Quantum-Inspired Meta-Blockchain Consensus Algorithm for Green Cloud Data Centers Optimizing Energy and Latency Trade-Offs
DOI:
https://doi.org/10.70062/globalscience.v1i3.178Keywords:
Blockchain Technology, Cloud Data, Consensus Algorithm, Energy Efficiency, Quantum ComputingAbstract
The increasing demand for cloud computing services has led to the rapid expansion of cloud data centers, which consume significant amounts of energy and contribute substantially to global CO2 emissions. As the IT industry grows, the environmental impact of these data centers becomes an urgent concern. Green Cloud Computing (GCC) has emerged as a solution to mitigate this impact by focusing on energy efficiency and reducing carbon footprints while maintaining the necessary functionality and performance of cloud infrastructures. However, traditional blockchain consensus algorithms such as Proof of Work (PoW) and Proof of Stake (PoS) face limitations regarding energy consumption and scalability, which exacerbates the environmental burden. This study proposes a quantum-inspired blockchain consensus algorithm designed to optimize energy consumption and reduce latency in cloud data centers. By integrating quantum principles such as superposition and entanglement, the algorithm enhances task scheduling and resource utilization, enabling more energy-efficient operations without sacrificing performance. Simulations in a green cloud environment showed that the quantum-inspired algorithm resulted in up to a 30% reduction in energy usage compared to traditional consensus methods, with a 40% improvement in consensus processing time. These results suggest that quantum-inspired algorithms hold significant potential for enhancing the sustainability of cloud infrastructures by improving energy efficiency and scalability. Furthermore, this study discusses the feasibility of implementing quantum-inspired algorithms on classical hardware, addressing challenges in scalability and integration into existing blockchain frameworks. The findings provide valuable insights into the potential of quantum-inspired technologies to drive energy-efficient solutions in cloud computing.
References
Agrawal, M., & Jain, A. (2021). Study on green cloud computing-A review. In Machine Learning Approach for Cloud Data Analytics in IoT. https://doi.org/10.1002/9781119785873.ch12
Ahuja, S. P., & Muthiah, K. (2019). Survey of State-of-Art in Green Cloud Computing. In Green Business: Concepts, Methodologies, Tools, and Applications. https://doi.org/10.4018/978-1-5225-7915-1.ch066
Ahuja, S. P., & Muthiah, K. (2021). Advances in Green Cloud Computing. In Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing. https://doi.org/10.4018/978-1-7998-5339-8.ch128
BahraniPour, F., Ebrahimi Mood, S., & Farshi, M. (2024). Energy-delay aware request scheduling in hybrid Cloud and Fog computing using improved multi-objective CS algorithm. Soft Computing, 28(5), 4037 – 4050. https://doi.org/10.1007/s00500-023-09381-5
Bhattacherjee, S., Khatua, S., & Roy, S. (2017). A review on energy efficient resource management strategies for cloud. Advances in Intelligent Systems and Computing, 568, 3 – 15. https://doi.org/10.1007/978-981-10-3391-9_1
Chang, Y.-C., Peng, S.-L., Liao, Y.-H., & Chang, R.-S. (2015). Green computing: An SLA-based energy-aware methodology for data centers. Frontiers in Artificial Intelligence and Applications, 274, 1345 – 1354. https://doi.org/10.3233/978-1-61499-484-8-1345
Chauhan, J., & Alam, T. (2024). Adjustable rotation gate based quantum evolutionary algorithm for energy optimisation in cloud computing systems. International Journal of Computational Science and Engineering, 27(4), 414 – 433. https://doi.org/10.1504/IJCSE.2024.139693
Damaševičius, R., & Maskeliunas, R. (2024). Leveraging entangled quantum states to develop consensus mechanisms in blockchain networks for smart forestry applications. 6th International Conference on Computing and Informatics, ICCI 2024, 356 – 360. https://doi.org/10.1109/ICCI61671.2024.10485031
Deng, X., Li, K., Wang, Z., Li, J., & Luo, Z. (2022). A Survey of Blockchain Consensus Algorithms. Proceedings - 2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022, 188 – 192. https://doi.org/10.1109/ICBCTIS55569.2022.00050
Dhinakaran, D., Selvaraj, D., Dharini, N., Edwin Raja, S., & Sakthi Lakshmi Priya, C. (2024). Towards a Novel Privacy-Preserving Distributed Multiparty Data Outsourcing Scheme for Cloud Computing with Quantum Key Distribution. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 286 – 300. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182340834&partnerID=40&md5=dae3da8cb64a86cdaeebd89a582d64ed
dos Reis, T. N. F., Teixeira, M. M., de Salles Soares Neto, C., Oliveira, A., & Sousa, A. M. (2024). A tier labeling proposal for energy efficiency in green cloud. Journal of Sustainability Research, 6(1). https://doi.org/https://doi.org/10.20900/jsr20240004
Fan, Y., Chen, J., Wang, L., & Cao, Z. (2018). Energy-efficient and latency-aware data placement for geo-distributed cloud data centers. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 210, 465 – 474. https://doi.org/10.1007/978-3-319-66628-0_44
