Open Access
| Issue |
E3S Web Conf.
Volume 619, 2025
3rd International Conference on Sustainable Green Energy Technologies (ICSGET 2025)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 9 | |
| Section | Smart Electronics for Sustainable Solutions | |
| DOI | https://doi.org/10.1051/e3sconf/202561903012 | |
| Published online | 12 March 2025 | |
- Lin JCW, Djenouri Y, Srivastava G, Li Y, Yu PS. Scalable mining of high- utilitysequential patterns with three-tier MapReduce model. ACM Trans Knowl Discov Data 2021;16(3):1–26. [Google Scholar]
- Wu JMT, Srivastava G, Wei M, Yun U, Lin JCW. Fuzzy high-utility pattern mining in parallel and distributed hadoop framework. Inform Sci 2021; 553:31–48. [CrossRef] [Google Scholar]
- Beloglazov A, Buyya R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr Comput: Pract Exper 2012;24:1397–420; [CrossRef] [Google Scholar]
- Andrae AS, Edler T. On global electricity usage of communication technology: trends to 2030. Challenges 2015; 6:117–57. [CrossRef] [Google Scholar]
- Fard SYZ, Ahmadi MR, Adabi S. A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. J Supercomput 2017;73:4347–68. [CrossRef] [Google Scholar]
- Panda SK, Jana PK. An energy-efficient task scheduling algorithm for heterogeneous cloud computing systems. Cluster Comput 2019;22:509–27. [CrossRef] [Google Scholar]
- Hussain A, Aleem M, Khan A, Iqbal MA, Islam MA. RALBA: a computation-aware load balancing scheduler for cloud computing. Cluster Comput 2018;21:1667–80. [CrossRef] [Google Scholar]
- Zhou Z, Abawajy J, Chowdhury M, Hu Z, Li K, Cheng H, Alelaiwi AA, Li F. Minimizing SLA violation and power consumption in cloud data centers using adaptive energy-aware algorithms. Future Gener Comput Syst 2018;86:836–50. [CrossRef] [Google Scholar]
- Masdari M, Nabavi SS, Ahmadi V. An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 2016;66:106–27. [CrossRef] [Google Scholar]
- Karthikeyan, R., Chitra, P. Novel power reduction framework for enhancing cloud computing by integrated GSNN scheduling method. Cluster Comput 21, 755–766 (2018). https://doi.org/10.1007/s10586-017-0889-1. [CrossRef] [Google Scholar]
- R. Karthikeyan and P. Chitra, “Novel heuristics energy efficiency approach for cloud data center,” 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), Ramanathapuram, India, 2012, pp. 202-207, doi: 10.1109/ICACCCT.2012.6320771. [Google Scholar]
- Karthikeyan R, Balamurugan V, Cyriac R, Sundaravadivazhagan B. COSCO2: AI- augmented evolutionary algorithm-based workload prediction framework for sustainable cloud data centers. Trans Emerging Tel Tech. 2023; 34(1):e4652. doi:10.1002/ett.4652. [CrossRef] [Google Scholar]
- Balamurugan, V., Karthikeyan, R., Sundaravadivazhagan, B. et al. Enhanced Elman spike neural network based fractional order discrete Tchebyshev encryption fostered big data analytical method for enhancing cloud data security. Wireless Netw 29, 523–537 (2023). https://doi.org/10.1007/s11276-022-03142-2. [CrossRef] [Google Scholar]
- Hariharan, Shanmugasundaram, D. Anandan, Murugaperumal Krishnamoorthy, Vinay Kukreja, Nitin Goyal, and Shih-Yu Chen. “Advancements in Liver Tumor Detection: A Comprehensive Review of Various Deep Learning Models.” CMES-Computer Modeling in Engineering & Sciences 142, no. 1 (2025). [Google Scholar]
- Q. Chou, W. Fan and J. Zhang, “A Reinforcement Learning Model for Virtual Machines Consolidation in Cloud Data Center,” 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China, 2021, pp. 16-21, doi: 10.1109/CACRE52464.2021.9501288. [Google Scholar]
- Zhu, X., Xia, R., Zhou, H. et al. An intelligent decision system for virtual machine migration based on specific Q-learning. J Cloud Comp 13, 122 (2024). https://doi.org/10.1186/s13677-024-00684-y [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.