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We consider straggler-resilient learning. In many previous works, e.g., inthe coded computing literature, straggling is modeled as random delays that areindependent and identically distributed between workers. However, in manypractical scenarios, a given worker may straggle over an extended period oftime. We propose a latency model that captures this behavior and issubstantiated by traces collected on Microsoft Azure, Amazon Web Services(AWS), and a small local cluster. Building on this model, we propose DSAG, amixed synchronous-asynchronous iterative optimization method, based on thestochastic average gradient (SAG) method, that combines timely and staleresults. We also propose a dynamic load-balancing strategy to further reducethe impact of straggling workers. We evaluate DSAG for principal componentanalysis, cast as a finite-sum optimization problem, of a large genomicsdataset, and for logistic regression on a cluster composed of 100 workers onAWS, and find that DSAG is up to about 50% faster than SAG, and more than twiceas fast as coded computing methods, for the particular scenario that weconsider.

DSAG: A mixed synchronous-asynchronous iterative method for straggler-resilient learning