A Heavy-Load-Enhanced and Changeable-Periodicity-Perceived Workload Prediction Network

TL;DR

This paper introduces PePNet, a workload prediction network that adaptively detects periodicity and enhances heavy workload prediction accuracy, addressing variability and data imbalance in cloud server workloads.

Contribution

The paper proposes a novel PePNet model with a periodicity-perceived mechanism and Achilles' Heel loss to improve heavy workload prediction in variable periodicity time series.

Findings

PePNet improves overall workload prediction accuracy by 11.8%.

PePNet enhances heavy workload prediction accuracy by 21.0%.

Experiments on real datasets validate the effectiveness of PePNet.

Abstract

Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional workload bursts, which makes workload prediction challenging. The time series forecasting methods relying on periodicity information, often assume fixed and known periodicity length, which does not align with the periodicity-changeable nature of cloud service workloads. Although many state-of-the-art time-series forecasting methods do not rely on periodicity information and achieve high overall accuracy, they are vulnerable to data imbalance between heavy workloads and regular workloads. As a result, their prediction accuracy on rare heavy workloads is limited. Unfortunately, heavyload-prediction accuracy is more important than overall one, as errors in heavyload prediction are more likely to cause Service Level Agreement violations than errors in normal-load prediction. Thus, we propose a changeable-periodicity-perceived workload prediction network (PePNet) to fuse periodic information adaptively for periodicity-changeable time series and improve rare heavy workload prediction accuracy. It has two distinctive characteristics: (i) A Periodicity-Perceived Mechanism to detect the periodicity length automatically and fuses periodic information adaptively, which is suitable for periodicity-changeable time series, and (ii) An Achilles' Heel Loss Function that is used to iteratively optimize the most under-fitting part in predicting sequence for each step, thus evidently improving the prediction accuracy of heavy load. Extensive experiments conducted on real-world datasets demonstrate that PePNet improves accuracy for overall workload by 11.8% averagely, compared with state-of-the-art methods. Especially, PePNet improves accuracy for heavy workload by 21.0% averagely.