Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

Ziyi Gao; Linze Huang; Zhigang Wu; Zhenyan Wu; Chunhui Li

doi:10.4108/ew.7728

Authors

Ziyi Gao Guangdong Power Grid Co.
Linze Huang Guangdong Power Grid Co.
Zhigang Wu Guangdong Power Grid Co.
Zhenyan Wu Guangdong Power Grid Co.
Chunhui Li Guangdong Power Grid Co.

DOI:

https://doi.org/10.4108/ew.7728

Keywords:

Energy Optimization, Fuzzy Allocation, Data Centers, High-Density Storage, Green Technology

Abstract

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.

Downloads

Download data is not yet available.

References

[1] C. Huang, J. An, Q. Liu, L. Yin, and W. Wang, “Research on Fuzzy Energy Management Strategy of Hybrid Energy Storage System for Novel Power System,” J Phys Conf Ser, vol. 2527, no. 1, p. 012019, Jun. 2023, doi: 10.1088/1742-6596/2527/1/012019.

[2] M. Barukcic, T. Varga, T. Bensic, and V. J. Stil, “Research on node voltage indices for battery storage management through fuzzy decision making in power distribution networks,” in 2022 IEEE 7th International Energy Conference (ENERGYCON), IEEE, May 2022, pp. 1–6. doi: 10.1109/ENERGYCON53164.2022.9830192.

[3] J. K. Samriya, R. Tiwari, M. S. Obaidat, and G. Bathla, “Fuzzy-EPO Optimization Technique for Optimised Resource Allocation and Minimum Energy Consumption with the Brownout Algorithm,” Wirel Pers Commun, vol. 129, no. 4, pp. 2633–2651, Apr. 2023, doi: 10.1007/s11277-023-10250-5.

[4] M. Ahmed, M. Khatri, F. Ahmed, and J. Goyal, “An Optimized Fuzzy-based Load Balancing in Cloud Computing,” in 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), IEEE, May 2023, pp. 323–328. doi: 10.1109/REEDCON57544.2023.10150583.

[5] K.-Y. Tai, F. Y.-S. Lin, and C.-H. Hsiao, “An Integrated Optimization-Based Algorithm for Energy Efficiency and Resource Allocation in Heterogeneous Cloud Computing Centers,” IEEE Access, vol. 11, pp. 53418–53428, 2023, doi: 10.1109/ACCESS.2023.3280930.

[6] E. Ivokhin, L. Adzhubey, P. Vavryk, and M. Makhno, “On some methods for solving the problem of power distribution of data transmission channels taking into account fuzzy constraints on consumption volumes,” System research and information technologies, no. 4, pp. 88–99, Dec. 2022, doi: 10.20535/SRIT.2308-8893.2022.4.08.

[7] S. Luo, C. Xia, J. Zhang, R. Xia, and Y. Zhu, “Summary of Research on Optimal Allocation of Energy Storage in the Distribution Network,” in 2022 9th International Conference on Electrical and Electronics Engineering (ICEEE), IEEE, Mar. 2022, pp. 222–226. doi: 10.1109/ICEEE55327.2022.9772560.

[8] S. Pan, R. Zhao, C. Huang, H. Wang, and Z. Shi, “Research on Energy Consumption Optimization Using a Lyapunov-Based LSTM-PSO Algorithm,” 2024, pp. 15–28. doi: 10.1007/978-981-97-1010-2_2.

[9] S. Leng, “Research on Electronic Communication Information Storage Based on Ranking Optimization Algorithm,” in 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC), IEEE, Jan. 2024, pp. 1–4. doi: 10.1109/ICOCWC60930.2024.10470839.

[10] X. Wang, “Fuzzy Decoupling Energy Efficiency Optimization Algorithm in Cloud Computing Environment,” International Journal of Information Technologies and Systems Approach, vol. 14, no. 2, pp. 52–69, Jul. 2021, doi: 10.4018/IJITSA.2021070104.

[11] H. Gu, J. Wu, W. Hu, and T. Ma, “Research on low energy task allocation and scheduling algorithm based on imprecise heterogeneous multi-core technology,” in 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Oct. 2022, pp. 2613–2619. doi: 10.1109/SMC53654.2022.9945120.

