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Understanding lifecycle management complexity of datacenter topologies

~ Adrian Colyer ~ 1 Comment
Understanding lifecycle management complexity of datacenter topologies Zhang et al., NSDI'19 There has been plenty of interesting research on network topologies for datacenters, with Clos-like tree topologies and Expander based graph topologies both shown to scale using widely deployed hardware. This research tends to focus on performance properties such as throughput and latency, together with ... Continue Reading

Exploiting commutativity for practical fast replication

~ Adrian Colyer ~ 8 Comments
Exploiting commutativity for practical fast replication Park & Ousterhout, NSDI'19 I’m really impressed with this work. The authors give us a practical-to-implement enhancement to replication schemes (e.g., as used in primary-backup systems) that offers a signification performance boost. I’m expecting to see this picked up and rolled-out in real-world systems as word spreads. At a ... Continue Reading

Efficient synchronisation of state-based CRDTs

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Efficient synchronisation of state-based CRDTs Enes et al., arXiv’18 CRDTs are a great example of consistency as logical monotonicity. They come in two main variations: operation-based CRDTs send operations to remote replicas using a reliable dissemination layer with exactly-once causal delivery. (If operations are idempotent then at-least-once is ok too). state-based CRDTs exchange information about ... Continue Reading

Efficient large-scale fleet management via multi-agent deep reinforcement learning

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Efficient large-scale fleet management via multi-agent deep reinforcement learning Lin et al., KDD'18 A couple of weeks ago we looked at a survey paper covering approaches to dynamic, stochastic, vehicle routing problems (DSVRPs). At the end of the write-up I mentioned that I couldn’t help wondering about an end-to-end deep learning based approach to learning ... Continue Reading

GAN dissection: visualizing and understanding generative adversarial networks

~ Adrian Colyer ~ 2 Comments
GAN dissection: visualizing and understanding generative adversarial networks Bau et al., arXiv'18 Earlier this week we looked at visualisations to aid understanding and interpretation of RNNs, today’s paper choice gives us a fascinating look at what happens inside a GAN (generative adversarial network). In addition to the paper, the code is available on GitHub and ... Continue Reading