Informatics. Economics. Management

Adaptive resource management algorithm in distributed dynamic computing systems based on dynamic frequency and voltage control

E. R. Briukhanova

https://orcid.org/0000-0001-6176-837X

DOI: https://doi.org/10.47813/2782-5280-2023-2-4-0201-0208

Keywords: dynamic resource management, distributed dynamic computing systems, energy consumption, performance, time constraints, task execution frequency, resource optimization, energy conservation, processor resources, dynamic adaptation, resource efficiency

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Abstract

This study focuses on developing effective resource management methods in distributed dynamic computing systems, emphasizing energy consumption reduction and ensuring task completion within specified time limits. Addressing contemporary demands in computing systems where energy efficiency and performance are paramount, a new resource management approach is proposed, based on dynamically adjusting task execution frequencies according to time intervals between the completion of one task and the start of the next one on local processor cores. This paper presents an algorithm for dynamic resource management in distributed dynamic computing systems, allowing the optimization of system energy consumption while considering task completion time constraints and ensuring optimal utilization of processor resources. The proposed resource management method achieves a balance between performance and energy conservation by adapting the system to changing work conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its applicability in various scenarios of distributed dynamic computing system operations. The developed algorithm offers promising opportunities for optimizing the functioning of computing systems, considering the dynamic nature of modern computing tasks and limited energy consumption resources.  

 

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Author Biography

E. R. Briukhanova

Evgeniia Briukhanova, Postgraduate Student, Reshetnev Siberian State University of Science and Technology, research engineer, Siberian Federal University, Krasnoyarsk, Russian Federation.

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References

Antamoshkin O., Bryukhanova E.. Minimizing the Carbon Footprint With the Use of Zeroing Neural Networks. Hybrid Methods of Modeling and Optimization in Complex Systems. European Proceedings of Computers and Technology. 2022; 1: 160-166. https://doi.org/10.15405/epct.23021.20

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

Coninck 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). 2013; 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

REFERENCES

Antamoshkin O., Bryukhanova E.. Minimizing the Carbon Footprint With the Use of Zeroing Neural Networks. Hybrid Methods of Modeling and Optimization in Complex Systems. European Proceedings of Computers and Technology. 2022; 1: 160-166. https://doi.org/10.15405/epct.23021.20 DOI: https://doi.org/10.15405/epct.23021.20

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 DOI: https://doi.org/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 DOI: https://doi.org/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 DOI: https://doi.org/10.1016/j.ejor.2018.02.059

Coninck 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 DOI: https://doi.org/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). 2013; 1-8. DOI:10.1109/ANTS.2013.6802891 DOI: https://doi.org/10.1109/ICCCN.2013.6614111

Reddy G. A Deadline and Budget Constrained Cost and Time Optimization Algorithm for Cloud Computing. Commun. Comput. Inf. Sci. 2011; 193: 455-462. DOI: https://doi.org/10.1007/978-3-642-22726-4_47

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 DOI: https://doi.org/10.1016/j.jnca.2016.12.032