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LOCATION:D173
DTSTART;TZID=America/Chicago:20181111T103000
DTEND;TZID=America/Chicago:20181111T105500
UID:submissions.supercomputing.org_SC18_sess163_ws_works101@linklings.com
SUMMARY:Reduction of Workflow Resource Consumption Using a Density-based C
 lustering Model
DESCRIPTION:Qimin Zhang (Chinese Academy of Sciences) and Nathaniel Kremer
 -Herman, Benjamin Tovar, and Douglas Thain (University of Notre Dame)\n\nO
 ften times, a researcher running a scientific workflow will ask for orders
  of magnitude too few or too many resources to run their workflow. If the 
 resource requisition is too small, the job may fail due to resource exhaus
 tion; if it is too large, resources will be wasted though job may succeed.
  It would be ideal to achieve a near-optimal number of resources the workf
 low runs to ensure all jobs succeed and minimize resource waste. We presen
 t a strategy for solving the resource allocation problem: (1) resources co
 nsumed by each job are recorded by a resource monitor tool; (2) a density-
 based clustering model is proposed for discovering clusters in all jobs; (
 3) a maximal resource requisition is calculated as the ideal number of eac
 h cluster. We ran experiments with a synthetic workflow of homogeneous tas
 ks as well as the bioinformatics tools Lifemapper, SHRIMP, BWA and BWA-GAT
 K to capture the inherent nature of resource consumption of a workflow, th
 e clustering allowed by the model, and its usefulness in real workflows. I
 n Lifemapper, the least time saving, cores saving, memory saving, and disk
  saving are 13.82%, 16.62%, 49.15%, and 93.89%, respectively. In SHRIMP, B
 WA, and BWA-GATK, the least cores saving, memory saving and disk saving ar
 e 50%, 90.14%, and 51.82%, respectively.  Compared with fixed resource all
 ocation strategy, our approach provide a noticeable reduction of workflow 
 resource consumption.\n\nTag: Reproducibility, Scientific Computing, Scien
 tific Workflows, Workflows, HPC, Data Intensive\n\nRegistration Category: 
 Workshop Reg Pass\n\n
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