COMPOSITE APPLICATION DISTRIBUTION METHODS MODELING
Article Sidebar
Main Article Content
Abstract
The subject of consideration are algorithms for optimal distribution of existing pool of computing resources between composite applications and algorithm of utilization of resources on computing blocks. The purpose of the article is to analyze the results of simulation and mathematical modeling of the resource allocation process between composite applications, depending on the distribution option. Results The efficiency of existing dynamic planning algorithms that are related to the greedy algorithm class is considered. They find a locally optimal solution at each step. The boundary of effective planning of algorithms based on clustering approach is revealed. The efficiency of using ant colony optimization algorithm and algorithms of cluster approach using ant colony optimization algorithm is shown. The simulation of the distribution of the composite application is carried out, depending on the complexity of the graph construction. The dependence of the execution time of the composite application on utilization of resources on the calculated blocks is obtained. Using the resource utilization function, the quality of the distribution of composite application resources is analyzed, depending on the amount of data transferred to the calculations. Conclusions. Data on the quality of resource allocation is obtained, depending on such parameters as the time of implementation of the composite application, the volume of transmitted data, the complexity of the graph construction. A method for choosing the optimal resource allocation algorithm between composite applications depending on the listed parameters is proposed. This will allow you to quickly dispose of distributed computing blocks that are occupied by calculating a distributed task, which will speed up the computation of distributed tasks on an existing pool of computing blocks.
Article Details
References
Bulba, S.S., Davydov, V.V. and Kuchuk G.А. (2018), “Method for Resource Distribution between Composite Applications”, Control, navigation and communication systems, PNTU, Poltava, No. 4 (50), pp. 99-104.
Lord P. and Goble C. (2005), “Seven Bottlenecks to Workflow Reuse and Repurposing Sattler”, The Semantic Web – ISW 2005, pp. 323-337.
Merlac, V., Smatkov, S., Kuchuk, N. and Nechausov A. (2018), “Resourses Distribution Method of University e-learning on the Hypercovergent platform”, Сonference Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Service and Technologies. DESSERT’2018, Ukraine, Kyiv, May 24-27, pp. 136-140.
Kuchuk, G., Kharchenko, V., Kovalenko, A., Ruchkov, E. (2016), “Approaches to Selection of Combinatorial Algorithm for Optimization in Network Traffic Control of Safety-Critical Systems”, Proceeding of IEEE East-West Design & Test Symposium (EWDTS’2016), pp. 384-389, available at: http://dx.doi.org/10.1109/EWDTS.2016.7807655 (last accessed on July 16, 2018).
Kuchuk, G., Nechausov, S., Kharchenko, V. (2015), “Two-stage optimization of resource allocation for hybrid cloud data store”, International Conference on Information and Digital Technologies, pp. 266-271, available at:
http://dx.doi.org/10.1109/DT.2015.7222982 (last accessed on July 16, 2018).
Kovalchuk S.V. and Bukhanovsky A.V. (2012), “Second Generation Cloud Computing: Composite Applications, Interactive Systems and Semantic Technologies”, Infocommunication Technologies, Tarusa, available at:
http://keepslide.com/technology/8702#sthash.YHi3I5Gy.dpuf (last accessed on July 16, 2018).
Svyrydov, A., Kuchuk, H., Tsiapa, O. (2018), “Improving efficienty of image recognition process: Approach and case study”, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593-597, available at: http://dx.doi.org/10.1109/DESSERT.2018.8409201 (last accessed on July 16, 2018).
Bulba, S.S. (2016), "Resource-oriented mathematical model of the basic network of a heterogeneous distributed system", Control, navigation and communication systems, PNTU, Poltava, No. 2 (38), pp. 73-75.
Dorigo, M., Di Caro G. & Gambardella, L.M. (1999), “Ant Algorithms for Discrete Optimization”, Artificial Life, No. 5 (2), pp. 137-172.
Khaidukov D.S. (2009), Application of cluster analysis, MAKS Press, Moscow, 287 p.