Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs
arXiv:2011.07965 · doi:10.1109/BigData50022.2020.9377994
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.
arXiv comment6 pages, 7 figures, 1 table; Associated experiment results: https://github.com/dos-group/c3o-experiments ; Appearence in the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data); Presentation at the 4th International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD). IEEE. 2020
References in corpus (2)
Cited by in corpus (10)
- Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters
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- Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud
- On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds
- Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing
- Collaborative Cluster Configuration for Distributed Data-Parallel Processing: A Research Overview
- C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds
- Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling
- Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts