BPF-oF: Storage Function Pushdown Over the Network

Storage disaggregation, wherein storage is accessed over the network, is popular because it allows applications to independently scale storage capacity and bandwidth based on dynamic application demand. However, the added network processing introduced by disaggregation can consume significant CPU resources. In many storage systems, logical storage operations (e.g., lookups, aggregations) involve a series of simple but dependent I/O access patterns. Therefore, one way to reduce the network processing overhead is to execute dependent series of I/O accesses at the remote storage server, reducing the back-and-forth communication between the storage layer and the application. We refer to this approach as \emph{remote-storage pushdown}. We present BPF-oF, a new remote-storage pushdown protocol built on top of NVMe-oF, which enables applications to safely push custom eBPF storage functions to a remote storage server. The main challenge in integrating BPF-oF with storage systems is preserving the benefits of their client-based in-memory caches. We address this challenge by designing novel caching techniques for storage pushdown, including splitting queries into separate in-memory and remote-storage phases and periodically refreshing the client cache with sampled accesses from the remote storage device. We demonstrate the utility of BPF-oF by integrating it with three storage systems, including RocksDB, a popular persistent key-value store that has no existing storage pushdown capability. We show BPF-oF provides significant speedups in all three systems when accessed over the network, for example improving RocksDB's throughput by up to 2.8$\times$ and tail latency by up to 2.6$\times$

Enabling Hyperscale Web Services

Modern web services such as social media, online messaging, web search, video streaming, and online banking often support billions of users, requiring data centers that scale to hundreds of thousands of servers, i.e., hyperscale. In fact, the world continues to expect hyperscale computing to drive more futuristic applications such as virtual reality, self-driving cars, conversational AI, and the Internet of Things. This dissertation presents technologies that will enable tomorrow’s web services to meet the world’s expectations. The key challenge in enabling hyperscale web services arises from two important trends. First, over the past few years, there has been a radical shift in hyperscale computing due to an unprecedented growth in data, users, and web service software functionality. Second, modern hardware can no longer support this growth in hyperscale trends due to a decline in hardware performance scaling. To enable this new hyperscale era, hardware architects must become more aware of hyperscale software needs and software researchers can no longer expect unlimited hardware performance scaling. In short, systems researchers can no longer follow the traditional approach of building each layer of the systems stack separately. Instead, they must rethink the synergy between the software and hardware worlds from the ground up. This dissertation establishes such a synergy to enable futuristic hyperscale web services. This dissertation bridges the software and hardware worlds, demonstrating the importance of that bridge in realizing efficient hyperscale web services via solutions that span the systems stack. The specific goal is to design software that is aware of new hardware constraints and architect hardware that efficiently supports new hyperscale software requirements. This dissertation spans two broad thrusts: (1) a software and (2) a hardware thrust to analyze the complex hyperscale design space and use insights from these analyses to design efficient cross-stack solutions for hyperscale computation. In the software thrust, this dissertation contributes uSuite, the first open-source benchmark suite of web services built with a new hyperscale software paradigm, that is used in academia and industry to study hyperscale behaviors. Next, this dissertation uses uSuite to study software threading implications in light of today’s hardware reality, identifying new insights in the age-old research area of software threading. Driven by these insights, this dissertation demonstrates how threading models must be redesigned at hyperscale by presenting an automated approach and tool, uTune, that makes intelligent run-time threading decisions. In the hardware thrust, this dissertation architects both commodity and custom hardware to efficiently support hyperscale software requirements. First, this dissertation characterizes commodity hardware’s shortcomings, revealing insights that influenced commercial CPU designs. Based on these insights, this dissertation presents an approach and tool, SoftSKU, that enables cheap commodity hardware to efficiently support new hyperscale software paradigms, improving the efficiency of real-world web services that serve billions of users, saving millions of dollars, and meaningfully reducing the global carbon footprint. This dissertation also presents a hardware-software co-design, uNotify, that redesigns commodity hardware with minimal modifications by using existing hardware mechanisms more intelligently to overcome new hyperscale overheads. Next, this dissertation characterizes how custom hardware must be designed at hyperscale, resulting in industry-academia benchmarking efforts, commercial hardware changes, and improved software development. Based on this characterization’s insights, this dissertation presents Accelerometer, an analytical model that estimates gains from hardware customization. Multiple hyperscale enterprises and hardware vendors use Accelerometer to make well-informed hardware decisions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169802/1/akshitha_1.pd