This paper introduces Heimdall, a highly accurate and efficient machine learning-powered I/O admission policy for flash storage, designed to operate in a black-box manner. We make domain-specific innovations in various ML stages by introducing accurate period-based labeling, 3-stage noise filtering, in-depth feature engineering, and fine-grained tuning, which together improve the decision accuracy from 67% up to 93%. We perform various deployment optimizations to reach a sub-μs inference latency and a small, 28KB, memory overhead. With 500 unbiased random experiments derived from production traces, we show Heimdall delivers 15-35% lower average I/O latency compared to the state of the art and up to 2x faster to a baseline. Heimdall is ready for user-level, in-kernel, and distributed deployments.

Modern recommendation systems, primarily driven by deep-learning models, depend on fast model inferences to be useful. To tackle the sparsity in the input space, particularly for categorical variables, such inferences are made by storing increasingly large embedding vector (EV) tables in memory. A core challenge is that the inference operation has an all-or-nothing property: each inference requires multiple EV table lookups, but if any memory access is slow, the whole inference request is slow. In our paper, we design, implement and evaluate EVStore, a 3-layer EV table lookup system that harnesses both structural regularity in inference operations and domain-specific approximations to provide optimized caching, yielding up to 23% and 27% reduction on the average and p90 latency while quadrupling throughput at 0.2% loss in accuracy. Finally, we show that at a minor cost of accuracy, EVStore can reduce the Deep Recommendation System (DRS) memory usage by up to 94%, yielding potentially enormous savings for these costly, pervasive systems.

We introduce an ecosystem of contention mitigation supports within the operating system, runtime and library layers. This ecosystem provides an end-to-end request abstraction that enables a uniform type of contention mitigation capabilities, namely request cancellation and delay prediction, that can be stackable together across multiple resource layers. Our evaluation shows that in our ecosystem, multi-resource storage applications are faster by 5-70% starting at 90P (the 90thpercentile) compared to popular practices such as speculative execution and is only 3% slower on average compared to a best-case (no contention) scenario.

We present a fast and scalable testing approach for datacenter/cloud systems such as Cassandra, Hadoop, Spark, and ZooKeeper. The uniqueness of our approach is in its ability to overcome the path/state-space explosion problem in testing workloads with complex interleavings of messages and faults. We introduce three powerful algorithms: state symmetry, event independence, and parallel flips, which collectively makes our approach on average 16x (up to 78x) faster than other state-of-the-art solutions. We have integrated our techniques with 8 popular datacenter systems, successfully reproduced 12 old bugs, and found 10 new bugs --- all were done without random walks or manual checkpoints.

Conventional wisdom states that to improve quality of experience (QoE), web service providers should reduce the median or other percentiles of server-side delays. This work shows that doing so can be inefficient due to user heterogeneity in how the delays impact QoE. From the perspective of QoE, the sensitivity of a request to delays can vary greatly even among identical requests arriving at the service, because they differ in the wide-area network latency experienced prior to arriving at the service. In other words, saving 50ms of server-side delay affects different users differently.

We reveal loopholes of Speculative Execution (SE) implementations under a unique fault model: node-level network throughput degradation. This problem appears in many data-parallel frameworks such as Hadoop MapReduce and Spark. To address this, we present PBSE, a robust, path-based speculative execution that employs three key ingredients: path progress, path diversity, and path-straggler detection and speculation. We show how PBSE is superior to other approaches such as cloning and aggressive speculation under the aforementioned fault model. PBSE is a general solution, applicable to many data-parallel frameworks such as Hadoop/HDFS+QFS, Spark and Flume.