Trade-offs under pressure: heuristics and observations of teams resolving internet service outages (Part II)

Trade-offs under pressure: heuristics and observations of teams resolving internet service outages, Allspaw, Masters thesis, Lund University 2015 This is part 2 of our look at Allspaw's 2015 master thesis (here's part 1). Today we'll be digging into the analysis of an incident that took place at Etsy on December 4th, 2014. 1:00pm Eastern Standard … Continue reading Trade-offs under pressure: heuristics and observations of teams resolving internet service outages (Part II)

Trade-offs under pressure: heuristics and observations of teams resolving internet service outages (Part 1)

Trade-offs under pressure: heuristics and observations of teams resolving internet service outages, Allspaw, Masters thesis, Lund University, 2015 Following on from the STELLA report, today we're going back to the first major work to study the human and organisational side of incident management in business-critical Internet services: John Allspaw's 2015 Masters thesis. The document runs … Continue reading Trade-offs under pressure: heuristics and observations of teams resolving internet service outages (Part 1)

STELLA: report from the SNAFU-catchers workshop on coping with complexity

STELLA: report from the SNAFU-catchers workshop on coping with complexity, Woods 2017, Coping with Complexity workshop "Coping with complexity" is about as good a three-word summary of the systems and software challenges facing us over the next decade as I can imagine. Today's choice is a report from a 2017 workshop convened with that title, … Continue reading STELLA: report from the SNAFU-catchers workshop on coping with complexity

Synthesizing data structure transformations from input-output examples

Synthesizing data structure transformations from input-output examples, Feser et al., PLDI'15 The Programmatically Interpretable Reinforcement Learning paper that we looked at last time out contained this passing comment coupled with a link to today's paper choice: It is known from prior work that such [functional] languages offer natural advantages in program synthesis. That certainly caught … Continue reading Synthesizing data structure transformations from input-output examples

Programmatically interpretable reinforcement learning

Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a controller learned through reinforcement learning (RL) is one of the key challenges for real-world deployments of RL that we looked at earlier this week. It's also an essential requirement for agents in human-machine collaborations (i.e, all deployments at some … Continue reading Programmatically interpretable reinforcement learning

Challenges of real-world reinforcement learning

Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML'19 Last week we looked at some of the challenges inherent in automation and in building systems where humans and software agents collaborate. When we start talking about agents, policies, and modelling the environment, my thoughts naturally turn to reinforcement learning (RL). Today's paper choice sets out … Continue reading Challenges of real-world reinforcement learning

Ten challenges for making automation a ‘team player’ in joint human-agent activity

Ten challenges for making automation a 'team player' in joint human-agent activity, Klein et al., IEEE Computer Nov/Dec 2004 With thanks to Thomas Depierre for the paper suggestion. Last time out we looked at some of the difficulties inherit in automating control systems. However much we automate, we're always ultimately dealing with some kind of … Continue reading Ten challenges for making automation a ‘team player’ in joint human-agent activity