Tuneful: An Online Significance-Aware Configuration Tuner for Big Data Analytics

Abstract

Distributed analytics engines such as Spark are a common choice for processing extremely large datasets. However, finding good configurations for these systems remains challenging, with each workload potentially requiring a different setup to run optimally. Using suboptimal configurations incurs significant extra runtime costs. %Furthermore, Spark and similar platforms are gaining traction within data-scientists communities where awareness of such issues is relatively low. We propose Tuneful, an approach that efficiently tunes the configuration of in-memory cluster computing systems. Tuneful combines incremental Sensitivity Analysis and Bayesian optimization to identify near-optimal configurations from a high-dimensional search space, using a small number of executions. This setup allows the tuning to be done online, without any previous training. Our experimental results show that Tuneful reduces the search time for finding close-to-optimal configurations by 62\% (at the median) when compared to existing state-of-the-art techniques. This means that the amortization of the tuning cost happens significantly faster, enabling practical tuning for new classes of workloads.

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Cited by in corpus (3)

• Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs using Graph Propagation
• Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts
• C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds