Self-adaptive Executors for Big Data Processing

Abstract

The demand for additional performance due to the rapid increase in the size and importance of data-intensive applications has considerably elevated the complexity of computer architecture. In response, systems offer pre-determined behaviors based on heuristics and then expose a large number of configuration parameters for operators to adjust them to their particular infrastructure. Unfortunately, in practice this leads to a substantial manual tuning effort. In this work, we focus on one of the most impactful tuning decisions in big data systems: the number of executor threads. We first show the impact of I/O contention on the runtime of workloads and a simple static solution to reduce the number of threads for I/O-bound phases. We then present a more elaborate solution in the form of self-adaptive executors which are able to continuously monitor the underlying system resources and detect contentions. This enables the executors to tune their thread pool size dynamically at runtime in order to achieve the best performance. Our experimental results show that being adaptive can significantly reduce the execution time especially in I/O intensive applications such as Terasort and PageRank which see a 34% and 54% reduction in runtime.