Collaborative Research: CNS Core: Medium: Optimizing Storage Caches via Adaptive and Reconfigurable Tiering

New types of data storage and memory devices are being developed and released, but they have very different properties, such as speeds, costs, sizes, reliability, and energy. With these new options, there is an opportunity to reduce the cost or environmental impact of storage while improving its reliability and performance. To realize these benefits, this project explores techniques to use new storage devices together. By exploring when and how to move data between “tiers” of new storage, as well as automatically determining what devices to use in data storage tiers, the project dramatically improves storage for both providers and the end users. This project analyzes methods to detect optimal reconfiguration points using machine-learning and time-series techniques via three interrelated thrusts. In the first thrust, accurate, analytical, multi-tier tail latency models are developed using queuing theory. Then, the project builds an efficient platform to simulate configurations and investigates methods to estimate tiered-cache reconfiguration costs. Finally, lightweight, low-overhead, accurate sampling techniques are explored for running systems, to quickly detect significant input/output and cache behavior changes. In response, this project further builds techniques to reconfigure a tiered-cache on running systems with minimal interference. Empirical case studies are applied to Memcached and Kubernetes containers. The storage community benefits from multiple artifacts this project produces: an open-source versatile multi-tier cache simulator, workload and analytical latency models, several case studied systems, a database of metrics from empirically evaluated devices, and publications reporting unexpected or counter-intuitive results. Storage consumers (e.g., companies) can simulate many “what-if” scenarios before actually purchasing any hardware, so as to avoid under- or over-provisioning. This project develops new course modules including short video tutorials. Several students, including women and members of underrepresented groups, are mentored and trained in research techniques. The project's artifacts—software, source code, data sets, multi-tier cache simulator, traces, and results—are all embodied in a system we call "MTCache: Multi-Tier Cache". Results will be disseminated using peer-reviewed publications and arxiv.org. All artifacts will be made public through the project Website: https://www.filesystems.org/mtcache. The project plans to maintain that site for at least ten years following the end of the project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.