Batfish is a tool to analyze network configurations and forwarding. It has evolved from a research prototype to an industrial-strength product, guided by scalability, fidelity, and usability challenges encountered when analyzing complex, real-world networks. We share key lessons from this evolution, including how Datalog had significant limitations when generating and analyzing forwarding state and how binary decision diagrams (BDDs) proved highly versatile. We also describe our new techniques for addressing real-world challenges, which increase Batfish performance by three orders of magnitude and enable high-fidelity analysis of networks with thousands of nodes within minutes.

Community detection is an increasingly popular approach to uncover important structures in large networks. Flow-based community detection methods rely on communication patterns of the network rather than structural properties to determine communities. The Infomap algorithm in particular optimizes a novel objective function called the map equation and has been shown to outperform other approaches in third-party benchmarks. However, Infomap and its variants are inherently sequential, limiting their use for large-scale graphs. In this article, we propose a novel algorithm to optimize the map equation called RelaxMap. RelaxMap provides two important improvements over Infomap: parallelization, so that the map equation can be optimized over much larger graphs, and prioritization, so that the most important work occurs first, iterations take less time, and the algorithm converges faster. We implement these techniques using OpenMP on shared-memory multicore systems, and evaluate our approach on a variety of graphs from standard graph clustering benchmarks as well as real graph datasets. Our evaluation shows that both techniques are effective: RelaxMap achieves 70% parallel efficiency on eight cores, and prioritization improves algorithm performance by an additional 20--50% on average, depending on the graph properties. Additionally, RelaxMap converges in the similar number of iterations and provides solutions of equivalent quality as the serial Infomap implementation.

We analyze the workload from a multi-year deployment of a database-as-a-service platform targeting scientists and data scientists with minimal database experience. Our hypothesis was that relatively minor changes to the way databases are delivered can increase their use in ad hoc analysis environments. The web-based SQLShare system emphasizes easy dataset-at-a-time ingest, relaxed schemas and schema inference, easy view creation and sharing, and full SQL support. We find that these features have helped attract workloads typically associated with scripts and files rather than relational databases: complex analytics, routine processing pipelines, data publishing, and collaborative analysis. Quantitatively, these workloads are characterized by shorter dataset "lifetimes", higher query complexity, and higher data complexity. We report on usage scenarios that suggest SQL is being used in place of scripts for one-off data analysis and ad hoc data sharing. The workload suggests that a new class of relational systems emphasizing short-term, ad hoc analytics over engineered schemas may improve uptake of database technology in data science contexts. Our contributions include a system design for delivering databases into these contexts, a description of a public research query workload dataset released to advance research in analytic data systems, and an initial analysis of the workload that provides evidence of new use cases under-supported in existing systems.

MOTIVATION: Recent technological innovations in flow cytometry now allow oceanographers to collect high-frequency flow cytometry data from particles in aquatic environments on a scale far surpassing conventional flow cytometers. The SeaFlow cytometer continuously profiles microbial phytoplankton populations across thousands of kilometers of the surface ocean. The data streams produced by instruments such as SeaFlow challenge the traditional sample-by-sample approach in cytometric analysis and highlight the need for scalable clustering algorithms to extract population information from these large-scale, high-frequency flow cytometers. RESULTS: We explore how available algorithms commonly used for medical applications perform at classification of such a large-scale, environmental flow cytometry data. We apply large-scale Gaussian mixture models to massive datasets using Hadoop. This approach outperforms current state-of-the-art cytometry classification algorithms in accuracy and can be coupled with manual or automatic partitioning of data into homogeneous sections for further classification gains. We propose the Gaussian mixture model with partitioning approach for classification of large-scale, high-frequency flow cytometry data. AVAILABILITY AND IMPLEMENTATION: Source code available for download at https://github.com/jhyrkas/seaflow_cluster, implemented in Java for use with Hadoop. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Flow cytometers measure the optical properties of particles to classify microbes. Recent innovations have allowed oceanographers to collect flow cytometry data continuously during research cruises, leading to an explosion of data and new challenges for the classification task. The massive scale, time-varying underlying populations, and noisy measurements motivate the development of new classification methods. We describe the problem, the data, and some preliminary results demonstrating the difficulty with conventional methods.

Abstract Distributed database performance is often unpredictable due to issues such as system complexity, network congestion, or imbalanced data distribution. These issues are difficult for users to assess in part due to the opaque mapping between declaratively specified queries and actual physical execution plans. Database developers currently must expend significant time and effort scanning log files to isolate and debug the root causes of performance issues. In response, we present Perfopticon, an interactive query profiling tool that enables rapid insight into common problems such as performance bottlenecks and data skew. Perfopticon combines interactive visualizations of (1) query plans, (2) overall query execution, (3) data flow among servers, and (4) execution traces. These views coordinate multiple levels of abstraction to enable detection, isolation, and understanding of performance issues. We evaluate our design choices through engagements with system developers, scientists, and students. We demonstrate that Perfopticon enables performance debugging for real‐world tasks.

We present a new approach for data analytics with iterations. Users express their analysis in Datalog with bag-monotonic aggregate operators, which enables the expression of computations from a broad variety of application domains. Queries are translated into query plans that can execute in shared-nothing engines, are incremental, and support a variety of iterative models (synchronous, asynchronous, different processing priorities) and failure-handling techniques. The plans require only small extensions to an existing shared-nothing engine, making the approach easily implementable. We implement the approach in the Myria big-data management system and use our implementation to empirically study the performance characteristics of different combinations of iterative models, failure handling methods, and applications. Our evaluation uses workloads from a variety of application domains. We find that no single method outperforms others but rather that application properties must drive the selection of the iterative query execution model.

In this demonstration, we will showcase Myria, our novel cloud service for big data management and analytics designed to improve productivity. Myria's goal is for users to simply upload their data and for the system to help them be self-sufficient data science experts on their data -- self-serve analytics. Using a web browser, Myria users can upload data, author efficient queries to process and explore the data, and debug correctness and performance issues. Myria queries are executed on a scalable, parallel cluster that uses both state-of-the-art and novel methods for distributed query processing. Our interactive demonstration will guide visitors through an exploration of several key Myria features by interfacing with the live system to analyze big datasets over the web.

We consider a case study using SQL-as-a-Service to support "instant analysis" of weakly structured relational data at a multi-investigator science retreat. Here, "weakly structured" means tabular, rows-and-columns datasets that share some common context, but that have limited a priori agreement on file formats, relationships, types, schemas, metadata, or semantics. In this case study, the data were acquired from hundreds of distinct locations during a multi-day oceanographic cruise using a variety of physical, biological, and chemical sensors and assays. Months after the cruise when preliminary data processing was complete, 40+ researchers from a variety of disciplines participated in a two-day "data synthesis workshop." At this workshop, two computer scientists used a web-based query-as-a-service platform called SQLShare to perform "SQL stenography": capturing the scientific discussion in real time to integrate data, test hypotheses, and populate visualizations to then inform and enhance further discussion. In this "field test" of our technology and approach, we found that it was not only feasible to support interactive science Q&A with essentially pure SQL, but that we significantly increased the value of the "face time" at the meeting: researchers from different fields were able to validate assumptions and resolve ambiguity about each others' fields. As a result, new science emerged from a meeting that was originally just a planning meeting. In this paper, we describe the details of this experiment, discuss our major findings, and lay out a new research agenda for collaborative science database services.

The 60 GHz wireless technology that is now emerging has the potential to provide dense and extremely fast connectivity at low cost. In this paper, we explore its use to relieve hotspots in oversubscribed data center (DC) networks. By experimenting with prototype equipment, we show that the DC environment is well suited to a deployment of 60GHz links contrary to concerns about interference and link reliability. Using directional antennas, many wireless links can run concurrently at multi-Gbps rates on top-of-rack (ToR) switches. The wired DC network can be used to sidestep several common wireless problems. By analyzing production traces of DC traffic for four real applications, we show that adding a small amount of network capacity in the form of wireless flyways to the wired DC network can improve performance. However, to be of significant value, we find that one hop indirect routing is needed. Informed by our 60GHz experiments and DC traffic analysis, we present a design that uses DC traffic levels to select and adds flyways to the wired DC network. Trace-driven evaluations show that network-limited DC applications with predictable traffic workloads running on a 1:2 oversubscribed network can be sped up by 45% in 95% of the cases, with just one wireless device per ToR switch. With two devices, in 40% of the cases, the performance is identical to that of a non-oversubscribed network.

Simulations show that the Doppler component of the IEEE 802.11n channel model results in a dramatic decrease in transmit beamforming gain within only 20 ms delay, even though the model is intended for indoor WLAN environment with stationary devices. However, new measurements collected in an office environment show that degradation to transmit beamforming gain is much less sensitive to delay. With normal environmental conditions in the office environment, it was found that on average there was only a 22% decrease in transmit beamforming gain after 200 ms delay. Even with highly exaggerated motion, reasonable gain is maintained with over 100 ms of delay. Measurements with a moving device were also conducted with resulting sensitivity to delay similar to the 802.11n model. The measurements indicate that the Doppler component of the 802.11n channel model is more comparable to a moving device rather than a stationary device. The use of transmit beamforming in an indoor WLAN environment is more practical than simulations based on the IEEE 802.11n channel models would imply.

RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (<2 dB for most links) similar to the near-ideal case of narrowband, frequency-flat channels. Unlike RSSI, this lets us predict the highest rate that will work for a link, trim transmit power, and more. We use trace-driven simulation to show that our rate prediction is as good as the best rate adaptation algorithms for 802.11a/g, even over dynamic channels, and extends this good performance to 802.11n.

A fundamental problem with unmanaged wireless networks is high packet loss rates and poor spatial reuse, especially with bursty traffic typical of normal use. To address these limitations, we explore the notion of interference cancellation for unmanaged networks - the ability for a single receiver to disambiguate and successfully receive simultaneous overlapping transmissions from multiple unsynchronized sources. We describe a practical algorithm for interference cancellation, and implement it for ZigBee using software radios. In this setting, we find that our techniques can reduce packet loss rate and substantially increase spatial reuse. With carrier sense set to prevent concurrent sends, our approach reduces the packet loss rate during collisions from 14% to 8% due to improved handling of hidden terminals. Conversely, disabling carrier sense reduces performance for only 7% of all pairs of links and increases the delivery rate for the median pair of links in our testbed by a factor of 1.8 due to improved spatial reuse.

Our study analyzes the security and privacy properties of an implantable cardioverter defibrillator (ICD). Introduced to the U.S. market in 2003, this model of ICD includes pacemaker technology and is designed to communicate wirelessly with a nearby external programmer in the 175 kHz frequency range. After partially reverse-engineering the ICD's communications protocol with an oscilloscope and a software radio, we implemented several software radio-based attacks that could compromise patient safety and patient privacy. Motivated by our desire to improve patient safety, and mindful of conventional trade-offs between security and power consumption for resource-constrained devices, we introduce three new zero-power defenses based on RF power harvesting. Two of these defenses are human-centric, bringing patients into the loop with respect to the security and privacy of their implantable medical devices (IMDs). Our contributions provide a scientific baseline for understanding the potential security and privacy risks of current and future IMDs, and introduce human-perceptible and zero-power mitigation techniques that address those risks. To the best of our knowledge, this paper is the first in our community to use general-purpose software radios to analyze and attack previously unknown radio communications protocols.