Function-as-a-Service (FaaS) serverless computing enables a simple programming model with almost unbounded elasticity. Unfortunately, current FaaS platforms achieve this flexibility at the cost of lower performance for data-intensive applications compared to a serverful deployment. The ability to have computation close to data is a key missing feature. We introduce Palette load balancing, which offers FaaS applications a simple mechanism to express locality to the platform, through hints we term "colors". Palette maintains the serverless nature of the service - users are still not allocating resources - while allowing the platform to place successive invocations related to each other on the same executing node. We compare a prototype of the Palette load balancer to a state-of-the-art locality-oblivious load balancer on representative examples of three applications. For a serverless web application with a local cache, Palette improves the hit ratio by 6x. For a serverless version of Dask, Palette improves run times by 46% and 40% on Task Bench and TPC-H, respectively. On a serverless version of NumS, Palette improves run times by 37%. These improvements largely bridge the gap to serverful implementation of the same systems.

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.

Current caching methods for improving the performance of big-data jobs assume high (e.g., full bi-section) bandwidth; however many enterprise data centers and co-location facilities have large network imbalances due to over-subscription and incremental networking upgrades. We describe D3N, a multi-layer cooperative caching architecture that mitigates network imbalances by caching data on the access side of each layer of a hierarchical network topology, adaptively adjusting cache sizes of each layer based on observed workload patterns and network congestion. We have added (and submitted upstream) a 2-layer D3N cache to the Ceph RADOS Gateway; read bandwidth achieves the 5GB/s speed of our SSDs, and we show that it substantially improves big-data job performance while reducing network traffic.

We introduce FSTL, a Framework for Shingled Translation Layers: a toolkit for implementing host-side block translation layers for Shingled Magnetic Recording (SMR) drives. It provides a Linux kernel implementation of key translation mechanisms (write allocation, LBA translation, map persistence, and consistent copying) while allowing translation policy (e.g. layout, cleaning algorithms, crash recovery) to be implemented in a user-space controller which communicates through an ioctl-based API to the kernel data plane. Due to its use of a journaled write format, FSTL-based translation layers are able to handle synchronous and durable writes to random LBAs at high speed. We describe the architecture and implementation of FSTL, and present two FSTL-based translation layers implemented in 400 lines of Python each. Despite the simplicity of the controllers, experiments show our first translation layer performing 1.5x to 10x better than a drive-managed translation layer on trace replay experiments, with performance roughly comparable to the drive-managed device for real file system-based benchmarks; the second translation layer, based on a full-volume extent map, is shown to offer significantly better performance than prior work. Furthermore, we implement and evaluate three cleaning algorithms to demonstrate how FSTL-based translation layers may be readily modified, while still offering the robustness needed for long-duration benchmarks and use.