moitvivt Моделирование, оптимизация и информационные технологии Modeling, Optimization and Information Technology 2310-6018 Издательство 10.26102/2310-6018/2023.43.4.002 1439 Обобщенная экологическая модель динамической распределенной вычислительной системы Generalized ecological model of a dynamic distributed computing system 0000-0001-6176-837X Брюханова Евгения Романовна Bryukhanova Evgeniia Romanovna evgbryuhanova@gmail.com aff-1 0000-0002-5976-5847 Антамошкин Олеслав Александрович Antamoshkin Oleslav Aleksandrovich oleslav24@gmail.com aff-2 Сибирский государственный университет науки и технологий им. М.Ф. Решетнева Сибирский федеральный университет Reshetnev Siberian State University of Science and Technology Siberian Federal University Сибирский федеральный университет Сибирский государственный университет науки и технологий им. М.Ф. Решетнева Siberian Federal University Reshetnev Siberian State University of Science and Technology 01 01 2026 1 1 10.26102/2310-6018/2023.43.4.002 Copyright © Авторы, 2026 2026 This work is licensed under a Creative Commons Attribution 4.0 International License

В работе представлена обобщенная модель, позволяющая проводить структурный анализ распределенной вычислительной динамической системы, а также исследовать применимость различных методов управления с учетом параметров экологичности ее работы. С приходом эпохи информационного общества интенсивность использования распределенных вычислительных систем для обработки данных и выполнения разнообразных задач безостановочно растет. Однако с ростом их количества и масштабов остро встаеют вопросы энергопотребления и негативного воздействия на окружающую среду. Предложенная модель предоставляет инструментарий для оценки воздействия таких систем на окружающую среду, а также для принятия мер по минимизации их экологического следа. Она включает в себя комплекс параметров, позволяющих анализировать и учитывать факторы, такие как энергопотребление, выбросы углерода и эффективность использования ресурсов. Данная модель призвана способствовать развитию более экологически позитивных подходов к управлению распределенными вычислительными системами. Это имеет особую важность в свете нарастающего внимания к экологической проблематике и стремления общества к более ответственному использованию ресурсов. Результаты данного исследования открывают путь к созданию более эффективных и экологически дружественных вычислительных решений, способствуя снижению негативного воздействия на окружающую среду и более устойчивому будущему, обеспечивая баланс между производительностью и экологичностью распределенных систем вычислений.

The paper presents a generalized model that enables a structural analysis of a distributed computational dynamic system and makes it possible to investigate the applicability of various control methods taking into account the environmental parameters of its operation. With the advent of the information society era, distributed computing systems for data processing and performing various tasks are being increasingly used. However, with the growth of their number and scale, the issues of energy consumption and negative impact on the environment are becoming more acute. The proposed model provides tools for assessing the impact of such systems on the environment as well as for taking measures to minimize their ecological footprint. It includes a set of parameters that help to analyze and take into account such factors as energy consumption, carbon emissions and resource efficiency. This model is designed to promote the development of more environmentally positive approaches to the management of distributed computing systems. This is of particular importance in the light of the growing attention to environmental issues and the desire of society for a more responsible use of resources. The results of this study open the way to creating more efficient and environmentally friendly computing solutions reducing the negative impact on the environment and a more sustainable future ensuring a balance between performance and environmental friendliness of distributed computing systems.

