Monitoring containers on GKE with Google Stackdriver

Alternative Text by Ádám Sándor

After acquiring Stackdriver in 2014, Google worked hard to make it the default log aggregation and monitoring solution for Google Cloud Platform (GCP). The feature set of Stackdriver is pretty good for an out of box integrated solution for GCP. It is the equivalent of CloudWatch on AWS - doesn’t have all the bells and whistles but it’s relatively cheap, integrated and gets the job [...]

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Having a #Meltdown Over #Spectre?

Alternative Text by Anne Currie

Confused yet by Meltdown and Spectre? It’s hard not to be! So what should or can you do about it? Our View For Meltdown and Spectre it’s security business as usual. Patches exist for Meltdown and half of Spectre (for most machines) although more fixes will be forthcoming. Make sure you apply all these patches and keep your OSes and browsers up-to-date. In the cloud (AWS/Azure/Google [...]

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Cloud Based FPGA’s: We’re Not Quite There Yet

Alternative Text by Cyle Riggs

FPGA’s, Field Programmable Gate Arrays, are reprogrammable digital logic circuits able to shape shift into just about any digital circuit you can imagine: neural networks, image processors, CPU’s… the possibilities are endless. The idea is that adding an FPGA to an architecture allows one to expand on the generalized functionality offered in the modern CPU, adding custom hardware features [...]

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Why Use Distributed Systems? Resilience, Performance, and Availability

Alternative Text by Anne Currie

Earlier in this series we discussed what Distributed Systems are and why we use them, and we controversially defined a DistSys as any system divided over more than one physical location and using decoupling and copying to improve performance. Then we realised that’s most systems! However, when we explicitly talk about building DistSys we usually mean creating a system at big enough scale [...]

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Tensorflow on Kubernetes: Kubeflow

by Philip Winder

Google is quietly releasing increasing amounts of projects dedicated to data science. One such project that was recently pointed out to me is called Kubeflow. In its essence, it is not terribly complicated. But when considered as part of the adoption of data science (and Google’s strategy), the project is of utmost importance. Kubeflow is a mashup of Jupyter Hub and Tensorflow. It exposes [...]

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