Characterization of Performance Anomalies in Hadoop

TL;DR

This paper presents a decision tree-based method to characterize performance anomalies in Hadoop by modeling execution time variations with different system settings, enabling anomaly detection and performance insights.

Contribution

The study introduces a decision tree approach to predict execution time ranges and detect anomalies in Hadoop workloads, providing a new tool for performance analysis.

Findings

99% of samples' execution times fell within predicted ranges

Impact of execution parameters relates to their position in the decision tree

Configuration insights can be derived from the trained decision tree

Abstract

With the huge variety of data and equally large-scale systems, there is not a unique execution setting for these systems which can guarantee the best performance for each query. In this project, we tried so study the impact of different execution settings on execution time of workloads by varying them one at a time. Using the data from these experiments, a decision tree was built where each internal node represents the execution parameter, each branch represents value chosen for the parameter and each leaf node represents a range for execution time in minutes. The attribute in the decision tree to split the dataset on is selected based on the maximum information gain or lowest entropy. Once the tree is trained with the training samples, this tree can be used to get approximate range for the expected execution time. When the actual execution time differs from this expected value, a performance anomaly can be detected. For a test dataset with 400 samples, 99% of samples had actual execution time in the range predicted time by the decision tree. Also on analyzing the constructed tree, an idea about what configuration can give better performance for a given workload can be obtained. Initial experiments suggest that the impact an execution parameter can have on the target attribute (here execution time) can be related to the distance of that feature node from the root of the constructed decision tree. From initial results the percent change in the values of the target attribute for various value of the feature node which is closer to the root is 6 times larger than when that same iii feature node is away from the root node. This observation will depend on how well the decision tree was trained and may not be true for every case.