PDL ABSTRACT

Parity Models: Erasure-Coded Resilience for Prediction Serving Systems

SOSP ’19, October 27–30, 2019, Huntsville, ON, Canada.

Jack Kosaian*, K. V. Rashmi*, Shivaram Venkataraman†

* Carnegie Mellon University
† University of Wisconsin-Madison

http://www.pdl.cmu.edu/

Machine learning models are becoming the primary workhorses for many applications. Services deploy models through prediction serving systems that take in queries and return predictions by performing inference on models. Prediction serving systems are commonly run on many machines in cluster settings, and thus are prone to slowdowns and failures that inflate tail latency. Erasure coding is a popular technique for achieving resource-efficient resilience to data unavailability in storage and communication systems. However, existing approaches for imparting erasure-coded resilience to distributed computation apply only to a severely limited class of functions, precluding their use for many serving workloads, such as neural network inference. We introduce parity models, a new approach for enabling erasure-coded resilience in prediction serving systems. A parity model is a neural network trained to transform erasurecoded queries into a form that enables a decoder to reconstruct slow or failed predictions. We implement parity models in ParM, a prediction serving system that makes use of erasure-coded resilience. ParM encodes multiple queries into a “parity query,” performs inference over parity queries using parity models, and decodes approximations of unavailable predictions by using the output of a parity model. We showcase the applicability of parity models to image classification, speech recognition, and object localization tasks. Using parity models, ParM reduces the gap between 99.9th percentile and median latency by up to 3.5×, while maintaining the same median. These results display the potential of parity models to unlock a new avenue to imparting resourceefficient resilience to prediction serving systems.

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Last updated 5 September, 2019