The Science of Managing Data Science
Lessons learned managing a data science research team
"What are they doing all day?" When I first took over as VP of Engineering at a startup doing data mining and machine learning research, this was what the other executives wanted to know. They knew the team was super smart, and they seemed like they were working really hard, but the executives had lots of questions about the work itself. How did they know that the work they were doing was the "right" work? Were there other projects they could be doing instead? And how could we get this research into the hands of our customers faster?
Using Free and Open Source Tools to Manage Software Quality
Phelim Dowling and Kevin McGrath
An agile process implementation
The principles of agile software development place more emphasis on individuals and interactions than on processes and tools. They steer us away from heavy documentation requirements and guide us along a path of reacting efficiently to change rather than sticking rigidly to a pre-defined plan. To support this flexible method of operation, it is important to have suitable applications to manage the team's activities. It is also essential to implement effective frameworks to ensure quality is being built into the product early and at all levels. With these concerns in mind and coming from a budget-conscious perspective, this article will explore the free and open source applications and tools used by one organization in its quest to build process and quality around its projects and products.
From the EDVAC to WEBVACs
Daniel C. Wang
Cloud computing for computer scientists
By now everyone has heard of cloud computing and realized that it is changing how both traditional enterprise IT and emerging startups are building solutions for the future. Is this trend toward the cloud just a shift in the complicated economics of the hardware and software industry, or is it a fundamentally different way of thinking about computing? Having worked in the industry, I can confidently say it is both.
Spicing Up Dart with Side Effects
Erik Meijer, Applied Duality; Kevin Millikin, Google; Gilad Bracha, Google
A set of extensions to the Dart programming language, designed to support asynchrony and generator functions
The Dart programming language has recently incorporated a set of extensions designed to support asynchrony and generator functions. Because Dart is a language for Web programming, latency is an important concern. To avoid blocking, developers must make methods asynchronous when computing their results requires nontrivial time. Generator functions ease the task of computing iterable sequences.
Reliable Cron across the Planet
Štěpán Davidovič, Kavita Guliani, Google
...or How I stopped worrying and learned to love time
This article describes Google's implementation of a distributed Cron service, serving the vast majority of internal teams that need periodic scheduling of compute jobs. During its existence, we have learned many lessons on how to design and implement what might seem like a basic service. Here, we discuss the problems that distributed Crons face and outline some potential solutions.
There is No Now
Problems with simultaneity in distributed systems
"Now." The time elapsed between when I wrote that word and when you read it was at least a couple of weeks. That kind of delay is one that we take for granted and don't even think about in written media. "Now." If we were in the same room and instead I spoke aloud, you might have a greater sense of immediacy. You might intuitively feel as if you were hearing the word at exactly the same time that I spoke it. That intuition would be wrong. If, instead of trusting your intuition, you thought about the physics of sound, you would know that time must have elapsed between my speaking and your hearing. The motion of the air, carrying my word, would take time to get from my mouth to your ear.
Parallel Processing with Promises
A simple method of writing a collaborative system
In today's world, there are many reasons to write concurrent software. The desire to improve performance and increase throughput has led to many different asynchronous techniques. The techniques involved, however, are generally complex and the source of many subtle bugs, especially if they require shared mutable state. If shared state is not required, then these problems can be solved with a better abstraction called promises. These allow programmers to hook asynchronous function calls together, waiting for each to return success or failure before running the next appropriate function in the chain.
Relevance and repeatability
Dear KV, The company I work for has decided to use a wireless network link to reduce latency, at least when the weather between the stations is good. It seems to me that for transmission over lossy wireless links we'll want our own transport protocol that sits directly on top of whatever the radio provides, instead of wasting bits on IP and TCP or UDP headers, which, for a point-to-point network, aren't really useful.
Go Static or Go Home
In the end, dynamic systems are simply less secure.
Most current and historic problems in computer and network security boil down to a single observation: letting other people control our devices is bad for us. At another time, I'll explain what I mean by "other people" and "bad." For the purpose of this article, I'll focus entirely on what I mean by control. One way we lose control of our devices is to external distributed denial of service (DDoS) attacks, which fill a network with unwanted traffic, leaving no room for real ("wanted") traffic. Other forms of DDoS are similar—an attack by the Low Orbit Ion Cannon (LOIC), for example, might not totally fill up a network, but it can keep a web server so busy answering useless attack requests that the server can't answer any useful customer requests. Either way, DDoS means outsiders are controlling our devices, and that's bad for us.
