Эффективное распределение вычислительных ресурсов в геораспределенных гетерогенных динамичных вычислительных средах
Работая с нашим сайтом, вы даете свое согласие на использование файлов cookie. Это необходимо для нормального функционирования сайта, показа целевой рекламы и анализа трафика. Статистика использования сайта отправляется в «Яндекс» и «Google»
Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Efficient resource allocation in geodistributed heterogeneous dynamic computing environments

idKlimenko A.B.

UDC 004.023+004.891.2
DOI: 10.26102/2310-6018/2024.47.4.011

  • Abstract
  • List of references
  • About authors

Currently, the management of computing resources in geo-distributed heterogeneous dynamic computing environments is a non-trivial scientific problem. Due to the complexity of such systems, the distribution of computing resources becomes a computationally hard problem, usually multi-criteria, with nonlinear constraints, integer or mixed-integer. The solution of such problems produces some additional costs of system exploitation. In addition, the property of geo-distribution also introduces additional resource costs that arise during data transit between computing subtasks in the case when transit sections of the network are involved and the route length is more than one section. The purpose of this study is to implement effective management of computing resources based on the criterion of using computing resources – both in the process of their distribution and in solving a computational task in a computing environment. To achieve the goal of the study, a new formulation of the computational resource distribution problem has been developed, which takes into account the properties of heterogeneity, dynamics and geo-distribution of the computing environment and is distinguished by the presence of controlled parameters that determine the resource costs both for data transmission over the network and for solving the computational resource distribution problem. A method has been developed that allows solving the formulated problem, which includes the stages of developing a metaheuristic repository and its use. The results of the conducted modeling allow us to conclude that the developed method is promising – the computing resource usage for resources distribution has decreased by 28 times with a loss in the quality of the resulting solution of up to 10%.

1. Klimenko A.B. Problem statement and methods for the computing resources distribution in geo-distributed heterogeneous computing environments with dynamics and restrictions on the execution time of tasks. Journal of Applied Informatics. 2024;19(4):48–67. (In Russ.). https://doi.org/10.37791/2687-0649-2024-19-4-48-67

2. Sukhoroslov O., Gorokhovskii M. Benchmarking DAG Scheduling Algorithms on Scientific Workflow Instances. In: Supercomputing: Revised Selected Papers: Part II: 9th Russian Supercomputing Days, RuSCDays 2023, 25–26 September 2023, Moscow, Russia. Cham: Springer; 2023. pp. 3–20. https://doi.org/10.1007/978-3-031-49435-2_1

3. Kalyaev I.A., Kalyaev A.I. Method and Algorithms for Adaptive Multiagent Resource Scheduling in Heterogeneous Distributed Computing Environments. Automation and Remote Control. 2022;83(8):1228–1245. https://doi.org/10.1134/s0005117922080069

4. Heba M.F. Optimizing Task Scheduling and Resource Allocation in Computing Environments using Metaheuristic Methods. Fusion: Practice and Applications. 2024;15(1):157–179. https://doi.org/10.54216/FPA.150113

5. Narwal A. Resource Utilization Based on Hybrid WOA-LOA Optimization with Credit Based Resource Aware Load Balancing and Scheduling Algorithm for Cloud Computing. Journal of Grid Computing. 2024;22(3). https://doi.org/10.1007/s10723-024-09776-0

6. Hussain M., Nabi S., Hussain M. RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environment. Cluster Computing. 2024;27:13353–13377. https://doi.org/10.1007/s10586-024-04612-2

7. Behera S.R., Panigrahi N., Bhoi S.K., Sahoo K.S., Jhanjhi N.Z., Ghoniem R.M. Time Series-Based Edge Resource Prediction and Parallel Optimal Task Allocation in Mobile Edge Computing Environment. Processes. 2023;11(4). https://doi.org/10.3390/pr11041017

8. Dankolo N.M.D., Radzi N.H.M., Mustaffa N.H., Talib M.Sh., Yunos Z.M., Gabi D. Efficient Task Scheduling Approach in Edge-Cloud Continuum Based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm. Baghdad Science Journal. 2024;21(2). https://doi.org/10.21123/bsj.2024.10084

9. Abdel-Basset M., Mohamed R., Abd Elkhalik W., Sharawi M., Sallam K.M. Task Scheduling Approach in Cloud Computing Environment Using Hybrid Differential Evolution. Mathematics. 2022;10(21). https://doi.org/10.3390/math10214049

10. Mishra A.K., Mohapatra S., Sahu P.K. Adaptive Tasmanian Devil Optimization algorithm based efficient task scheduling for big data application in a cloud computing environment. Multimedia Tools and Applications. 2024. https://doi.org/10.1007/s11042-024-19887-1

11. Barskii A.B. Parallel'noe programmirovanie. Moscow: Natsional'nyi Otkrytyi Universitet "INTUIT"; 2016. 345 p. (In Russ.).

12. Toporkov V.V. Modeli raspredelennykh vychislenii. Moscow: Fizmatlit; 2004. 320 p. (In Russ.).

13. Sadeg S., Hamdad L., Kada O., Benatchba K., Habbas Z. Meta-learning to Select the Best Metaheuristic for the MaxSAT Problem. In: Modelling and Implementation of Complex Systems: Proceedings of the 6th International Symposium, MISC 2020, 24–26 October 2020, Batna, Algeria. Cham: Springer; 2020. pp. 122–135. https://doi.org/10.1007/978-3-030-58861-8_9

14. Kärcher J., Meyr H. A machine learning approach for predicting the best solution heuristic for a large scaled Capacitated Lotsizing Problem. Research Square. 2023. https://doi.org/10.21203/rs.3.rs-3709286/v1

15. Aksoy İ.C., Mutlu M.M. Comparing the performance of metaheuristics on the Transit Network Frequency Setting Problem. Journal of Intelligent Transportation Systems. 2024. https://doi.org/10.1080/15472450.2024.2392722

Klimenko Anna Borisovna
Ph.D
Email: anna_klimenko@mail.ru

ORCID |

IT and ST Institute

Moscow, Russia

Keywords: resource allocation, distributed computing, distributed computing management, dynamic computing environment, optimization, metaheuristics

For citation: Klimenko A.B. Efficient resource allocation in geodistributed heterogeneous dynamic computing environments. Modeling, Optimization and Information Technology. 2024;12(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1704 DOI: 10.26102/2310-6018/2024.47.4.011 (In Russ).

98

Full text in PDF

Received 01.10.2024

Revised 17.10.2024

Accepted 21.10.2024