Queue is published 6 times per year, with issues available via an app on iPhone and iPad and Android devices or via a browser-based 'digital edition' (DE).

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November/December 2017 Table of Contents

January/February 2018 acmqueue


Canary Analysis Service
- Štěpàn Davidovič
Google's centralized CAS takes the canary-in- the-coalmine approach to determining whether a partial and time-limited change in service is safe and effective. In the end, CAS quickens development, improves production safety, and helps prevent outages.

Thou Shalt Not Depend on Me
- Tobias Lauinger, Abdelberi Chaabane, and Christo B. Wilson
Websites that use third-party components such as JavaScript libraries are vulnerable to any security issues that these libraries may contain. The first step is understanding the scope of the problem, then determining what can be done to improve the situation.

Designing cluster schedulers for Internet-scale services
- Diptanu Gon Choudhury and Timothy Perrett
Cluster schedulers for distributed systems have become ubiquitous but implementing them is tricky. They must be designed for failure in order to assure system stability and reliability.

Columns / Departments

Manual Work is a Bug

- Thomas A. Limoncelli
A tale of two sysadmins—one who took time to write a lot of code to automate tasks; one who did not. One was successful; one not so much.

Watchdogs vs. Snowflakes

- George Neville-Neil
How to deal with a distributed system that randomly jams up? There may be no easy answers—only wild-ass guesses.

How to Come Up with Great Ideas

- Kate Matsudaira
Train yourself to have the mindset of an entrepreneur, and you will have no shortage of ideas. Here are a few techniques to start thinking in those terms.

Prediction-serving Systems

- Dan Crankshaw and Joseph Gonzalez
Machine learning involves training a model that can then be used to make predictions based on new inputs. Most research in this area has focused on designing and training these models. Here we look at papers that address the often-overlooked area of managing these models once they are trained.

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