Deadline-Aware Cost Optimization for Spark

• Published in: IEEE transactions on big data Vol. 7; no. 1; pp. 115 - 127
• Main Authors: Sidhanta, Subhajit, Golab, Wojciech, Mukhopadhyay, Supratik
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-03-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: availability; Big Data; Cloud computing; Clusters; Completion time; Composition; distributed file systems; Distributed systems; Estimation; Input variables; Job shops; middleware; Optimization; Parallel processing; performance evaluation; reliability; serviceability; Sparks; Virtual machining
• ISSN: 2332-7790; 2372-2096
• DOI: 10.1109/TBDATA.2019.2908188

We present OptEx, a closed-form model of job execution on Apache Spark, a popular parallel processing engine. To the best of our knowledge, OptEx is the first work that analytically models job completion time on Spark. The model can be used to estimate the completion time of a given Spark job on a cloud, with respect to the size of the input dataset, the number of iterations, and the number of nodes comprising the underlying cluster. Experimental results demonstrate that OptEx yields a mean relative error of 6 percent in estimating the job completion time. Furthermore, the model can be applied for estimating the cost-optimal cluster composition for running a given Spark job on a cloud under a completion deadline specified in the SLO (i.e., Service Level Objective). We show experimentally that OptEx is able to correctly estimate the required cluster composition for running a given Spark job under a given SLO deadline with an accuracy of 98 percent. We also provide a tool which can classify Spark jobs into job categories based on bisimilarity analysis on lineage graphs collected from the given jobs.