Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs

TL;DR

This paper presents a collaborative method for optimizing cluster configurations for distributed dataflow jobs by leveraging shared historical runtime data and specialized regression models to improve resource utilization and reduce costs.

Contribution

It introduces a novel collaborative approach that uses shared historical data and context-aware regression models to predict runtimes and optimize cluster configurations.

Findings

Shared data improves runtime prediction accuracy.

Context-aware models handle diverse user environments.

Optimized configurations reduce hardware bottlenecks.

Abstract

Analyzing large datasets with distributed dataflow systems requires the use of clusters. Public cloud providers offer a large variety and quantity of resources that can be used for such clusters. However, picking the appropriate resources in both type and number can often be challenging, as the selected configuration needs to match a distributed dataflow job's resource demands and access patterns. A good cluster configuration avoids hardware bottlenecks and maximizes resource utilization, avoiding costly overprovisioning. We propose a collaborative approach for finding optimal cluster configurations based on sharing and learning from historical runtime data of distributed dataflow jobs. Collaboratively shared data can be utilized to predict runtimes of future job executions through the use of specialized regression models. However, training prediction models on historical runtime data that were produced by different users and in diverse contexts requires the models to take these contexts into account.