With the ever-increasing dataset sizes, several file formats such as Parquet, ORC, and Avro have been developed to store data efficiently, save the network, and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25–100 Gb/s and storage devices delivering 1,000,000 reqs/sec, the CPU has become the bottleneck trying to keep up feeding data in and out of these fast devices. The result is that data access libraries executed on single clients are often CPU-bound and cannot utilize the scale-out benefits of distributed storage systems. One attractive solution to this problem is to offload data-reducing processing and filtering tasks to the storage layer. However, modifying legacy storage systems to support compute offloading is often tedious and requires an extensive understanding of the system internals. Previous approaches re-implemented functionality of data processing frameworks and access libraries for a particular storage system, a duplication of effort that might have to be repeated for different storage systems. This paper introduces a new design paradigm that allows ex-tending programmable object storage systems to embed existing, widely used data processing frameworks and access libraries into the storage layer with no modifications. In this approach, data processing frameworks and access libraries can evolve independently from storage systems while leveraging distributed storage systems' scale-out and availability properties. We present Skyhook, an example implementation of our design paradigm using Ceph, Apache Arrow, and Parquet. We provide a brief performance evaluation of Skyhook and discuss key results.

The P-RECS workshop focuses heavily on practical, actionable aspects of reproducibility in broad areas of computational science and data exploration, with special emphasis on issues in which community collaboration can be essential for adopting novel methodologies, techniques and frameworks aimed at addressing some of the challenges we face today. The workshop brings together researchers and experts to share experiences and advance the state of the art in the reproducible evaluation of computer systems, featuring contributed papers and invited talks.

Distributed data processing ecosystems are widespread and their components are highly specialized, such that efficient interoperability is urgent. Recently, Apache Arrow was chosen by the community to serve as a format mediator, providing efficient in-memory data representation. Arrow enables efficient data movement between data processing and storage engines, significantly improving interoperability and overall performance. In this work, we design a new zero-cost data interoperability layer between Apache Spark and Arrow-based data sources through the Arrow Dataset API. Our novel data interface helps separate the computation (Spark) and data (Arrow) layers. This enables practitioners to seamlessly use Spark to access data from all Arrow Dataset API-enabled data sources and frameworks. To benefit our community, we open-source our work and show that consuming data through Apache Arrow is zero-cost: our novel data interface is either on-par or more performant than native Spark.

Popper is an experimentation protocol for conducting scientific explorations and writing academic articles following a DevOps approach. The Popper CLI tool helps researchers automate the execution and validation of an experimentation pipeline. In this tutorial we give an introduction to the concepts and CLI tool, and go over hands-on exercises that help.

HPC and data center scale application developers are abandoning POSIX IO because file system metadata synchronization and serialization overheads of providing strong consistency and durability are too costly - and often unnecessary - for their applications. Unfortunately, designing file systems with weaker consistency or durability semantics excludes applications that rely on stronger guarantees, forcing developers to re-write their applications or deploy them on a different system. We present a framework and API that lets administrators specify their consistency/durability requirements and dynamically assign them to subtrees in the same namespace, allowing administrators to optimize subtrees over time and space for different workloads. We show similar speedups to related work but more importantly, we show performance improvements when we custom fit subtree semantics to applications such as checkpoint-restart (91.7x speedup), user home directories (0.03 standard deviation from optimal), and users checking for partial results (2% overhead).

We introduce quiho, a framework for profiling application performance that can be used in automated performance regression tests. quiho profiles an application by applying sensitivity analysis, in particular statistical regression analysis (SRA), using application-independent performance feature vectors that characterize the performance of machines. The result of the SRA, feature importance specifically, is used as a proxy to identify hardware and low-level system software behavior. The relative importance of these features serve as a performance profile of an application (termed inferred resource utilization profile or IRUP), which is used to automatically validate performance behavior across multiple revisions of an application»s code base without having to instrument code or obtain performance counters. We demonstrate that quiho can successfully discover performance regressions by showing its effectiveness in profiling application performance for synthetically introduced regressions as well as those found in real-world applications.

Storage systems need to support high-performance for special-purpose data processing applications that run on an evolving storage device technology landscape. This puts tremendous pressure on storage systems to support rapid change both in terms of their interfaces and their performance. But adapting storage systems can be difficult because unprincipled changes might jeopardize years of code-hardening and performance optimization efforts that were necessary for users to entrust their data to the storage system. We introduce the programmable storage approach, which exposes internal services and abstractions of the storage stack as building blocks for higher-level services. We also build a prototype to explore how existing abstractions of common storage system services can be leveraged to adapt to the needs of new data processing systems and the increasing variety of storage devices. We illustrate the advantages and challenges of this approach by composing existing internal abstractions into two new higher-level services: a file system metadata load balancer and a high-performance distributed shared-log. The evaluation demonstrates that our services inherit desirable qualities of the back-end storage system, including the ability to balance load, efficiently propagate service metadata, recover from failure, and navigate trade-offs between latency and throughput using leases.

The DOE Extreme-Scale Technology Acceleration Fast Forward Storage and IO Stack project is going to have significant impact on storage systems design within and beyond the HPC community. With phase two of the project starting, it is an excellent opportunity to explore the complete design and how it will address the needs of extreme scale platforms. This paper examines each layer of the proposed stack in some detail along with cross-cutting topics, such as transactions and metadata management. This paper not only provides a timely summary of important aspects of the design specifications but also captures the underlying reasoning that is not available elsewhere. We encourage the broader community to understand the design, intent, and future directions to foster discussion guiding phase two and the ultimate production storage stack based on this work. An initial performance evaluation of the early prototype implementation is also provided to validate the presented design.

Evaluating experimental results in the field of computer systems is a challenging task, mainly due to the many changes in software and hardware that computational environments go through. In this position paper, we analyze salient features of container technology that, if leveraged correctly, can help reduce the complexity of reproducing experiments in systems research. We present a use case in the area of distributed storage systems to illustrate the extensions that we envision, mainly in terms of container management infrastructure. We also discuss the benefits and limitations of using containers as a way of reproducing research in other areas of experimental systems research.

Given a replicated database, a divergent design tunes the indexes in each replica differently in order to specialize it for a specific subset of the workload. Empirical studies have shown that this specialization brings significant performance gains compared to the common practice of having the same indexes in all replicas. However, reaping the benefits of divergent designs requires the development of new tuning tools for database administrators, and the existing tools unfortunately suffer from severe shortcomings: they assume a fixed number of replicas and a known workload distribution, and ignore the possibility of replica failures and the subsequent effect on load imbalance.

Current production HPC IO stack design is unlikely to offer sufficient features and performance to adequately serve extreme scale science platform requirements as well as Big Data problems. A joint effort between the US Department of Energy's Office of Advanced Simulation and Computing and Advanced Scientific Computing Research commissioned a project to develop a design and prototype for an IO stack suitable for the extreme scale environment. It will be referred to as the Fast Forward Storage and IO (FFSIO) project. This is a joint effort led by Lawrence Livermore National Laboratory, with the DOE Data Management Nexus leads Rob Ross and Gary Grider as coordinators and contract lead Mark Gary.

Index tuning; i.e., selecting indexes that are appropriate for the workload to obtain good system performance, is a crucial task for database administrators. Administrators rely on automated index advisors for this task, but existing advisors work either offline, requiring a-priori knowledge of the workload, or online, taking the administrator out of the picture and assuming total control of the index tuning task. Semi-automatic index tuning is a new paradigm that achieves a middle ground: the advisor analyzes the workload online and provides recommendations tailored to the current workload, and the administrator is able to provide feedback to refine future recommendations. In this demonstration we present Kaizen, an index tuning tool that implements semi-automatic tuning.

Improving the performance and functionality of database system optimizers requires experimentation on real customer data. Often these data are of sensitive nature and the only way to keep them is by applying a non-reversible transformation to obfuscate them. However, in order that the database optimizer generates exactly the same query plans as for the sensitive data, the transformation has to preserve the order and some important properties of the data distribution. Unfortunately, existing data obfuscation techniques do not preserve all of these properties and therefore are not applicable in this context. In this paper we present a Desensitizer tool that we have developed for optimizer performance experiments of HP's Neoview high availability data warehousing product. The tool is based on novel numeric and string desensitization algorithms which are agnostic to the database system. We explain the core concepts behind the algorithms, how they preserve the required data properties and important implementation considerations that were made. We present the architecture of the Desensitizer tool and results of the extensive validation that we conducted.

QuickStart is a tool to automate the physical design of data warehouses for HP's Neoview system. It has been researched and prototyped at HP Labs with close interaction from Neoview design experts. It embodies heuristics and best practices of the experts to search for candidate physical features and uses cost calculations to recommend the features that result in good designs. It has some unique characteristics that differentiate it from other physical design advisors. In particular, it is the only advisor that is client-based, does not require a DBMS server installation and can work off a laptop by simply connecting to the customers' flat files. Another unique characteristic of QuickStart is that it provides the rationale for its recommendations.