SAMC: Semantic-aware model checking for fast discovery of deep bugs in cloud systems

SAMC: Semantic-aware model checking for fast discovery of deep bugs in cloud systems - Leesatapornwongsa et al. 2014 This is the second of three papers we'll be looking at this week on the theme of verifying correctness of, and catching bugs in, distributed systems. Yesterday we saw the Statecall Policy Language and associated tool chain … Continue reading SAMC: Semantic-aware model checking for fast discovery of deep bugs in cloud systems

Liquid: Unifying nearline and offline big data integration

Liquid: Unifying Nearline and Offline Big Data Integration - Fernandez et al. 2015 This is post 3 of 5 in a series looking at the latest research from the CIDR '15 conference. Also in the series so far this week: 'The missing piece in complex analytics' and 'WANalytics: analytics for a geo-distributed, data intensive world'. … Continue reading Liquid: Unifying nearline and offline big data integration

WANalytics: Analytics for a geo-distributed, data intensive world

WANalytics: analytics for a geo-distributed data intensive world - Vulimiri et al. 2015 ...data is born distributed; we only control data replication and distributed execution strategies. This is true for so many sources of data. Combine this with Dave McCrory's observation that 'Data has Gravity' (i.e. it attracts applications and other data processing workloads to … Continue reading WANalytics: Analytics for a geo-distributed, data intensive world

Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing

Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing - Google 2014 Mesa is another in the tapestry of systems that support Google's advertising business. Previously editions of The Morning Paper have covered Photon, Spanner, F1, and F1's online schema update mechanism. Mesa is a highly scalable analytic data warehousing system that stores critical measurement data related … Continue reading Mesa: Geo-Replicated, Near Real-Time, Scalable Data Warehousing