A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a significant impact on the entire data system architecture. The multiple-decadelong journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question:“What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns?”To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore's law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage components to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which profoundly impacts the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy allowing for better hardware utilization and, ultimately, better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware and the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control.

This paper presents Unikernel Linux (UKL), a path toward integrating unikernel optimization techniques in Linux, a general purpose operating system. UKL adds a configuration option to Linux allowing for a single, optimized process to link with the kernel directly, and run at supervisor privilege. This UKL process does not require application source code modification, only a re-link with our, slightly modified, Linux kernel and glibc. Unmodified applications show modest performance gains out of the box, and developers can further optimize applications for more significant gains (e.g. 26% throughput improvement for Redis). UKL retains support for co-running multiple user level processes capable of communicating with the UKL process using standard IPC. UKL preserves Linux's battle-tested codebase, community, and ecosystem of tools, applications, and hardware support. UKL runs both on bare-metal and virtual servers and supports multi-core execution. The changes to the Linux kernel are modest (1250 LOC).

A key design decision for data systems is whether they follow the row-store or the column-store paradigm. The former supports transactional workloads, while the latter is better for analytical queries. This decision has a significant impact on the entire data system architecture. The multiple-decade-long journey of these two designs has led to a new family of hybrid transactional/analytical processing (HTAP) architectures. Several efforts have been proposed to reap the benefits of both worlds by proposing systems that maintain multiple copies of data (in different physical layouts) and convert them into the desired layout as required. Due to data duplication, the additional necessary bookkeeping, and the cost of converting data between different layouts, these systems compromise between efficient analytics and data freshness. We depart from existing designs by proposing a radically new approach. We ask the question:"What if we could access any layout and ship only the relevant data through the memory hierarchy by transparently converting rows to (arbitrary groups of) columns?".To achieve this functionality, we capitalize on the reinvigorated trend of hardware specialization (that has been accelerated due to the tapering of Moore's law) to propose Relational Fabric, a near-data vertical partitioner that allows memory or storage components to perform on-the-fly transparent data transformation. By exposing an intuitive API, Relational Fabric pushes vertical partitioning to the hardware, which profoundly impacts the process of designing and building data systems. (A) There is no need for data duplication and layout conversion, making HTAP systems viable using a single layout. (B) It simplifies the memory and storage manager that needs to maintain and update a single data layout. (C) It reduces unnecessary data movement through the memory hierarchy allowing for better hardware utilization and, ultimately, better performance. In this paper, we present Relational Fabric for both memory and storage. We present our initial results on Relational Fabric for in-memory systems and discuss the challenges of building this hardware and the opportunities it brings for simplicity and innovation in the data system software stack, including physical design, query optimization, query evaluation, and concurrency control.

Transactional and analytical database management systems (DBMS) typically employ different data layouts: row-stores for the first and column-stores for the latter. In order to bridge the requirements of the two without maintaining two systems and two (or more) copies of the data, our proposed system Relational Memory employs specialized hardware that transforms the base row table into arbitrary column groups at query execution time. This approach maximizes the cache locality and is easy to use via a simple abstraction that allows transparent on-the-fly data transformation. Here, we demonstrate how to deploy and use Relational Memory via four representative scenarios. The demonstration uses the full-stack implementation of Relational Memory on the Xilinx Zynq UltraScale+ MPSoC platform. Conference participants will interact with Relational Memory deployed in the actual platform.

The proliferation of multi-core, accelerator-enabled embedded systems has introduced new opportunities to consolidate real-time systems of increasing complexity. But the road to build confidence on the temporal behavior of co-running applications has presented formidable challenges. Most prominently, the main memory subsystem represents a performance bottleneck for both CPUs and accelerators. And industry-viable frameworks for full-system main memory management and performance analysis are past due. In this paper, we propose our Envelope-aWare Predictive model, or E-WarP for short. E-WarP is a methodology and technological framework to: (1) analyze the memory demand of applications following a profile-driven approach; (2) make realistic predictions on the temporal behavior of workload deployed on CPUs and accelerators; and (3) perform saturation-aware system consolidation. This work aims at providing the technological foundations as well as the theoretical grassroots for truly workload-aware analysis of real-time systems. We provide a full implementation of our techniques on a commercial platform (NXP S32V234) and make two key observations. First, we achieve, on average, a 6% overprediction on the runtime of bandwidth-regulated applications. Second, we experimentally validate that the calculated bounds hold if the main memory subsystem operates below saturation.

Unikernels have demonstrated enormous advantages over Linux in many important domains, causing some to propose that the days of Linux's dominance may be coming to an end. On the contrary, we believe that unikernels' advantages represent the next natural evolution for Linux, as it can adopt the best ideas from the unikernel approach and, along with its battle-tested codebase and large open source community, continue to dominate. In this paper, we posit that an upstreamable unikernel target is achievable from the Linux kernel, and, through an early Linux unikernel prototype, demonstrate that some simple changes can bring dramatic performance advantages.