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Interference-Aware Workload Placement for Improving Latency Distribution of Converged HPC/Big Data Cloud Infrastructures

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Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13227))

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Abstract

Recently, High Performance, Big Data, and Cloud Computing worlds tend to converge in terms of workload deployment with containerization technology acting as an enabler towards this direction. In such cases of application diversity and multi-tenancy, a universal scheduler able to satisfy the end-user needs for seamless, yet, efficient application deployment is required. While Kubernetes container orchestrator seems to be the answer that enables application-agnostic deployment, it still depends highly on coarse system metrics for its scheduling policies, thus, neglecting the performance degradation due to resource contention in the underlying system.

In this paper, we design and implement an interference-aware modular framework, able to balance incoming workload based on low-level metrics monitoring. We evaluate our proposed solution over different workload mixes and co-location scenarios showing that against the state-of-art, but interference unaware Kubernetes scheduler the proposed framework significantly improves the latency distribution of the converged cloud infrastructure, improving median latency up to 27% and reducing standard deviation up to 25%.

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Authors and Affiliations

  1. National Technical University of Athens, Athens, Greece

    Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis & Dimitrios Soudris

  2. Harokopio University of Athens, Athens, Greece

    Sotirios Xydis

Authors
  1. Achilleas Tzenetopoulos
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  2. Dimosthenis Masouros
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  3. Sotirios Xydis
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  4. Dimitrios Soudris
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Corresponding author

Correspondence to Achilleas Tzenetopoulos .

Editor information

Editors and Affiliations

  1. University of California, San Diego, La Jolla, CA, USA

    Alex Orailoglu

  2. Fraunhofer IESE, Kaiserslautern, Germany

    Matthias Jung

  3. Brandenburg University of Technology, Cottbus, Germany

    Marc Reichenbach

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Tzenetopoulos, A., Masouros, D., Xydis, S., Soudris, D. (2022). Interference-Aware Workload Placement for Improving Latency Distribution of Converged HPC/Big Data Cloud Infrastructures. In: Orailoglu, A., Jung, M., Reichenbach, M. (eds) Embedded Computer Systems: Architectures, Modeling, and Simulation. SAMOS 2021. Lecture Notes in Computer Science, vol 13227. Springer, Cham. https://doi.org/10.1007/978-3-031-04580-6_8

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  • DOI: https://doi.org/10.1007/978-3-031-04580-6_8

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