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Adaptive Online Estimation of Thrashing-Avoiding Memory Reservations for Long-Lived Containers

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

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Abstract

Data-intensive computing systems in cloud datacenters create long-lived containers and allocate memory resource for them to execute long-running applications. It is a challenge to exactly estimate how much memory should be reserved for containers to enable smooth application execution and high resource utilization as well. Current state-of-the-art work has two limitations. First, prediction accuracy is restricted by the monotonicity of the iterative search. Second, application performance fluctuates due to the termination conditions. In this paper, we propose two improved strategies based on MEER, called MEER+ and Deep-MEER, which are designed to assist in memory allocation upon resource manager like YARN. MEER+ has one more step of approximation than MEER, to make the iterative search bi-directional and better approach the optimal value. Based on reinforcement learning and rich data, Deep-MEER achieves thrashing-avoiding estimation without involving termination conditions. Based on the different input requirements and advantages, a scheme to adaptively adopt MEER+ and Deep-MEER in cluster life cycle is proposed. We have evaluated MEER+ and Deep-MEER. Our experimental results show that MEER+ and Deep-MEER yield up to 88% and 20% higher accuracy. Moreover, Deep-MEER guarantees stable performance for applications during recurring executions.

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Acknowledgments

This work is supported by The National Key Research and Development Program of China (2019YFB1804502), Key-Area Research and Development Program of Guangdong Province (Grant No.2019B010107001), and National Natural Science Foundation of China (Grant No.61832020, 61702569).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong, 510275, China

    Jiayun Lin, Fang Liu, Zhenhua Cai, Zhijie Huang & Nong Xiao

  2. Shenzhen Dapu Microelectronic Co., Ltd., Shenzhen, China

    Weijun Li

Authors
  1. Jiayun Lin
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  2. Fang Liu
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  3. Zhenhua Cai
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  4. Zhijie Huang
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  5. Weijun Li
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  6. Nong Xiao
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Corresponding author

Correspondence to Fang Liu .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

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Lin, J., Liu, F., Cai, Z., Huang, Z., Li, W., Xiao, N. (2021). Adaptive Online Estimation of Thrashing-Avoiding Memory Reservations for Long-Lived Containers. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_37

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  • DOI: https://doi.org/10.1007/978-3-030-67537-0_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67536-3

  • Online ISBN: 978-3-030-67537-0

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