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IC-PCP profiling: software and data set

Taal, Arie; Wang, Junchao; de Laat, Cees; Zhao, Zhiming


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.2652669", 
  "title": "IC-PCP profiling: software and data set", 
  "issued": {
    "date-parts": [
      [
        2019, 
        4, 
        16
      ]
    ]
  }, 
  "abstract": "<pre>We study the scheduling decisions for handling deadline-constrained workflows in the context of planning customized virtual infrastructures in the cloud. We specifically focus on the effects of using different types of greediness in selecting cost-effective virtual machines for the tasks in an application&#39;s workflow graph. The profiling procedure followed demonstrates that for the widely used approach of the partial critical path algorithm a greedy version is preferred to a more stringent version under different stress conditions, from tight to loose deadlines. Representative topologies of workflow applications are used to generate sets of task graph scheduling problems. </pre>\n\n<pre>Monitoring the performance of the partial critical path algorithm with different types of greediness reveals which of the topologies tested are difficult to solve under various stress conditions.</pre>\n\n<pre>It turns out that an invalid outcome of a greedy version of the partial critical path algorithm is more susceptible to become valid via a final refinement cycle than a less greedy version. The procedure outlined in this paper will allow for a systematic study of a specific heuristic in a workflow scheduling method to increase its success in infrastructure planning under different deadline conditions and is proposed to be part of a general profiling framework.</pre>\n\n<pre>All four implementations of the IC-PCP algorithm used in this study as well as the data to produce the performance figures are available at https://bitbucket.org/uva-sne/ic-pcp-profiling/src/master.</pre>", 
  "author": [
    {
      "family": "Taal, Arie"
    }, 
    {
      "family": "Wang, Junchao"
    }, 
    {
      "family": "de Laat, Cees"
    }, 
    {
      "family": "Zhao, Zhiming"
    }
  ], 
  "note": "This research has received funding from the European Union's Horizon 2020 research and innovation program under grant agreements 643963 (SWITCH project), 654182 (ENVRIPLUS project), 676247 (VRE4EIC project), 824068(ENVRI-FAIR project) and 825134(ARTICONF project).", 
  "version": "version 4-16-2019", 
  "type": "article", 
  "id": "2652669"
}
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