Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook, Cao et al., FAST'20 You get good at what you practice. Or in the case of key-value stores, what you benchmark. So if you want to design a system that will offer good real-world performance, it's really useful to have benchmarks that accurately represent real-world workloads. … Continue reading Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook
Tag: Facebook
Taiji: managing global user traffic for large-scale Internet services at the edge
Taiji: managing global user traffic for large-scale internet services at the edge Xu et al., SOSP'19 It’s another networking paper to close out the week (and our coverage of SOSP’19), but whereas Snap looked at traffic routing within the datacenter, Taiji is concerned with routing traffic from the edge to a datacenter. It’s been in … Continue reading Taiji: managing global user traffic for large-scale Internet services at the edge
HackPPL: a universal probabilistic programming language
HackPPL: a universal probabilistic programming language Ai et al., MAPL'19 The Hack programming language, as the authors proudly tell us, is "a dominant web development language across large technology firms with over 100 million lines of production code." Nail that niche! Does your market get any smaller if we also require those firms to have … Continue reading HackPPL: a universal probabilistic programming language
Applied machine learning at Facebook: a datacenter infrastructure perspective
Applied machine learning at Facebook: a datacenter infrastructure perspective Hazelwood et al., _HPCA’18 _ This is a wonderful glimpse into what it’s like when machine learning comes to pervade nearly every part of a business, with implications top-to-bottom through the whole stack. It’s amazing to step back and think just how fundamentally software systems have … Continue reading Applied machine learning at Facebook: a datacenter infrastructure perspective
Maelstrom: mitigating datacenter-level disasters by draining interdependent traffic safely and efficiently
Maelstrom: mitigating datacenter-level disasters by draining interdependent traffic safely and efficiently Veeraraghavan et al., OSDI'18 Here’s a really valuable paper detailing four plus years of experience dealing with datacenter outages at Facebook. Maelstrom is the system Facebook use in production to mitigate and recover from datacenter-level disasters. The high level idea is simple: drain traffic … Continue reading Maelstrom: mitigating datacenter-level disasters by draining interdependent traffic safely and efficiently
Rosetta: large scale system for text detection and recognition in images
Rosetta: large scale system for text detection and recognition in images Borisyuk et al., KDD'18 Rosetta is Facebook’s production system for extracting text (OCR) from uploaded images. In the last several years, the volume of photos being uploaded to social media platforms has grown exponentially to the order of hundreds of millions every day, presenting … Continue reading Rosetta: large scale system for text detection and recognition in images
Unveiling and quantifying Facebook exploitation of sensitive personal data for advertising purposes
Unveiling and quantifying Facebook exploitation of sensitive personal data for advertising purposes Cabañas et al., USENIX Security 2018 Earlier this week we saw how the determined can still bypass most browser and tracker-blocking extension protections to track users around the web. Today’s paper is a great example of why you should care about that. Cabañas … Continue reading Unveiling and quantifying Facebook exploitation of sensitive personal data for advertising purposes