HTTP/2.0 - The IETF is Phoning It In
Bad protocol, bad politics
In the long run, the most memorable event of 1989 will probably be that Tim Berners-Lee hacked up the HTTP protocol and named the result the "World Wide Web." Tim's HTTP protocol ran on 10Mbit/s, Ethernet, and coax cables, and his computer was a NeXT Cube with a 25-MHz clock frequency. Twenty-six years later, my laptop CPU is a hundred times faster and has a thousand times as much RAM as Tim's machine had, but the HTTP protocol is still the same. A few days ago the IESG, The Internet Engineering Steering Group, asked for "Last Call" comments on new "HTTP/2.0" protocol before blessing it as a "Proposed Standard".
META II: Digital Vellum in the Digital Scriptorium
Revisiting Schorre's 1962 compiler-compiler
Some people do living history—reviving older skills and material culture by reenacting Waterloo or knapping flint knives. One pleasant rainy weekend in 2012, I set my sights a little more recently and settled in for a little meditative retro-computing, ca. 1962, following the ancient mode of transmission of knowledge: lecture and recitation—or rather, grace of living in historical times, lecture (here, in the French sense, reading) and transcription (or even more specifically, grace of living post-Post, lecture and reimplementation). Fortunately, for my purposes, Dewey Val Schorre's paper on META II was, unlike many more recent digital artifacts, readily available as a digital scan.
Model-based Testing: Where Does It Stand?
Robert V. Binder, Bruno Legeard, and Anne Kramer
MBT has positive effects on efficiency and effectiveness, even if it only partially fulfills high expectations.
From mid-June 2014 to early August 2014, we conducted a survey to learn how MBT users view its efficiency and effectiveness. The 2014 MBT User Survey, a follow-up to a similar 2012 survey (http://robertvbinder.com/real-users-of-model-based-testing/), was open to all those who have evaluated or used any MBT approach. Its 32 questions included some from a survey distributed at the 2013 User Conference on Advanced Automated Testing. Some questions focused on the efficiency and effectiveness of MBT, providing the figures that managers are most interested in. Other questions were more technical and sought to validate a common MBT classification scheme. A common classification scheme could help users understand both the general diversity and specific approaches.
Securing the Network Time Protocol
Crackers discover how to use NTP as a weapon for abuse.
In the late 1970s David L. Mills began working on the problem of synchronizing time on networked computers, and NTP (Network Time Protocol) version 1 made its debut in 1980. This was at a time when the net was a much friendlier place—the ARPANET days. NTP version 2 appeared approximately a year later, about the same time as CSNET (Computer Science Network). NSFNET (National Science Foundation Network) launched in 1986. NTP version 3 showed up in 1993.
Scalability Techniques for Practical Synchronization Primitives
Designing locking primitives with performance in mind
In an ideal world, applications are expected to scale automatically when executed on increasingly larger systems. In practice, however, not only does this scaling not occur, but it is common to see performance actually worsen on those larger systems.
Internal Access Controls
Trust But Verify.
Every day seems to bring news of another dramatic and high-profile security incident, whether it is the discovery of longstanding vulnerabilities in widely used software such as OpenSSL or Bash, or celebrity photographs stolen and publicized. There seems to be an infinite supply of zero-day vulnerabilities and powerful state-sponsored attackers. In the face of such threats, is it even worth trying to protect your systems and data? What can systems security designers and administrators do?
Use the database built for your access model.
The topic of data storage is one that doesn't need to be well understood until something goes wrong (data disappears) or something goes really right (too many customers). Because databases can be treated as black boxes with an API, their inner workings are often overlooked. They're often treated as magic things that just take data when offered and supply it when asked. Since these two operations are the only understood activities of the technology, they are often the only features presented when comparing different technologies.
Kode Vicious: Too Big to Fail
Visibility leads to debuggability.
Our project has been rolling out a well-known, distributed key/value store onto our infrastructure, and we've been surprised—more than once—when a simple increase in the number of clients has not only slowed things, but brought them to a complete halt. This then results in rollback while several of us scour the online forums to figure out if anyone else has seen the same problem. The entire reason for using this project's software is to increase the scale of a large system, so I have been surprised at how many times a small increase in load has led to a complete failure. Is there something about scaling systems that's so difficult that these systems become fragile, even at a modest scale?