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Balancing on the Edge: Transport Affinity without Network State | USENIX

João Taveira Araújo, Lorenzo Saino, Lennert Buytenhek, and Raul Landa, Fastly Content delivery networks and edge peering facilities have unique operating constraints which require novel approaches to load balancing. Contrary to traditional, centralized datacenter networks, physical space is heavily constrained. This limitation drives both the need for greater efficiency, maximizing the ability to

y_uuki 2018/04/13

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NetChain: Scale-Free Sub-RTT Coordination | USENIX

Xin Jin, Johns Hopkins University; Xiaozhou Li, Barefoot Networks; Haoyu Zhang, Princeton University; Nate Foster, Cornell University; Jeongkeun Lee, Barefoot Networks; Robert Soulé, Università della Svizzera italiana; Changhoon Kim, Barefoot Networks; Ion Stoica, UC Berkeley Awarded Best Paper! Coordination services are a fundamental building block of modern cloud systems, providing critical func

y_uuki 2018/04/11

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NSDI '18 Technical Sessions

All sessions will be held in the Grand Ballroom unless otherwise noted. The full Proceedings published by USENIX for the conference are available for download below. Individual papers can also be downloaded from the presentation page. Copyright to the individual works is retained by the author[s]. Proceedings Front Matter Proceedings Cover | Title Page and List of Organizers | Message from the Pro

y_uuki 2018/02/01

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TensorFlow: A system for large-scale machine learning

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs,