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With the increasing dominance of SSDs for local storage, today's network mounted virtual disks can no longer offer competitive performance. We propose a Log-Structured Virtual Disk (LSVD) that couples log-structured approaches at both the cache and storage layer to provide a virtual disk on top of S3-like storage. Both cache and backend store are order-preserving, enabling LSVD to provide strong consistency guarantees in case of failure. Our prototype demonstrates that the approach preserves all the advantages of virtual disks, while offering dramatic performance improvements over not only commonly used virtual disks, but the same disks combined with inconsistent (i.e. unsafe) local caching. 

With the increasing dominance of SSDs for local storage, today's network mounted virtual disks can no longer offer competitive performance. We propose a Log-Structured Virtual Disk (LSVD) that couples log-structured approaches at both the cache and storage layer to provide a virtual disk on top of S3-like storage. Both cache and backend store are order-preserving, enabling LSVD to provide strong consistency guarantees in case of failure. Our prototype demonstrates that the approach preserves all the advantages of virtual disks, while offering dramatic performance improvements over not only commonly used virtual disks, but the same disks combined with inconsistent (i.e. unsafe) local caching. 

Shingled Magnetic Recording (SMR) may be combined with conventional (re-writable) recording on the same drive; in host-managed drives shipping today this capability is used to provide a small number of re-writable zones, typically totaling a few tens of GB. Although these re-writable zones are widely used by SMR-aware applications, the literature to date has ignored them and focused on fully-shingled devices. We describe μCache, an SMR translation layer (STL) using re-writable (mutable) zones to take advantage of both workload spatial and temporal locality to reduce the garbage collection overhead resulted from out-of-place writes. In μCache the volume LBA space is divided into fixed -sized buckets and, on write access, the corresponding bucket is copied (promoted) to the re-writable zones, allowing subsequent writes to the same bucket be served in - place resulting in fewer garbage collection cycles. We evaluate μCache in simulation against real-world traces and show that with appropriate parameters it is able to hold the entire write working set of most workloads in re-writable storage, virtually eliminating garbage collection overhead. We also emulate μCache by replaying its translated traces against actual drive and show that 1) it outperforms its examined counterpart, an E-region based translation approach on average by 2x and up to 5.1x, and 2) it incurs additional latency only for a small fraction of write operations, (up to 10%) when compared with conventional non-shingled disks. 

Kariz is a new architecture for caching data from datalakes accessed, potentially concurrently, by multiple analytic platforms. It integrates rich information from analytics platforms with global knowledge about demand and resource availability to enable sophisticated cache management and prefetching strategies that, for example, combine historical run time information with job dependency graphs (DAGs), information about the cache state and sharing across compute clusters. Our prototype supports multiple analytic frameworks (Pig/Hadoop and Spark), and we show that the required changes are modest. We have implemented three algorithms in Kariz for optimizing the caching of individual queries (one from the literature, and two novel to our platform) and three policies for optimizing across queries from, potentially, multiple different clusters. With an algorithm that fully exploits the rich information available from Kariz, we demonstrate major speedups (as much as 3×) for TPC-H and TPC-DS.