We present a machine learning model to predict pharmacogenetic (PGx) guidelines using a novel dataset assembled from the PharmGKB knowledge base, incorporating chemical structure, genetic information and recommendation annotations. To facilitate modeling, free text recommendations were processed via domain expertise assisted NLP to create three categories: Standard Dose, Adjusted Dose, and Alternate Drug. We compared several models including Multi-Layer Perceptron, K Nearest Neighbors, Random Forest, Logistic Regression, Linear SVC, and XGBoost. XGBoost excelled, combining predictive power with explainability, achieving an accuracy of 89.14% and F1 scores from 0.85-0.90, with precision and recall of 0.83-0.97 and 0.82-0.97 respectively. Such models can accelerate PGx guidelines, enhancing personalized medicine in clinical settings.

Explainability/Interpretability in machine learning applications is becoming critical, with legal and industry requirements demanding human understandable machine learning results. We describe the additional complexities that occur when a known interpretability technique (canary models) is applied to a real production scenario. We furthermore argue that reproducibility is a key feature in practical usages of such interpretability techniques in production scenarios. With this motivation, we present a production ML reproducibility solution, namely a comprehensive time ordered event sequence for machine learning applications. We demonstrate how our approach can bring this known common interpretability technique into production viability. We further present the system design and early performance characteristics of our reproducibility solution.

Snapshots are a common and heavily relied upon feature in storage systems. The high performance of flash-based storage systems brings new, more stringent, requirements for this classic capability. We present ioSnap, a flash optimized snapshot system. Through careful design exploiting common snapshot usage patterns and flash oriented optimizations, including leveraging native characteristics of Flash Translation Layers, ioSnap delivers low-overhead snapshots with minimal disruption to foreground traffic. Through our evaluation, we show that ioSnap incurs negligible performance overhead during normal operation, and that common-case operations such as snapshot creation and deletion incur little cost. We also demonstrate techniques to mitigate the performance impact on foreground I/O during intensive snapshot operations such as activation. Overall, ioSnap represents a case study of how to integrate snapshots into a modern, well-engineered flash-based storage system.

Flash memory is widely used for its fast random I/O access performance in a gamut of enterprise storage applications. However, due to the limited endurance and asymmetric write performance of flash memory, minimizing writes to a flash device is critical for both performance and endurance. Previous studies have focused on flash memory as a candidate for primary storage devices; little is known about its behavior as a Solid State Cache (SSC) device. In this paper, we propose HEC, a High Endurance Cache that aims to improve overall device endurance via reduced media writes and erases while maximizing cache hit rate performance. We analyze the added write pressures that cache workloads place on flash devices and propose optimizations at both the cache and flash management layers to improve endurance while maintaining or increasing cache hit rate. We demonstrate the individual and cumulative contributions of cache admission policy, cache eviction policy, flash garbage collection policy, and flash device configuration on a) hit rate, b) overall writes, and c) erases as seen by the SSC device. Through our improved cache and flash optimizations, 83% of the analyzed workload ensembles achieved increased or maintained hit rate with write reductions up to 20x, and erase count reductions up to 6x.