Mitigating Tail Catastrophe in Steered Database Query Optimization with Risk-Averse Contextual Bandits
Keywords: risk-aversion, contextual bandits, query optimization
TL;DR: This paper develops the first risk-averse contextual bandit algorithm with an online regret guarantee, and mitigates the performance regressions in a database query optimization scenario.
Abstract: Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonetheless we exhibit the first risk-averse contextual bandit algorithm with an online regret guarantee. We apply the technique to a self-tuning software scenario in a production exascale data processing system, where worst-case outcomes should be avoided.
Submission Number: 8
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