Cloud native solutions are widely applied in various fields, placing higher demands on the efficient management and utilization of resource platforms. To achieve the efficiency, load forecasting and elastic scaling have become crucial technologies for dynamically adjusting cloud resources to meet user demands and minimizing resource waste. However, existing prediction-based methods lack comprehensive analysis and integration of load characteristics across different time scales. For instance, long-term trend analysis helps reveal long-term changes in load and resource demand, thereby supporting proactive resource allocation over longer periods, while short-term volatility analysis can examine short-term fluctuations in load and resource demand, providing support for real-time scheduling and rapid response. In response to this, our research introduces TempoScale, which aims to enhance the comprehensive understanding of temporal variations in cloud workloads, enabling more intelligent and adaptive decision-making for elastic scaling. TempoScale utilizes the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm to decompose time-series load data into multiple Intrinsic Mode Functions (IMF) and a Residual Component (RC). First, we integrate the IMF, which represents both long-term trends and short-term fluctuations, into the time series prediction model to obtain intermediate results. Then, these intermediate results, along with the RC, are transferred into a fully connected layer to obtain the final result. Finally, this result is fed into the resource management system based on Kubernetes for resource scaling. Our proposed approach can reduce the Mean Square Error by 5.80% to 30.43% compared to the baselines, and reduce the average response time by 5.58% to 31.15%.

翻译：云原生解决方案广泛应用于各个领域，对资源平台的高效管理与利用提出了更高要求。为实现这一效率目标，负载预测与弹性伸缩已成为动态调整云资源以满足用户需求并最小化资源浪费的关键技术。然而，现有基于预测的方法缺乏对不同时间尺度负载特征的综合分析与集成。例如，长期趋势分析有助于揭示负载与资源需求的长期变化，从而支持更长时间跨度内的主动资源分配；而短期波动分析则能考察负载与资源需求的短期波动，为实时调度与快速响应提供支持。针对此问题，我们的研究引入TempoScale，旨在增强对云工作负载时间变化的全面理解，为弹性伸缩实现更智能、自适应的决策。TempoScale利用具有自适应噪声的完全集成经验模态分解算法，将时间序列负载数据分解为多个固有模态函数（IMF）与一个残差分量（RC）。首先，我们将同时表征长期趋势与短期波动的IMF整合到时间序列预测模型中，获取中间结果；随后，将这些中间结果与RC输入全连接层，获得最终结果；最后，将该结果馈入基于Kubernetes的资源管理系统进行资源伸缩。与基线方法相比，我们提出的方法可将均方误差降低5.80%至30.43%，并将平均响应时间减少5.58%至31.15%。