[12] A. Khandelwal, S. Saxena, and A. Kumar, “OPTIMIZATION OF FUZZY ASSIGNMENT PROBLEM USING R,” jnanabha, vol. 52, no. 02, pp. 58–67, 2022, doi: 10.58250/jnanabha.2022.52206.

[13] L. Raskin and L. Sukhomlyn, “OPTIMIZATION OF RESOURCE DISTRIBUTION UNDER THE CONDITIONS OF FUZZY INITIAL DATA,” Bulletin of National Technical University “KhPI”. Series: System Analysis, Control and Information Technologies, no. 1 (7), pp. 9–15, Jul. 2022, doi: 10.20998/2079-0023.2022.01.02.

[14] M. Sivaram et al., “RETRACTED ARTICLE: Secure storage allocation scheme using fuzzy based heuristic algorithm for cloud,” J Ambient Intell Humaniz Comput, vol. 12, no. 5, pp. 5609–5617, May 2021, doi: 10.1007/s12652-020-02082-z.

[15] E. Gur and Z. Zalevsky, “CPU and Memory Allocation Optimization using Fuzzy Logic Based Clustering,” in 2023 IEEE 18th Conference on Industrial Electronics and Applications (ICIEA), IEEE, Aug. 2023, pp. 1821–1826. doi: 10.1109/ICIEA58696.2023.10241904.

[16] E. Arianyan, H. Taheri, and V. Khoshdel, “Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers,” Journal of Network and Computer Applications, vol. 78, pp. 43–61, Jan. 2017, doi: 10.1016/j.jnca.2016.09.016.

[17] M. Suguna, R. Logesh, and C. U. Om Kumar, “Fuzzy‐Based Efficient Resource Allocation and Scheduling in a Computational Distributed Environment,” in Cognitive Analytics and Reinforcement Learning, Wiley, 2024, pp. 189–202. doi: 10.1002/9781394214068.ch10.

[18] R. Bala and J. Mann, “A Research Paper on Green Computing Using Energy Efficient Task Allocation Strategy in Cloud Environment,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 7, no. 6, pp. 186–191, Jun. 2017, doi: 10.23956/ijarcsse/V7I6/0248.

[19] M. Seddiki, R. P. de Prado, J. E. Munoz-Expósito, and S. García-Galán, “Fuzzy Rule-Based Systems for Optimizing Power Consumption in Data Centers,” 2014, pp. 301–308. doi: 10.1007/978-3-319-01622-1_34.

[20] D. Adami, A. Gabbrielli, S. Giordano, M. Pagano, and G. Portaluri, “A Fuzzy Logic Approach for Resources Allocation in Cloud Data Center,” in 2015 IEEE Globecom Workshops (GC Wkshps), IEEE, Dec. 2015, pp. 1–6. doi: 10.1109/GLOCOMW.2015.7414107.

[21] J. Sun, Z. Yang, Z. Guo, and Q. Yang, “Long Term Operational Optimization of Data Center Network Under Uncertainty,” in 2018 IEEE 14th International Conference on Control and Automation (ICCA), IEEE, Jun. 2018, pp. 522–527. doi: 10.1109/ICCA.2018.8444191.

[22] J. Dong-han and L. Hai-tao, “Research on a resource allocation algorithm based on energy conservation,” in 2017 3rd IEEE International Conference on Computer and Communications (ICCC), IEEE, Dec. 2017, pp. 569–572. doi: 10.1109/CompComm.2017.8322609.

[23] S. R. Jayasimha, J. Usha, and S. G. Srivani, “Efficient Power Management using Fuzzy Logic for Cloud Computing Environment,” in 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS), IEEE, Dec. 2018, pp. 35–40. doi: 10.1109/CSITSS.2018.8768764.

[24] Zs. Danyadi, P. Foldesi, and L. T. Koczy, “Solution of a fuzzy resource allocation problem by various evolutionary approaches,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE, Jun. 2013, pp. 807–812. doi: 10.1109/IFSA-NAFIPS.2013.6608504.

[25] S. Chen, Y. Wang, and M. Pedram, “Resource allocation optimization in a data center with energy storage devices,” in IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Oct. 2014, pp. 2604–2610. doi: 10.1109/IECON.2014.7048873.