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Using Kubernetes in Academic Environment : Problems and Approaches

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F23%3A00130019" target="_blank" >RIV/00216224:14610/23:00130019 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/63839172:_____/23:10133550

  • Výsledek na webu

    <a href="https://link.springer.com/chapter/10.1007/978-3-031-22698-4_12" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-22698-4_12</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-22698-4_12" target="_blank" >10.1007/978-3-031-22698-4_12</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Using Kubernetes in Academic Environment : Problems and Approaches

  • Popis výsledku v původním jazyce

    In this work, we discuss our experience when utilizing the Kubernetes orchestrator (K8s) to efficiently allocate resources in a heterogeneous and dynamic academic environment. In the commercial world, the "pay per use" model is a strong regulating factor for efficient resource usage. In the academic environment, resources are usually provided "for free" to the end-users, thus they often lack a clear motivation to plan their use efficiently. In this paper, we show three major sources of inefficiencies. One is the users' requirement to have interactive computing environments, where the users need resources for their application as soon as possible. Users do not appreciate waiting for interactive environments, but constantly keeping some resources available for interactive tasks is inefficient. The second phenomenon is observable in both interactive and batch workloads; users tend to overestimate necessary limits for their computations, thus wasting resources. Finally, Kubernetes does not support fair-sharing functionality (dynamic user priorities) which hampers the efforts when developing a fair scheme for Pod/job scheduling and/or eviction. We discuss various approaches to deal with these problems such as scavenger jobs, placeholder jobs, Kubernetes-specific resource allocation policies, separate clusters, priority classes, and novel hybrid cloud approach. We also show that all these proposals open interesting scheduling-related questions that are hard to answer with existing Kubernetes tools and policies. Last but not least, we provide a real workload trace from our installation to the scheduling community which captures these phenomena.

  • Název v anglickém jazyce

    Using Kubernetes in Academic Environment : Problems and Approaches

  • Popis výsledku anglicky

    In this work, we discuss our experience when utilizing the Kubernetes orchestrator (K8s) to efficiently allocate resources in a heterogeneous and dynamic academic environment. In the commercial world, the "pay per use" model is a strong regulating factor for efficient resource usage. In the academic environment, resources are usually provided "for free" to the end-users, thus they often lack a clear motivation to plan their use efficiently. In this paper, we show three major sources of inefficiencies. One is the users' requirement to have interactive computing environments, where the users need resources for their application as soon as possible. Users do not appreciate waiting for interactive environments, but constantly keeping some resources available for interactive tasks is inefficient. The second phenomenon is observable in both interactive and batch workloads; users tend to overestimate necessary limits for their computations, thus wasting resources. Finally, Kubernetes does not support fair-sharing functionality (dynamic user priorities) which hampers the efforts when developing a fair scheme for Pod/job scheduling and/or eviction. We discuss various approaches to deal with these problems such as scavenger jobs, placeholder jobs, Kubernetes-specific resource allocation policies, separate clusters, priority classes, and novel hybrid cloud approach. We also show that all these proposals open interesting scheduling-related questions that are hard to answer with existing Kubernetes tools and policies. Last but not least, we provide a real workload trace from our installation to the scheduling community which captures these phenomena.

Klasifikace

  • Druh

    D - Stať ve sborníku

  • CEP obor

  • OECD FORD obor

    10200 - Computer and information sciences

Návaznosti výsledku

  • Projekt

    <a href="/cs/project/LM2018140" target="_blank" >LM2018140: e-Infrastruktura CZ</a><br>

  • Návaznosti

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Ostatní

  • Rok uplatnění

    2023

  • Kód důvěrnosti údajů

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Údaje specifické pro druh výsledku

  • Název statě ve sborníku

    Job Scheduling Strategies for Parallel Processing

  • ISBN

    9783031226977

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Počet stran výsledku

    19

  • Strana od-do

    235-253

  • Název nakladatele

    Springer

  • Místo vydání

    Cham (Switzerland)

  • Místo konání akce

    Virtual Event

  • Datum konání akce

    1. 1. 2022

  • Typ akce podle státní příslušnosti

    WRD - Celosvětová akce

  • Kód UT WoS článku

    000972597400013