A Periodicity-Perceived Workload Prediction Network for Accurate Forecasting of Rare Heavy Workload Occurrences

Core Concepts

PePNet, a novel workload prediction network, improves overall and heavy workload prediction accuracy by leveraging a Periodicity-Perceived Mechanism and an Achilles' Heel Loss Function.

Abstract

The paper proposes PePNet, a workload prediction network that addresses the challenges of highly variable workloads with rare occurrences of heavy workload. Key highlights: Periodicity-Perceived Mechanism: PePNet automatically detects the existence and length of periodicity in the workload time series, and adaptively fuses the periodic information to handle both periodic and aperiodic workloads. Achilles' Heel Loss Function: PePNet uses a specialized loss function that iteratively optimizes the most under-fitting part of the prediction sequence, effectively improving the accuracy of heavy workload prediction while maintaining high overall prediction accuracy. Extensive experiments on Alibaba, SMD, and Dinda's datasets show that PePNet outperforms state-of-the-art methods, improving heavy workload prediction accuracy by 21.0% and overall prediction accuracy by 11.8% on average. PePNet introduces only slight time overhead compared to efficient baseline models, while being insensitive to hyperparameter settings.

Stats

"The heavy workload is defined as the workload greater than the average workload plus one standard deviation for each machine." "The heavy workload proportion is 15.73% for CPU usage and 12.89% for memory usage of Alibaba's dataset, and 11.15% for workload of Dinda's dataset."

Quotes

"Either the overall inaccuracy of statistic methods or the heavy-workload inaccuracy of neural-network-based models can cause service level agreement violations." "The Achilles' Heel Loss Function actually forces the loss to descend along the gradient directions of some of the largest prediction errors."

Key Insights Distilled From

PePNet: A Periodicity-Perceived Workload Prediction Network Supporting Rare Occurrence of Heavy Workload

by Feiyi Chen,Z... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2308.01917.pdf

Deeper Inquiries

How can the Periodicity-Perceived Mechanism be extended to handle more complex patterns in the workload time series, such as multiple periodicities or non-stationary periodicity

The Periodicity-Perceived Mechanism can be extended to handle more complex patterns in the workload time series by incorporating advanced techniques for detecting and adapting to multiple periodicities or non-stationary periodicity. One approach could involve implementing a multi-resolution analysis to capture different periodic patterns at varying scales. This could involve using wavelet transforms or Fourier analysis to decompose the time series into different frequency components, each representing a different periodicity. By analyzing these components separately and then fusing the periodic information adaptively, the model can better handle multiple periodicities in the data. Additionally, techniques like dynamic time warping or attention mechanisms can be employed to align and combine periodic signals with varying lengths or phases, enabling the model to effectively capture non-stationary periodicity in the workload time series.

What other loss functions or optimization techniques could be explored to further improve the heavy workload prediction accuracy without compromising the overall prediction performance

To further improve heavy workload prediction accuracy without compromising overall performance, exploring alternative loss functions or optimization techniques can be beneficial. One approach could be to incorporate a weighted loss function that assigns higher penalties to prediction errors in heavy workload instances. By giving more importance to accurately predicting heavy workload periods, the model can focus on improving accuracy where it matters most. Additionally, techniques like ensemble learning, where multiple models are combined to make predictions, can be explored to leverage the strengths of different models and improve overall prediction performance. Optimization techniques like Bayesian optimization or genetic algorithms can also be used to fine-tune hyperparameters and improve the model's ability to capture complex patterns in the data.

What are the potential applications of the PePNet approach beyond workload prediction, such as in other time series forecasting domains with imbalanced data distributions

The PePNet approach has potential applications beyond workload prediction in other time series forecasting domains with imbalanced data distributions. One such application could be in financial forecasting, where predicting rare but significant events, such as market crashes or anomalies, is crucial. PePNet's ability to handle imbalanced data and focus on accurately predicting rare occurrences can be valuable in financial time series analysis. Additionally, in healthcare, PePNet could be used for predicting rare medical conditions or anomalies in patient data, where early detection is critical. The model's adaptability to handle heavy workload bursts can be leveraged in various industries where anomaly detection or rare event forecasting is essential for decision-making and risk management.