Ashwini Raina will present his FPO "Rethinking System Design with Awareness for cross-layer aspects of datacenter storage" on Friday, May 3, 2024 at 10:30am in CS 302.
The members of his committee are as follows:
Examiners: Michael Freedman (adviser), Wyatt Lloyd, Ravi Netravali
Readers: Amit Levy, Asaf Cidon (Columbia University)
Abstract follows below.
Storage is a critical piece of infrastructure in modern web applications. In recent years, storage technologies employed in building such systems have undergone significant evolution, bringing about novel cost-performance trade-offs. Concurrently, datacenter storage architectures have become increasingly layered. Software systems designed based on outdated assumptions of datacenter storage often result in poor cost-performance trade-offs or suffer from suboptimal performance. This dissertation proposes a new design approach for systems, one that incorporates the awareness of cross-layer aspects of datacenter storage, and validates the effectiveness of this approach through two systems. The first system is PrismDB, a novel key-value store that exploits two extreme ends of the spectrum of modern NVMe storage technologies (3D XPoint and QLC NAND) simultaneously. In recent years, emerging storage technologies have focused on divergent goals: better performance or lower cost. Correspondingly, data systems that employ these technologies are typically optimized either to be fast (but expensive) or cheap (but slow). PrismDB take a different approach: by architecting a storage engine to natively utilize two tiers of fast and low-cost storage technologies, it shows that a Pareto-efficient balance between performance and cost can be achieved. The second system is Fusion, an object store for analytics that is optimized for query pushdown on erasure-coded data. Computation pushdown is a widely adopted technique to reduce latency of highly selective queries in modern OLAP cloud database running on disaggregated storage. However, existing pushdown solutions are inefficient on erasure-coded storage since the analytics file objects get partitioned across storage nodes. Consequently, the storage system must reassemble the object across nodes before executing the query, leading to significant network latency. Fusion addresses this problem by co-designing its erasure coding and file placement topologies, taking into account popular analytics file formats (e.g., Parquet).
It employs a novel stripe construction algorithm that prevents the fragmentation of computable units within an object, and minimizes storage overhead during erasure coding. Overall, this dissertation advocates for designing software systems with an awareness of cross-layer aspects in datacenter storage, and demonstrates the benefits of that approach via two systems: PrismDB and Fusion.