# BigRoots: An Effective Approach for Root-cause Analysis of Stragglers in Big Data System

arXiv: https://arxiv.org/abs/1801.03314 · HTML: https://mlsanity.com/paper/1801.03314/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [SiriusHelper: An LLM Agent-Based Operations Assistant for Big Data Platforms](https://mlsanity.com/paper/2605.00043/) (2026)

## Abstract (arXiv)

Stragglers are commonly believed to have a great impact on the performance of big data system. However, the reason to cause straggler is complicated. Previous works mostly focus on straggler detection, schedule level optimization and coarse-grained cause analysis. These methods cannot provide valuable insights to help users optimize their programs. In this paper, we propose BigRoots, a general method incorporating both framework and system features for root-cause analysis of stragglers in big data system. BigRoots considers features from big data framework such as shuffle read/write bytes and JVM garbage collection time, as well as system resource utilization such as CPU, I/O and network, which is able to detect both internal and external root causes of stragglers. We verify BigRoots by injecting high resource utilization across different system components and perform case studies to analyze different workloads in Hibench. The experimental results demonstrate that BigRoots is effective to identify the root cause of stragglers and provide useful guidance for performance optimization.