
Be the first to like this
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
F. Petroni, L. Querzoni, K. Daudjee, S. Kamali and G. Iacoboni:
"HDRF: StreamBased Partitioning for PowerLaw Graphs."
In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM), 2015.
Abstract: "Balanced graph partitioning is a fundamental problem that is receiving growing attention with the emergence of distributed graphcomputing (DGC) frameworks. In these frameworks, the partitioning strategy plays an important role since it drives the communication cost and the workload balance among computing nodes, thereby affecting system performance.
However, existing solutions only partially exploit a key characteristic of natural graphs commonly found in the
realworld: their highly skewed powerlaw degree distributions.
In this paper, we propose HighDegree (are) Replicated First (HDRF), a novel streaming vertexcut graph partitioning algorithm that effectively exploits skewed degree distributions by explicitly taking into account vertex degree in the placement decision. We analytically and experimentally evaluate HDRF on both synthetic and realworld graphs and show that it outperforms all existing algorithms in partitioning quality."
Be the first to like this
Be the first to comment