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Efficient Proactive Resource Allocation for Multi-stage Cloud-Native Microservices

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14488))

Abstract

Microservices deployment in the cloud often faces a prevalent challenge: how to maximize resource utilization while maintaining high quality-of-service (QoS). Existing automatic scaling tools frequently exhibit limited adaptability, particularly when handling frequent request load fluctuations, which exacerbates the challenge. To address this issue, we introduce a proactive runtime deployment optimization method for multi-stage microservices, aiming to ensure both resource efficiency and QoS.

Our proposed method encompasses four interrelated modules–forecasting, constraint planning, judgment selection, and execution–which collaboratively work towards optimizing runtime resource allocation, generating viable deployment plans, and identifying cost-efficient solutions without compromising QoS. Through a set of experiments, we demonstrate that the proposed proactive deployment optimization method can potentially reduce computational resource usage by 35% while maintaining the desired quality of service.

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Acknowledgements

This research was funded by the Basic Public Welfare Research Project of Zhejiang Province grant number LY20F020014 and the National Science Foundation for Young Scientists of China grant number 61802096.

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Authors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Pengfei Liao, Xingzhen He & Yuxia Cheng

  2. Taizhou Urban and Rural Planning and Design Institute, Taizhou, China

    Guanyan Pan, Wenbing Peng & Minhui Fang

  3. School of Computer Science, Zhejiang University, Hangzhou, China

    Bei Wang

  4. HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, China

    Fanding Huang & Yifei Chen

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  1. Pengfei Liao
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  2. Guanyan Pan
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  4. Xingzhen He
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  6. Minhui Fang
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  9. Yuxia Cheng
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Correspondence to Yuxia Cheng .

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Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Liao, P. et al. (2024). Efficient Proactive Resource Allocation for Multi-stage Cloud-Native Microservices. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14488. Springer, Singapore. https://doi.org/10.1007/978-981-97-0801-7_24

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  • DOI: https://doi.org/10.1007/978-981-97-0801-7_24

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