College of Information Science & Technology, Donghua University, Shanghai, China.
Department of Computer Science & Engineering, Rashtrakavi Ramdhari Singh Dinkar College of Engineering, Begusarai, India.
Comput Intell Neurosci. 2023 Feb 20;2023:6090282. doi: 10.1155/2023/6090282. eCollection 2023.
Per-user pricing is possible with cloud computing, a relatively new technology. It provides remote testing and commissioning services through the web, and it utilizes virtualization to make available computing resources. In order to host and store firm data, cloud computing relies on data centers. Data centers are made up of networked computers, cables, power supplies, and other components. Cloud data centers have always had to prioritise high performance over energy efficiency. The biggest obstacle is finding a happy medium between system performance and energy consumption, namely, lowering energy use without compromising system performance or service quality. These results were obtained using the PlanetLab dataset. In order to implement the strategy we recommend, it is crucial to get a complete picture of how energy is being consumed in the cloud. Using proper optimization criteria and guided by energy consumption models, this article offers the Capsule Significance Level of Energy Consumption (CSLEC) pattern, which demonstrates how to conserve more energy in cloud data centers. Capsule optimization's prediction phase F1-score of 96.7 percent and 97 percent data accuracy allow for more precise projections of future value.
云计算采用按用户定价，这是一种相对较新的技术。它通过网络提供远程测试和调试服务，并利用虚拟化提供计算资源。为了托管和存储公司数据，云计算依赖于数据中心。数据中心由联网的计算机、电缆、电源和其他组件组成。云数据中心一直以来都将高性能放在首位，而不是能源效率。最大的障碍是在系统性能和能源消耗之间找到一个平衡点，即不影响系统性能或服务质量的情况下降低能源消耗。这些结果是使用 PlanetLab 数据集获得的。为了实施我们建议的策略，了解云环境中的能源消耗情况至关重要。本文使用适当的优化标准和能源消耗模型为指导，提出了 Capsule Significance Level of Energy Consumption (CSLEC) 模式，该模式展示了如何在云数据中心中节省更多能源。Capsule 优化的预测阶段 F1 得分为 96.7%，数据准确率为 97%，可以更精确地预测未来值。
