Hyperscalers like Microsoft – which makes Azure available in 140 countries – also have other disaster recovery plans in place, from management of roles across fault domains to automated failover of user traffic to another region if the user’s region fails to enabling users to geo-replicate Azure Storage to secondary regions . For Facebook, with its 2.1 billion users and global datacenters in places ranging from Santa Clara, California and Ashburn, Virginia to Lulea, Sweden and Odense, Denmark, disaster recovery is not only crucial to its operations, but it’s something the giant social networking companies works on constantly. “The ability to seamlessly handle the loss of a portion of Facebook’s global compute, storage, and network footprint has been a long-standing goal of Facebook Infrastructure,” a group of Facebook engineers wrote in a recent paper about the company’s infrastructure. “Internally, our disaster recovery team regularly performs drills to identify and remedy the weakest links in our global infrastructure and software stacks. Disruptive actions include taking an entire datacenter offline with little to no notice in order to confirm that the loss of any of our global datacenters results in minimal disruption to the business.” Ensuring high availability – while always critical to operations – has become even more so as the role of artificial intelligence (AI) and machine learning has become even more prevalent within in the company’s operations. Facebook is leveraging machine learning in a broad array of services, from rankings in the News Feed and searches to displaying ads aimed at specific users and Facer for facial recognition, as well as language translation, speech recognition and internal operations like Sigma for anomaly detection. The company also uses multiple machine learning models, including deep neural networks, logistic regression and support vector machines. There are deep learning frameworks like Caffe2 and PyTorch and internal machine learning-as-a-service capabilities like FBLearner Feature Store, FBLearner Flow, and FBLearner Prediction. As we’ve noted in The Next Platform , much of Facebook’s distributed and scalable machine learning infrastructure is based on systems designed in-house, such as the Big Basin Disaster Recovery GPU server, and relies heavily on both CPUs from Intel and GPUs from Nvidia for training and inference. The growth of machine learning capabilities throughout the Facebook’s operations put an even greater premium on disaster recovery, according to the paper’s authors. “For both the training and inference portions of machine learning, the importance of disaster-readiness cannot be underestimated,” they wrote.

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