PePNet: A Periodicity-Perceived Workload Prediction Network Supporting
  Rare Occurrence of Heavy Workload
Feiyi Chen Zhen Qin Hailiang Zhao Mengchu Zhou Shuiguang Deng
Cloud providers can greatly benefit from accurate workload prediction.
However, the workload of cloud servers is highly variable, with occasional
heavy workload bursts. This makes workload prediction challenging.
  There are mainly two categories of workload prediction methods: statistical
methods and neural-network-based ones. The former ones rely on strong
mathematical assumptions and have reported low accuracy when predicting highly
variable workload. The latter ones offer higher overall accuracy, yet they are
vulnerable to data imbalance between heavy workload and common one. This
impairs the prediction accuracy of neural network-based models on heavy
workload.
  Either the overall inaccuracy of statistic methods or the heavy-workload
inaccuracy of neural-network-based models can cause service level agreement
violations.
  Thus, we propose PePNet to improve overall especially heavy workload
prediction accuracy. It has two distinctive characteristics:
  (i) A Periodicity-Perceived Mechanism to detect the existence of periodicity
and the length of one period automatically, without any priori knowledge.
Furthermore, it fuses periodic information adaptively, which is suitable for
periodic, lax periodic and aperiodic time series.
  (ii) An Achilles' Heel Loss Function iteratively optimizing the most
under-fitting part in predicting sequence for each step, which significantly
improves the prediction accuracy of heavy load.
  Extensive experiments conducted on Alibaba2018, SMD dataset and Dinda's
dataset demonstrate that PePNet improves MAPE for overall workload by 20.0% on
average, compared with state-of-the-art methods. Especially, PePNet improves
MAPE for heavy workload by 23.9% on average.