Software Open Access

IC-PCP profiling: software and data set

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


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    <subfield code="a">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).</subfield>
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    <subfield code="a">&lt;pre&gt;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&amp;#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. &lt;/pre&gt;

&lt;pre&gt;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.&lt;/pre&gt;

&lt;pre&gt;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.&lt;/pre&gt;

&lt;pre&gt;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.&lt;/pre&gt;</subfield>
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