research-article

KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices

Authors:

Guangtao XueAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 1551 - 1561

https://doi.org/10.1145/3543507.3583288

Published: 30 April 2023 Publication History

Abstract

Accurately forecasting workloads in terms of throughput that is quantified as queries per second (QPS) is essential for microservices to elastically adjust their resource allocations. However, long-term QPS prediction is challenging in two aspects: 1) generality across various services with different temporal patterns, 2) characterization of intricate QPS sequences which are entangled by multiple components. In this paper, we propose a knowledge auto-embedding Informer network (KAE-Informer) for forecasting the long-term QPS sequences of microservices. By analyzing a large number of microservice traces, we discover that there are two main decomposable and predictable components in QPS sequences, namely global trend & dominant periodicity (TP) and low-frequency residual patterns with long-range dependencies. These two components are important for accurately forecasting long-term QPS. First, KAE-Informer embeds the knowledge of TP components through mathematical modeling. Second, KAE-Informer designs a convolution ProbSparse self-attention mechanism and a multi-layer event discrimination scheme to extract and embed the knowledge of local context awareness and event regression effect implied in residual components, respectively. We conduct experiments based on three real datasets including a QPS dataset collected from 40 microservices. The experiment results show that KAE-Informer achieves a reduction of MAPE, MAE and RMSE by about 16.6%, 17.6% and 23.1% respectively, compared to the state-of-the-art models.

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Cited By

Shi RHuang HYin KZhou WJin HBaeza-Yates RBonchi F(2024)Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution ClassificationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671768(2674-2685)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671768
Han WZhu TChen LNing HLuo YWan Y(2024)MCformer: Multivariate Time Series Forecasting With Mixed-Channels TransformerIEEE Internet of Things Journal10.1109/JIOT.2024.340169711:17(28320-28329)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3401697
Zhao SKong MLi RHounye ASu RHou MCao C(2024)SCARNet: using convolution neural network to predict time series with time-varying varianceMultimedia Tools and Applications10.1007/s11042-024-19322-5Online publication date: 13-May-2024
https://doi.org/10.1007/s11042-024-19322-5

Index Terms

KAE-Informer: A Knowledge Auto-Embedding Informer for Forecasting Long-Term Workloads of Microservices

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cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

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Published: 30 April 2023

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Alibaba Group through Alibaba Innovative Research Program and Alibaba Research Intern Program
the Artificial Intelligence Technology Support Project of the Science and Technology Commission of Shanghai Municipality
the Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Shi RHuang HYin KZhou WJin HBaeza-Yates RBonchi F(2024)Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution ClassificationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671768(2674-2685)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671768
Han WZhu TChen LNing HLuo YWan Y(2024)MCformer: Multivariate Time Series Forecasting With Mixed-Channels TransformerIEEE Internet of Things Journal10.1109/JIOT.2024.340169711:17(28320-28329)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3401697
Zhao SKong MLi RHounye ASu RHou MCao C(2024)SCARNet: using convolution neural network to predict time series with time-varying varianceMultimedia Tools and Applications10.1007/s11042-024-19322-5Online publication date: 13-May-2024
https://doi.org/10.1007/s11042-024-19322-5

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