BOAT: Building auto-tuners with structured Bayesian optimization

BOAT: Building auto-tuners with structured Bayesian optimization Dalibard et al., WWW'17 Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance... From the number of machines used in a distributed application, to low-level parameters such as compiler flags, managing configurations has become one of the main challenges faced ... Continue Reading

CherryPick: Adaptively unearthing the best cloud configurations for big data analytics

CherryPick: Adaptively unearthing the best cloud configurations for big data analytics Alipourfard et al., NSDI'17 For big data analytics jobs, especially recurring jobs, finding a good cloud configuration (number and type of machines, CPU, memory ,disk and network options) can make a big different to overall cost and runtimes. Likewise, a poor choice can seriously ... Continue Reading

Enlightening the I/O path: A holistic approach for application performance

Enlightening the I/O Path: A holistic approach for application performance Kim et al., FAST '17 Lots of applications contain a mix of foreground and background tasks. Since we're at the file system level here, for application, think Redis, MongoDB, PostgreSQL and so on. Typically user requests are considered foreground tasks, and tasks such as housekeeping, ... Continue Reading

REX: A development platform and online learning approach for runtime emergent software systems

REX: A development platform and online learning approach for runtime emergent software systems Porter et al. OSDI 2016 If you can get beyond the (for my taste, ymmv) somewhat grand claims and odd turns of phrase (e.g., “how the software ‘feels’ at a given point in time” => metrics) then there’s something quite interesting at ... Continue Reading