Smartpick: Workload Prediction for Serverless-enabled Scalable Data Analytics Systems
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Many data analytic systems have adopted a newly emerging compute resource, serverless (SL), to handle data analytics queries in a timely and cost-efficient manner, i.e., serverless data analytics. While these systems can start processing queries quickly thanks to the agility and scalability of SL, they may encounter performance- and cost-bottlenecks based on workloads due to SL's worse performance and more expensive cost than traditional compute resources, e.g., virtual machine (VM). In this project, we introduce Smartpick, a SL-enabled scalable data analytics system that exploits SL and VM together to realize composite benefits, i.e., agility from SL and better performance with reduced cost from VM. Smartpick uses a machine learning prediction scheme, decision-tree based Random Forest with Bayesian Optimizer, to determine SL and VM configurations, i.e., how many SL and VM instances for queries, that meet cost-performance goals. Smartpick offers a knob for applications to allow them to explore a richer cost-performance tradeoff space opened by exploiting SL and VM together. To maximize the benefits of SL, Smartpick supports a simple but strong mechanism, called relay-instances. Smartpick also supports event-driven prediction model retraining to deal with workload dynamics. A Smartpick prototype was implemented on Spark and deployed on live test-beds, Amazon AWS and Google Cloud Platform. Evaluation results indicate 97.05 prediction accuracies respectively with up to 50 the baselines. The results also confirm that Smartpick allows data analytics applications to navigate the richer cost-performance tradeoff space efficiently and to handle workload dynamics effectively and automatically.
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