ADWISE: Adaptive Window-based Streaming Edge Partitioning for High-Speed Graph Processing

TL;DR

ADWISE is a window-based streaming graph partitioning algorithm that dynamically balances partitioning quality and latency, significantly reducing total graph analysis time for large, complex graphs.

Contribution

It introduces a novel adaptive window-based streaming partitioning method that improves partitioning quality by selecting optimal edges, balancing latency and quality dynamically.

Findings

Reduces total graph analysis latency by up to 47%

Balances partitioning and processing latency effectively

Outperforms state-of-the-art partitioning algorithms

Abstract

In recent years, the graph partitioning problem gained importance as a mandatory preprocessing step for distributed graph processing on very large graphs. Existing graph partitioning algorithms minimize partitioning latency by assigning individual graph edges to partitions in a streaming manner --- at the cost of reduced partitioning quality. However, we argue that the mere minimization of partitioning latency is not the optimal design choice in terms of minimizing total graph analysis latency, i.e., the sum of partitioning and processing latency. Instead, for complex and long-running graph processing algorithms that run on very large graphs, it is beneficial to invest more time into graph partitioning to reach a higher partitioning quality --- which drastically reduces graph processing latency. In this paper, we propose ADWISE, a novel window-based streaming partitioning algorithm that increases the partitioning quality by always choosing the best edge from a set of edges for assignment to a partition. In doing so, ADWISE controls the partitioning latency by adapting the window size dynamically at run-time. Our evaluations show that ADWISE can reach the sweet spot between graph partitioning latency and graph processing latency, reducing the total latency of partitioning plus processing by up to 23-47 percent compared to the state-of-the-art.