Load Balancing for MapReduce-based Entity Resolution

TL;DR

This paper presents two novel load balancing approaches for MapReduce-based entity resolution, effectively handling data skew and improving scalability in distributed data processing tasks.

Contribution

It introduces two new skew handling and load balancing methods supporting blocking and preprocessing to enhance MapReduce entity resolution performance.

Findings

Effective load balancing on real cloud infrastructure

Improved scalability for skewed data distributions

Enhanced entity resolution efficiency

Abstract

The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks. In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel. For the complex problem of entity resolution, we propose and evaluate two approaches for such skew handling and load balancing. The approaches support blocking techniques to reduce the search space of entity resolution, utilize a preprocessing MapReduce job to analyze the data distribution, and distribute the entities of large blocks among multiple reduce tasks. The evaluation on a real cloud infrastructure shows the value and effectiveness of the proposed load balancing approaches.