Sage

Abstract

We propose Sage, a system for uncertain network analysis. Algorithms for uncertain network analysis require large amounts of memory and computing resources as they sample a large number of network instances and run analysis on them. Sage makes uncertain network analysis simple and efficient. By extending the edge-centric programming model, Sage makes writing sampling-based analysis algorithms as simple as writing conventional graph algorithms in Pregel-like systems. Moreover, Sage proposes four optimization techniques, namely, deterministic sampling, hybrid gathering, schedule-aware caching, and copy-on-write attributes, that exploit common properties of uncertain network analysis. Extensive evaluation of Sage with eight algorithms on six real-world networks shows that the four optimizations in Sage jointly improve performance by up to 13.9X and on average 2.7X.