Conference paper Open Access

ECSched: Efficient Container Scheduling on Heterogeneous Clusters

Hu, Yang; Zhou, Huan; de Laat, Cees; Zhao, Zhiming


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/dd0a136e-aa44-42d4-83e2-92a6ae43ed7f/2018.europar-cameraready.pdf"
      }, 
      "checksum": "md5:caf4c5d3c939e06dd024e60424828942", 
      "bucket": "dd0a136e-aa44-42d4-83e2-92a6ae43ed7f", 
      "key": "2018.europar-cameraready.pdf", 
      "type": "pdf", 
      "size": 534844
    }
  ], 
  "owners": [
    26570
  ], 
  "doi": "10.1007/978-3-319-96983-1_26", 
  "stats": {
    "version_unique_downloads": 42.0, 
    "unique_views": 10.0, 
    "views": 11.0, 
    "version_views": 11.0, 
    "unique_downloads": 42.0, 
    "version_unique_views": 10.0, 
    "volume": 22463448.0, 
    "version_downloads": 42.0, 
    "downloads": 42.0, 
    "version_volume": 22463448.0
  }, 
  "links": {
    "doi": "https://doi.org/10.1007/978-3-319-96983-1_26", 
    "latest_html": "https://zenodo.org/record/5501723", 
    "bucket": "https://zenodo.org/api/files/dd0a136e-aa44-42d4-83e2-92a6ae43ed7f", 
    "badge": "https://zenodo.org/badge/doi/10.1007/978-3-319-96983-1_26.svg", 
    "html": "https://zenodo.org/record/5501723", 
    "latest": "https://zenodo.org/api/records/5501723"
  }, 
  "created": "2021-09-12T06:52:37.489235+00:00", 
  "updated": "2021-09-13T01:48:22.610753+00:00", 
  "conceptrecid": "5501722", 
  "revision": 2, 
  "id": 5501723, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.1007/978-3-319-96983-1_26", 
    "version": "camera ready", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "title": "ECSched: Efficient Container Scheduling on Heterogeneous Clusters", 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "5501722"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "5501723"
          }
        }
      ]
    }, 
    "grants": [
      {
        "code": "676247", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::676247"
        }, 
        "title": "A Europe-wide Interoperable Virtual Research Environment to Empower Multidisciplinary Research Communities and Accelerate Innovation and Collaboration", 
        "acronym": "VRE4EIC", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }, 
      {
        "code": "654182", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::654182"
        }, 
        "title": "Environmental Research Infrastructures Providing Shared Solutions for Science and Society", 
        "acronym": "ENVRI PLUS", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }, 
      {
        "code": "643963", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::643963"
        }, 
        "title": "Software Workbench for Interactive, Time Critical and Highly self-adaptive cloud applications", 
        "acronym": "SWITCH", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }
    ], 
    "keywords": [
      "Container", 
      "scheduling", 
      "Cloud computing"
    ], 
    "publication_date": "2018-08-14", 
    "creators": [
      {
        "affiliation": "University of Amsterdam", 
        "name": "Hu, Yang"
      }, 
      {
        "affiliation": "University of Amsterdam", 
        "name": "Zhou, Huan"
      }, 
      {
        "affiliation": "University of Amsterdam", 
        "name": "de Laat, Cees"
      }, 
      {
        "orcid": "0000-0002-6717-9418", 
        "affiliation": "University of Amsterdam", 
        "name": "Zhao, Zhiming"
      }
    ], 
    "meeting": {
      "acronym": "Euro-Par 2018", 
      "url": "https://europar2018.org/", 
      "dates": "August 27-31, 2018", 
      "place": "Turing, Italy", 
      "title": "Euro-Par 2018 - 24th International European Conference on Parallel and Distributed Computing"
    }, 
    "access_right": "open", 
    "resource_type": {
      "subtype": "conferencepaper", 
      "type": "publication", 
      "title": "Conference paper"
    }, 
    "description": "<p>Operating system (OS) containers are becoming increasingly popular in cloud computing for improving productivity and code porta-bility. However, container scheduling on large heterogeneous cluster is quite challenging. Recent research on cluster scheduling focuses either on scheduling speed to quickly assign resources, or on scheduling quality to improve application performance and cluster utilization. In this paper, we propose ECSched, an efficient container scheduler that can make high-quality and fast placement decisions for concurrent deployment requests on heterogeneous clusters. We map the scheduling problem to a graphic data structure and model it as minimum cost flow problem (MCFP). We implement ECSched based on our cost model, which encodes the deployment requirements of requested containers. In the evaluation, we show that ECSched exceeds the placement quality of existing container schedulers with relatively small overheads, while providing 1.1&times; better resource efficiency and 1.3&times; lower average container completion time.</p>"
  }
}
11
42
views
downloads
Views 11
Downloads 42
Data volume 22.5 MB
Unique views 10
Unique downloads 42

Share

Cite as