.com/redbooks
2005.
5. Kahanwal B., Singh T. The distributed computing paradigms: p2p, grid, cluster, cloud, and
jungle, International Journal of Latest Research in Science and Technology, 2012, Vol. 1,
Issue 2, pp. 183-187.
6. Magoulès F., Nguyen T., Yu L. Grid resource management: toward virtual and services compliant
grid computing, Numerical analysis and scientific computing. CRC Press, UK, 2009.
7. Zhozhikashvili V.V., Vishnevskiy V.M. Seti massovogo obsluzhivaniya. Teoriya i primenenie k
setyam EVM [Queuing networks. Theory and application to computer networks]. Moscow:
Radio i svyaz', 1998.
8. Pinedo M. Scheduling: Theory, Algorithms and Systems. 3nd ed. Springer Verlag, New York,
2008.
9. Conway R.M., Maxwell W.L., Miller L.W. Theory of Scheduling. 2nd ed. Dover Publications,
Mineola NY, 2004.
10. Yarushkina N.G. Osnovy teorii nechetkikh i gibridnykh system [Fundamentals of the theory of
fuzzy and hybrid systems]. Moscow: Finansy i statistika, 2004.
11. Batyrshin I.Z., Nedosekin A.O. i dr. Nechetkie gibridnye sistemy. Teoriya i praktika [Fuzzy
hybrid systems. Theory and practice]. Moscow: Fizmatlit, 2007.
12. Kar А.K. Bio Inspired Computing – A Review of Algorithms and Scope of Applications, Expert
Systems with Applications, 2016, Vol. 59, pp. 20-32.
13. Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications.
Academic Press, 2020. ISBN 978-0-12-819714-1. Available at: https://doi.org/10.1016/C2019-
0-00628-0.
14. Gross D., Shortle J.F., Thompson J.M., Harris C.M. Solutions Manual to Accompany Fundamentals
of Queueing Theory. 4th Edition. John Wiley & Sons Inc., 2008.
15. Bhat Narayan U. An Introduction to Queueing Theory: Modeling and Analysis in Applications
(Statistics for Industry and Technology). Birkhäuser, 2008.
16. Gladkov L.A., Gladkova N.V., Gromov S.A. Gibridnaya model' resheniya zadach operativnogo
proizvodstvennogo planirovaniya [Hybrid model for solving operational production planning
tasks], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2018,
No. 4 (165), pp. 102-114.
17. Li M., Baker M. The grid: core technologies. John Wiley & Sons Ltd, England, 2005.
18. Saak A.E. Polinomial'nye algoritmy raspredeleniya resursov v Grid-sistemakh na osnove
kvadratichnoy tipizatsii massivov zayavok [Polynomial algorithms of resource allocation in
Grid systems based on quadratic typing of arrays of applications], Informatsionnye tekhnologii
[Information Technologies], 2013, No. 7.
19. Gladkov L.A., Kureychik V.V., Kureychik V.M. Geneticheskie algoritmy [Kureychik V.M.
Genetic algorithms]. Moscow: Fizmatlit, 2010.
20. Emel'yanov V.V., Kureychik V.V., Kureychik V.M. Teoriya i praktika evolyutsionnogo
modelirovaniya [Theory and practice of evolutionary modeling]. Moscow: Fizmatlit, 2003.
21. Gladkov L.A., Gladkova N.V., Gromov S.A. Gibridnyy algoritm resheniya zadach operativnogo
planirovaniya proizvodstvennogo protsessa [Hybrid algorithm for solving problems of operational
planning of the production process], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya
SFedU. Engineering Sciences], 2017, No. 9 (194), pp. 112-123.
22. Herrera F., Lozano M. Fuzzy Adaptive Genetic Algorithms: design, taxonomy, and future
directions, J. Soft Computing, 2003, pp. 545-562.
23. Michael A., Takagi H. Dynamic control of genetic algorithms using fuzzy logic techniques,
Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann,
1993, pp. 76-83
24. Lee M.A., Takagi H. Integrating design stages of fuzzy systems using genetic algorithms, Proceedings
of the 2nd IEEE International Conference on Fuzzy System, 1993, pp. 612-617.