GRASS: trimming stragglers in approximation analytics
Ganesh Ananthanarayanan, Michael Chien‐Chun Hung, Xiaoqi Ren, Ion Gabriel Stoica, Adam Wierman, Minlan Yu · CaltechAUTHORS (California Institute of Technology) · 2014
In big data analytics timely results, even if based on only part of the data, are often good enough. For this reason, approximation jobs, which have deadline or er-ror bounds and require only a subset of their tasks to complete, are projected to dominate big data workloads. Straggler tasks are an important hurdle when designing approximate data analytic frameworks, and the widely adopted approach to deal with them is speculative ex-ecution. In this paper, we present GRASS, which care-fully uses speculation to mitigate the impact of stragglers in approximation jobs. The design of GRASS is based on first principles analysis of the impact of speculative copies. GRASS delicately balances immediacy of im-proving the approximation goal with the long term impli-cations of using extra resources for speculation. Evalua-tions with production workloads from Facebook and Mi-crosoft Bing in an EC2 cluster of 200 nodes shows that GRASS increases accuracy of deadline-bound jobs by 47 % and speeds up error-bound jobs by 38%. GRASS’s design also speeds up exact computations, making it a unified solution for straggler mitigation. 1