Model-driven autoscaling for hadoop clusters
In this paper, we present the design and implementation of a model-driven auto scaling solution for Hadoop clusters. We first develop novel performance models for Hadoop workloads that relate job completion times to various workload and system parameters such as input size and resource allocation. We then employ statistical techniques to tune the models for specific workloads, including Terasort and K-means. Finally, we employ the tuned models to determine the resources required to successfully complete the Hadoop jobs as per the user-specified response time SLA. We implement our solution on an Open Stack-based cloud cluster running Hadoop. Our experimental results across different workloads demonstrate the auto scaling capabilities of our solution, and enable significant resource savings without compromising performance.