  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%.
