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Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning
Distributed, Parallel, and Cluster Computing · Submitted 28 Feb 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints.
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The abstract, sentence by sentence
In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner S5P, designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph's edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on PowerGraph.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | The primary goal is to minimize communication overhead and runtime cost. | background 1.00 | 0.05 | 0.03 | asserted 1.00 |
| s2 | However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. | background 0.96 | 0.06 | 0.02 | asserted 0.99 |
| s3 | Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s4 | This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s5 | Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. | gap 0.88 | 0.05 | 0.02 | asserted 1.00 |
| s6 | In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner S5P, designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. | contribution 0.99 | 0.84 | 0.11 | asserted 1.00 |
| s7 | S5P offers high partitioning quality by segregating the graph's edge set into two subsets, head and tail sets. | method 0.96 | 0.69 | 0.05 | asserted 1.00 |
| s8 | Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. | method 1.00 | 0.59 | 0.03 | asserted 0.99 |
| s9 | Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. | result 1.00 | 0.97 | 0.62 | asserted 1.00 |
| s10 | For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. | result 1.00 | 0.93 | 0.15 | asserted 0.66 |
| s11 | Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on PowerGraph. | result 1.00 | 0.96 | 0.29 | asserted 1.00 |
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Original abstract
In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing, where accommodating a graph with non-distributed systems is unattainable. Currently, existing streaming partitioning algorithms exhibit a skew-oblivious nature, yielding satisfactory partitioning results exclusively for specific graph types. In this paper, we propose a novel streaming partitioning algorithm, the Skewness-aware Vertex-cut Partitioner S5P, designed to leverage the skewness characteristics of real graphs for achieving high-quality partitioning. S5P offers high partitioning quality by segregating the graph's edge set into two subsets, head and tail sets. Following processing by a skewness-aware clustering algorithm, these two subsets subsequently undergo a Stackelberg graph game. Our extensive evaluations conducted on substantial real-world and synthetic graphs demonstrate that, in all instances, the partitioning quality of S5P surpasses that of existing streaming partitioning algorithms, operating within the same load balance constraints. For example, S5P can bring up to a 51% improvement in partitioning quality compared to the top partitioner among the baselines. Lastly, we showcase that the implementation of S5P results in up to an 81% reduction in communication cost and a 130% increase in runtime efficiency for distributed graph processing tasks on PowerGraph.
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