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

• URL: http://arxiv.org/abs/2308.01917v2
• Date: Sun, 28 Apr 2024 12:23:38 GMT
• Title: PePNet: A Periodicity-Perceived Workload Prediction Network Supporting Rare Occurrence of Heavy Workload
• Authors: Feiyi Chen, Zhen Qin, Hailiang Zhao, Shuiguang Deng,
• Abstract summary: workload of cloud servers is highly variable, with occasional heavy workload bursts. There are two categories of workload prediction methods: statistical methods and neural-network-based ones. We propose PePNet to improve overall especially heavy workload prediction accuracy.
• Score: 11.93843096959306
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models [68.23649978697027]
  Forecast-PEFT is a fine-tuning strategy that freezes the majority of the model's parameters, focusing adjustments on newly introduced prompts and adapters. Our experiments show that Forecast-PEFT outperforms traditional full fine-tuning methods in motion prediction tasks. Forecast-FT further improves prediction performance, evidencing up to a 9.6% enhancement over conventional baseline methods.
  arXiv  Detail & Related papers  (2024-07-28T19:18:59Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• GVFs in the Real World: Making Predictions Online for Water Treatment [23.651798878534635]
  We investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. We first describe this dataset, and highlight challenges with seasonality, nonstationarity, partial observability. We show the importance of learning in deployment, by comparing a TD agent trained purely offline with no online updating to a TD agent that learns online.
  arXiv  Detail & Related papers  (2023-12-04T04:49:10Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction [17.346001585453415]
  A job completes when all its tasks finish, so stragglers are a major impediment to performance. This paper presents NURD, a novel Negative-Unlabeled learning approach with Reweighting and Distribution-compensation. We evaluate NURD on two production traces from Google and Alibaba.
  arXiv  Detail & Related papers  (2022-03-16T01:15:50Z)
• Improved Fine-tuning by Leveraging Pre-training Data: Theory and Practice [52.11183787786718]
  Fine-tuning a pre-trained model on the target data is widely used in many deep learning applications. Recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy. We propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task.
  arXiv  Detail & Related papers  (2021-11-24T06:18:32Z)
• Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models [11.171712535005357]
  We propose Deep Kernel Accelerated Failure Time models for the time-to-event prediction task. Our model shows better point estimate performance than recurrent neural network based baselines in experiments on two real-world datasets.
  arXiv  Detail & Related papers  (2021-07-26T14:55:02Z)
• Towards More Fine-grained and Reliable NLP Performance Prediction [85.78131503006193]
  We make two contributions to improving performance prediction for NLP tasks. First, we examine performance predictors for holistic measures of accuracy like F1 or BLEU. Second, we propose methods to understand the reliability of a performance prediction model from two angles: confidence intervals and calibration.
  arXiv  Detail & Related papers  (2021-02-10T15:23:20Z)
• Online Capacity Scaling Augmented With Unreliable Machine Learning Predictions [0.0]
  We propose a novel algorithm, called Adaptive Balanced Capacity Scaling (ABCS), that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that ABCS is $(varepsilon)$-competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate.
  arXiv  Detail & Related papers  (2021-01-28T18:14:18Z)
• Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction [58.98112070128482]
  We propose a lightweight solution for series prediction based on historic observations. It consists of a heterogeneous ensemble method composed of two models - a neural network and a mean predictor. It achieves an overall $R2$ score of 0.10 on the available FedCSIS 2020 challenge dataset.
  arXiv  Detail & Related papers  (2020-07-07T15:44:16Z)
• Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile [15.875569404476495]
  We focus on a tensor-based prediction and propose several practical techniques to improve prediction. For long-term prediction specifically, we propose the "Tensor Decomposition + 2-Dimensional Auto-Regressive Moving Average (2D-ARMA)" model. For short-term prediction, we propose to conduct tensor completion based on tensor clustering to avoid oversimplifying and ensure accuracy.
  arXiv  Detail & Related papers  (2020-04-23T08:30:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.