Gamage, T. A., & Perera, I. (2024). Optimizing Energy Efficient Cloud Architectures for Edge Computing: A Comprehensive Review. International Journal of Advanced Computer Science and Applications, 15(11), 637 – 645. https://doi.org/10.14569/IJACSA.2024.0151161
Gharehchopogh, F. S. (2023). Quantum-inspired metaheuristic algorithms: comprehensive survey and classification. Artificial Intelligence Review, 56(6), 5479 – 5543. https://doi.org/10.1007/s10462-022-10280-8
Hakemi, S., Houshmand, M., KheirKhah, E., & Hosseini, S. A. (2024). A review of recent advances in quantum-inspired metaheuristics. Evolutionary Intelligence, 17(2), 627 – 642. https://doi.org/10.1007/s12065-022-00783-2
Hussein, Z., Salama, M. A., & El-Rahman, S. A. (2023). Evolution of blockchain consensus algorithms: a review on the latest milestones of blockchain consensus algorithms. Cybersecurity, 6(1). https://doi.org/10.1186/s42400-023-00163-y
Karmakar, S., Dey, A., & Saha, I. (2018). Use of quantum-inspired metaheuristics during last two decades. Proceedings - 7th International Conference on Communication Systems and Network Technologies, CSNT 2017, 272 – 278. https://doi.org/10.1109/CSNT.2017.8418551
Kaur, S., & Chaurasia, N. (2021). Improved Green Cloud Computing with Reduce Fault in the Network: A Study. ICSCCC 2021 - International Conference on Secure Cyber Computing and Communications, 427 – 431. https://doi.org/10.1109/ICSCCC51823.2021.9478122
Liu, X., & Yu, W. (2024). A Review of Research on Blockchain Consensus Mechanisms and Algorithms. International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS, 2024. https://doi.org/10.1109/ICIIBMS62405.2024.10792685
Mani, N., Gursaran, & Mani, A. (2016). Performance of static random topologies in fine-grained QEA on P-PEAKS problem instances. Proceedings of 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks, ICRCICN 2015, 163 – 168. https://doi.org/10.1109/ICRCICN.2015.7434229
Mishra, S. K., Puthal, D., Sahoo, B., Sharma, S., Xue, Z., & Zomaya, A. Y. (2018). Energy-efficient deployment of edge dataenters for mobile clouds in sustainable iot. IEEE Access, 6, 56587 – 56597. https://doi.org/10.1109/ACCESS.2018.2872722
Nan, Y., Li, W., Bao, W., Delicato, F. C., Pires, P. F., Dou, Y., & Zomaya, A. Y. (2017). Adaptive Energy-Aware Computation Offloading for Cloud of Things Systems. IEEE Access, 5, 23947 – 23957. https://doi.org/10.1109/ACCESS.2017.2766165
Pavithra, B., Suchitra, S., Gino Sophia, S. G., & George, J. L. (2019). SDN based energy efficient cloud data center networks. International Journal of Innovative Technology and Exploring Engineering, 8(12), 4250 – 4256. https://doi.org/10.35940/ijitee.L2703.1081219
Pineda, M., Jabba, D., Nieto-Bernal, W., & Pérez, A. (2024). Sustainable Consensus Algorithms Applied to Blockchain: A Systematic Literature Review. Sustainability (Switzerland), 16(23). https://doi.org/10.3390/su162310552
Prakash, K. B. (2021). Quantum Meta-Heuristics and Applications. In Cognitive Engineering for Next Generation Computing: A Practical Analytical Approach. https://doi.org/10.1002/9781119711308.ch10
Qin, Z., Zhu, X., Huang, T., Lin, K., Song, C., Zhang, Z., & Liu, J. (2024). Research on Lightweight ER-PBFT Consensus Algorithm for IoT Applications. 2024 4th International Conference on Electronic Information Engineering and Computer Communication, EIECC 2024, 771 – 776. https://doi.org/10.1109/EIECC64539.2024.10929584
Raghav, Y. Y., & Pandey, P. (2024). Adoption of green cloud computing for environmental sustainability: An analysis. In Convergence Strategies for Green Computing and Sustainable Development. https://doi.org/10.4018/979-8-3693-0338-2.ch008
Ravi, A., & Peddoju, S. K. (2017). Energy - Service Trade-Off Model for Mobile Cloud Computing. Proceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017, 616 – 621. https://doi.org/10.1109/MASS.2017.64
Silva, D. S., Machado, J. D. S., Ribeiro, A. D. R. L., & Ordonez, E. D. M. (2020). Towards self-optimisation in fog computing environments. International Journal of Grid and Utility Computing, 11(6), 755 – 768. https://doi.org/10.1504/IJGUC.2020.110903
Windiatmaja, J. H., Salman, M., & Sari, R. F. (2023). A Review of Recent Trends in Blockchain Consensus Algorithms: Artificial Intelligence-Based Approaches. Proceedings - 2023 28th Asia Pacific Conference on Communications, APCC 2023, 335 – 341. https://doi.org/10.1109/APCC60132.2023.10460688
Zhang, J., Zhong, S., Wang, J., Wang, L., Yang, Y., Wei, B., & Zhou, G. (2020). A Review on Blockchain-Based Systems and Applications. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11894 LNCS, 237 – 249. https://doi.org/10.1007/978-3-030-38651-1_20
Zhou, W., Wang, H., Mohiuddin, G., Chen, D., & Ren, Y. (2022). Consensus Mechanism of Blockchain Based on PoR with Data Deduplication. Intelligent Automation and Soft Computing, 34(3), 1473 – 1488. https://doi.org/10.32604/iasc.2022.029657
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