 распределенные вычислительные системы динамические системы экологическая устойчивость энергопотребление оптимизация distributed computing systems dynamic systems environmental sustainability energy consumption optimization Исследование выполнено без спонсорской поддержки. The study was performed without external funding. References Dubey K., Kumar M. Sharma S. Modified HEFT algorithm for task scheduling in cloud environment. Procedia Computer Science. 2018;125:725–732. DOI: 10.1016/j.procs.2017.12.093. Mondal R., Nandi E., Sarddar D. Load balancing scheduling with shortest load first. International Journal of Grid and Distributed Computing. 2015;8:171–178. DOI: 10.14257/ijgdc.2015.8.4.17. Lakra A.V., Yadav D.K. Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. Procedia Computer Science. 2015;48:107–113. DOI: 10.1016/j.procs.2015.04.158. Wang H., Wang F., Liu J., Wang D., Groen J. Enabling customer-provided resources for cloud computing: Potentials, challenges, and implementation. IEEE Transactions on Parallel and Distributed Systems. 2015;26:1874–1886. Gill S.S., Chana I., Singh M., Buyya R. CHOPPER: An intelligent QoS-aware autonomic resource management approach for cloud computing. Cluster Computing. 2018;21:1203–1241. DOI: 10.1007/s10586-017-1040-z. Thomas A., Krishnalal G., Raj P.V. Credit based scheduling algorithm in cloud computing environment. Procedia Computer Science. 2015;46:913–920. DOI: 10.1016/j.procs.2015.02.162. Sajid M., Raza, Z. Turnaround time minimization-based static scheduling model using task duplication for fine-grained parallel applications onto hybrid cloud environment. IETE Journal of Research. 2015;62(3):1–13. DOI: 10.1080/03772063.2015.1075911. Hadji M., Zeghlache D. Minimum cost maximum flow algorithm for dynamic resource allocation in clouds. 2012 IEEE 5th International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA. 2012. p. 876-882. DOI: 10.1109/CLOUD.2012.36. Elzeki O., Reshad M., Abu Elsoud, M. Improved Max-Min Algorithm in Cloud Computing. International Journal of Computer Applications. 2012;50(12):22–27. DOI: 10.5120/7823-1009. Fernández Cerero D., Fernández-Montes A., Jakóbik A., Kołodziej J., Toro M. SCORE: Simulator for cloud optimization of resources and energy consumption. Simulation Modelling Practice and Theory. 2018;82:160–173. DOI: 10.1016/j.simpat.2018.01.004. Ma T., Chu Y., Zhao L., Otgonbayar A. Resource allocation and scheduling in cloud computing: policy and algorithm. IETE Technical Review. 2014;31(1):4–16. DOI: 10.1080/02564602.2014.890837. Carrasco R., Iyengar G., Stein C. Resource cost aware scheduling. European Journal of Operational Research. 2018;269(2):621–632. DOI: 10.1016/j.ejor.2018.02.059. 1Coninck E., Verbelen T., Vankeirsbilck B., Bohez S., Simoens P., Dhoedt, B. Dynamic auto-scaling and scheduling of deadline constrained service workloads on IaaS clouds. Journal of Systems and Software. 2016;118:101–114. DOI: 10.1016/j.jss.2016.05.011. Yi P., Ding H., Ramamurthy B. Budget-minimized resource allocation and task scheduling in distributed grid/clouds. 2013 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS), Nassau, Bahamas. 2013. p. 1–8. DOI: 10.1109/ANTS.2013.6802891. Reddy G. A deadline and budget constrained cost and time optimization algorithm for cloud computing. Commun. Comput. Inf. Sci. 2011;193:455–462. Xin Y., Xie Z.Q., Yang J. A load balance oriented cost efficient scheduling method for parallel tasks. Journal of Network and Computer Applications. 2018;81:37–46. DOI: 10.1016/j.jnea.2016.12.032. Yang S.J., Chen Y.R. Design adaptive task allocation scheduler to improve MapReduce performance in heterogeneous Clouds. Journal of Network and Computer Applications. 2015;57:61–70. DOI: 10.1016/j.jnca.2015.07.012. Li Z., Chang V., Hu Haiyang, Hu Hua. Real-time and dynamic fault-tolerant scheduling for scientific workflows in Clouds. Information Science. 2021;568(12). DOI: 10.1016/j.ins.2021.03.003. Zhou Z., Abawajy J., Chowdhury M., Hu Z., Li K., Cheng H., Alelaiwi A., Li F. Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Future Generation Computer Systems. 2017;86:836–850. DOI: 10.1016/j.future.2017.07.048. Pradhan R., Satapathy S. Energy-aware cloud task scheduling algorithm in heterogeneous multi-cloud environment. Intelligent Decision Technologies. 2022;16(8):1–6. DOI: 10.3233/IDT-210048. Bryukhanova E.R., Antamoshkin O.A. Minimizing the carbon footprint with the use of zeroing neural networks. The European Proceedings of Computers and Technology. 2023. DOI: 10.15405/epct.23021.20. Duan H., Chen C., Min G., Wu Y. Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Future Generation Computer Systems. 2017;74:142–150. DOI: 10.1016/j.future.2016.02.016. Shaikh M.B., Waghmare Shinde K., Borde S. Challenges of big data processing and scheduling of processes using various Hadoop schedulers: a survey. Int. J. Multifaceted Multiling. Stud. 2019;III:1–6. Reddy G., Kumar S. MACO-MOTS: Modified ant colony optimization for multi objective task scheduling in cloud environment. International Journal of Intelligent Systems and Applications. 2019;11(1):73–79. DOI: 10.5815/ijisa.2019.01.08. Biswas D., Samsuddoha M., Asif M.R.A., Ahmed M.M. Optimized round robin scheduling algorithm using dynamic time quantum approach in cloud computing environment. International Journal of Intelligent Systems and Applications. 2023;15(1):22–34. DOI: 10.5815/ijisa.2023.01.03. Soltani N., Barekatain B., Soleimani Neysiani B. MTC: Minimizing time and cost of cloud task scheduling based on customers and providers needs using genetic algorithm. I.J. Intelligent Systems and Applications. 2021;2:38–51. DOI: 10.5815/ijisa.2021.02.03. Mohseni Z., Kiani V., Rahmani A. A Task scheduling model for multi-CPU and multi-hard disk drive in soft real-time systems. International Journal of Information Technology and Computer Science. 2019;11(1):1–13. DOI: 10.5815/ijitcs.2019.01.01. Zaharia M., Borthakur D., Sen Sarma J., Elmeleegy K., Shenker S., Stoica I. Delay scheduling: a simple technique for achieving locality and fairness in cluster scheduling. European Conference on Computer Systems, Proceedings of the 5th European conference on Computer systems, EuroSys 2010, April 13–16 2010, Paris, France. p. 265–278. DOI: 10.1145/1755913.1755940. Bouhouch L., Zbakh M., Tadonki C. Dynamic data replication and placement strategy in geographically distributed data centers. Concurrency and Computation Practice and Experience. 2022;35(11). DOI: 10.1002/cpe.6858. Samadi Y., Zbakh M., Tadonki C. DT-MG: Many-to-one matching game for tasks scheduling towards resources optimization in cloud computing. International Journal of Computers and Applications. 2020;43(6):1–13. DOI: 10.1080/1206212X.2018.1519630.

The authors declare that there are no conflicts of interest present.