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Memory leak is a notorious issue. Despite the extensive efforts, addressing memory leaks in large production cloud systems remains challenging. Existing solutions incur high overhead and/or suffer from high inaccuracies. This paper presents RESIN, a solution designed to holistically address memory leaks in production cloud infrastructure. RESIN takes a divide-and-conquer approach to tackle the challenges. It performs a low-overhead detection first with a robust bucketization-based pivot scheme to identify suspicious leaking entities. It then takes live heap snapshots at appropriate time points in carefully sampled leak entities. RESIN analyzes the collected snapshots for leak diagnosis. Finally, RESIN automatically mitigates detected leaks. RESIN has been running in production in Microsoft Azure for 3 years. It reports on average 24 leak tickets each month with high accuracy and low overhead, and provides effective diagnosis reports. Its results translate into a 41× reduction of VM reboots caused by low memory. 

When a failure occurs in production systems, the highest priority is to quickly mitigate it. Despite its importance, failure mitigation is done in a reactive and ad-hoc way: taking some fixed actions only after a severe symptom is observed. For cloud systems, such a strategy is inadequate. In this paper, we propose a preventive and adaptive failure mitigation service, Narya, that is integrated in a production cloud, Microsoft Azure's compute platform. Narya predicts imminent host failures based on multi-layer system signals and then decides smart mitigation actions. The goal is to avert VM failures. Narya's decision engine takes a novel online experimentation approach to continually explore the best mitigation action. Narya further enhances the adaptive decision capability through reinforcement learning. Narya has been running in production for 15 months. It on average reduces VM interruptions by 26% compared to the previous static strategy.