Datasize-Aware High Dimensional Configurations Auto-Tuning of In-Memory Cluster Computing

Abstract

In-Memory cluster Computing (IMC) frameworks (e.g., Spark) have become increasingly important because they typically achieve more than 10× speedups over the traditional On-Disk cluster Computing (ODC) frameworks for iterative and interactive applications. Like ODC, IMC frameworks typically run the same given programs repeatedly on a given cluster with similar input dataset size each time. It is challenging to build performance model for IMC program because: 1) the performance of IMC programs is more sensitive to the size of input dataset, which is known to be difficult to be incorporated into a performance model due to its complex effects on performance; 2) the number of performance-critical configuration parameters in IMC is much larger than ODC (more than 40 vs. around 10), the high dimensionality requires more sophisticated models to achieve high accuracy. To address this challenge, we propose DAC, a datasize-aware auto-tuning approach to efficiently identify the high dimensional configuration for a given IMC program to achieve optimal performance on a given cluster. DAC is a significant advance over the state-of-the-art because it can take the size of input dataset and 41 configuration parameters as the parameters of the performance model for a given IMC program, --- unprecedented in previous work. It is made possible by two key techniques: 1) Hierarchical Modeling (HM), which combines a number of individual sub-models in a hierarchical manner; 2) Genetic Algorithm (GA) is employed to search the optimal configuration. To evaluate DAC, we use six typical Spark programs, each with five different input dataset sizes. The evaluation results show that DAC improves the performance of six typical Spark programs, each with five different input dataset sizes compared to default configurations by a factor of 30.4x on average and up to 89x. We also report that the geometric mean speedups of DAC over configurations by default, expert, and RFHOC are 15.4x, 2.3x, and 1.5x, respectively.