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TempoScale: A Cloud Workloads Prediction Approach Integrating Short-Term and Long-Term Information

Linfeng Wen, Minxian Xu, Adel N. Toosi, Kejiang Ye

TL;DR

The results demonstrate the effectiveness of the proposed TempoScale method in reducing violations of service-level objectives and providing better performance in terms of resource utilization.

Abstract

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%.

TempoScale: A Cloud Workloads Prediction Approach Integrating Short-Term and Long-Term Information

TL;DR

The results demonstrate the effectiveness of the proposed TempoScale method in reducing violations of service-level objectives and providing better performance in terms of resource utilization.

Abstract

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%.
Paper Structure (22 sections, 12 equations, 11 figures, 4 tables, 1 algorithm)

This paper contains 22 sections, 12 equations, 11 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivation and Feasibility
  3. Related Work
  4. Traditional Machine learning-based models
  5. Neural network-based models
  6. Attention mechanism-based models
  7. Critical analysis
  8. TempoScale: A Resource Scheduler Integrating Short-Term and Long-Term Information
  9. Preprocessing of data and decomposition of IMFs
  10. Processing intermediate results using different model
  11. Short-term prediction model
  12. Long-term prediction model
  13. Obtaining the final results through a MLP
  14. Performance Evaluations
  15. Experimental setup
  16. ...and 7 more sections

Figures (11)

  • Figure 1: The Focus Differs Between Long-Term and Short-Term Predictions in Time Series Forecasting.
  • Figure 2: Performance Comparison of Short-Term (esDNN) and Long-Term (Informer) Prediction.
  • Figure 3: The Architecture of TempoScale.
  • Figure 4: Modal Decomposition for Feature Extraction.
  • Figure 5: The Network Structure of Short-term Prediction Model in TempoScale.
  • ...and 6 more figures