A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application's performance and Dynamic Voltage and Frequency Scaling (DVFS) actions, alongside assessments of server power consumption and operating temperature, indicates that existing Linux scaling governors often fall into non-optimal DVFS strategies, especially for cloud applications with varying workloads and requests. This shortfall comes from the misleading CPU load metrics, which fail to accurately capture the applications' true performance requirements and demands. In this context, we introduce a novel scaling governor named GreenDVFS. First, it identifies the optimal frequencies for the application in a range of workload scenarios. Optimal frequencies are used to maintain application performance, reduce server power consumption, and maintain a balanced operating temperature in different workload scenarios. Furthermore, we design a long short-term memory (LSTM)-based time series methodology to detect the real-time workloads of cloud applications accurately and timely. Building on these foundations, the proposed method takes optimal DVFS actions, tailored for cloud applications under different workload conditions, to optimize performance, energy efficiency, and temperature. The experimental results highlight the effectiveness of the proposed GreenDVFS, with up to 18% savings in energy consumption and a 30% decrease in operational temperature by comparing against the default Linux governor, all while not compromising the application's performance. Such improvements help to optimize cloud computing operations for enhanced efficiency and sustainability.