EFFICIENCY METRICS FOR COMPUTATIONAL RESOURCES IN CLOUD ENVIRONMENTS FOR MICROSERVICE ARCHITECTURES

Authors

  • Dmytro Murzha Simon Kuznets Kharkiv National University of Economics

DOI:

https://doi.org/10.34132/mspc2025.01.06.23

Keywords:

microservices, cloud computing, efficiency metrics, multi-objective optimization, energy efficiency, economic efficiency.

Abstract

The thesis presents a systematization and analysis of multidimensional efficiency metrics for microservices in cloud environments. Technical, economic, and environmental aspects of efficiency, their interrelationships, and trade-offs are examined. A conceptual multi-level model for efficiency assessment is proposed, along with approaches to multi-objective resource optimization. Special attention is paid to modeling a decision-making system that considers conflicting optimization goals at different architecture levels – from an individual microservice to data center infrastructure.

References

A. Bogner et al., “On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study,” Applied Sciences, vol. 11, no. 17, 2021. DOI: 10.3390/app11177856. Retrieved from: https://www.mdpi.com/2076-3417/11/17/7856

K. Toczé et al., “SoK: Microservice Architectures from a Dependability Perspective,” arXiv, 2025. DOI: 10.48550/arXiv.2503.03392. Retrieved from: https://arxiv.org/abs/2503.03392

A. Sorgalla et al., “Architectural Tactics to Improve the Environmental Sustainability of Microservices: A Rapid Review,” arXiv, 2024. DOI: 10.48550/arXiv.2407.16706. Retrieved from: https://arxiv.org/abs/2407.16706v1

D. K. Pandiya, “Performance Analysis of Microservices Architecture in Cloud Environments,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 12, 2022. Retrieved from: https://ijritcc.org/index.php/ijritcc/article/view/10745

M. H. Fourati et al., “Cloud Elasticity of Microservices-based Applications: A Survey,” February 2024. DOI: 10.21203/rs.3.rs-3925329/v1. Retrieved from: https://www.researchgate.net/publication/378053513_Cloud_Elasticity_of_Microservices-based_Applications_A_Survey

I. Papakonstantinou et al., “A Techno-Economic Assessment of Microservices,” Conference on Network and Service Management (CNSM), 2020. DOI: 10.23919/CNSM50824.2020.9269114. Retrieved from: https://www.semanticscholar.org/paper/A-Techno-Economic-Assessment-of-Microservices-Papakonstantinou-Kalafatidis/39a533d5a8e3e91b3fb580d3d91df02e02a1743d

S. Arshad et al., “A Survey of Cloud Computing Variable Pricing Models,” 10th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE 2015), Barcelona, Spain, pp. 27-32, 2015. DOI: 10.5220/0005429900270032. Retrieved from: https://www.researchgate.net/publication/277341823_A_Survey_of_Cloud_Computing_Variable_Pricing_Models

J. Chen et al., “A multi-objective optimization for resource allocation of emergent demands in cloud computing,” Journal of Cloud Computing, vol. 10, no. 1, 2021. DOI: 10.1186/s13677-021-00237-7. Retrieved from: https://www.researchgate.net/publication/349707086_A_multi-objective_optimization_for_resource_allocation_of_emergent_demands_in_cloud_computing

J. Wang et al., “Peeling Back the Carbon Curtain: Carbon Optimization Challenges in Cloud Computing,” HotCarbon ‘23: Proceedings of the 2nd Workshop on Sustainable Computer Systems, Article No. 8, pp. 1-7, 2023. DOI: 10.1145/3604930.3605718. Retrieved from: https://dl.acm.org/doi/abs/10.1145/3604930.3605718

D. Patterson et al., “Carbon Emissions and Large Neural Network Training,” arXiv, 2021. DOI: 10.48550/arXiv.2104.10350. Retrieved from: https://arxiv.org/abs/2104.10350

“Green Computing Reduces Data Centers’ Carbon Footprint,” Sustainability, January 2023. Retrieved from: https://greenexdc.com/green-computing-reduces-data-centers-carbon-footprint/

S. Bharany et al., “A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing,” Sustainability, vol. 14, no. 10, 2022. DOI: 10.3390/su14106256. Retrieved from: https://www.mdpi.com/2071-1050/14/10/6256

B. Tan et al., “A NSGA-II-based Approach for Multi-objective Micro-service Allocation in Container-based Clouds,” IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020. DOI: 10.1109/CCGrid49817.2020.00-65. Retrieved from: https://www.researchgate.net/publication/342929909_A_NSGA-II-based_Approach_for_Multi-objective_Micro-service_Allocation_in_Container-based_Clouds

H. X. Nguyen et al., “A Survey on Graph Neural Networks for Microservice-Based Cloud Applications,” Sensors, vol. 22, no. 23, 2022. DOI: 10.3390/s22239492. Retrieved from: https://www.mdpi.com/1424-8220/22/23/9492

Published

2025-05-20