Abstract-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, the number of nodes comprising the underlying cluster. Experimental results demonstrate that OptEx yields a mean relative error of 6% 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 cost optimal cluster composition for running a given Spark job under an SLO deadline with an accuracy of 98%.
To predict the completion time of a job and the cost of the cluster with a deadline, Subhajit et al. REF proposed a job complete time model based on the amount of input data, the size of the underlying cluster nodes and the number of iterations.
OptEx: A Deadline-Aware Cost Optimization Model for Spark
2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)
2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)
Computer Science