Fault Tolerant Frequent Pattern Mining

TL;DR

This paper introduces a novel fault-tolerant FP-Growth algorithm that uses in-memory checkpointing and advanced MPI features to ensure high efficiency and scalability in large-scale data mining tasks.

Contribution

It presents a new parallel, algorithm-level fault-tolerant FP-Growth algorithm leveraging MPI features and dataset memory for checkpointing, with efficient recovery methods.

Findings

Achieves O(1) space complexity for checkpointing.

Demonstrates 20x speed-up over Spark.

Shows efficient checkpointing and recovery on large clusters.

Abstract

FP-Growth algorithm is a Frequent Pattern Min- ing (FPM) algorithm that has been extensively used to study correlations and patterns in large scale datasets. While several researchers have designed distributed memory FP-Growth algorithms, it is pivotal to consider fault tolerant FP-Growth, which can address the increasing fault rates in large scale systems. In this work, we propose a novel parallel, algorithm-level fault-tolerant FP-Growth algorithm. We leverage algorithmic properties and MPI advanced features to guarantee an O(1) space complexity, achieved by using the dataset memory space itself for checkpointing. We also propose a recovery algorithm that can use in-memory and disk-based checkpointing, though in many cases the recovery can be completed without any disk access, and incurring no memory overhead for checkpointing. We evaluate our FT algorithm on a large scale InfiniBand cluster with several large datasets using up to 2K cores. Our evaluation demonstrates excellent efficiency for checkpointing and recovery in comparison to the disk-based approach. We have also observed 20x average speed-up in comparison to Spark, establishing that a well designed algorithm can easily outperform a solution based on a general fault-tolerant programming model.