News

How AI is Starting to Influence Wireless Communications

Machine learning and deep learning technologies are promising an end-to-end optimization of wireless networks while they commoditize PHY and signal-processing designs and help overcome RF complexities What happens when artificial intelligence (AI) technology arrives on wireless channels? For a start, AI promises to address the design complexity of radio frequency (RF) systems by employing powerful machine learning algorithms and significantly improving RF parameters such as channel bandwidth, antenna sensitivity and spectrum monitoring. So far, engineering efforts have been made for smartening individual components in wireless networks via technologies like cognitive radio.

Predictive CPU isolation of containers at Netflix

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. However, the key insight here is that these caches are partially shared among the CPUs, which means that perfect performance isolation of co-hosted containers is not possible. If the container running on the core next to your container suddenly decides to fetch a lot of data from the RAM, it will inevitably result in more cache misses for you (and hence a potential performance degradation).

Accelerating Uber’s Self-Driving Vehicle Development with Data

A key challenge faced by self-driving vehicles comes during interactions with pedestrians. In our development of self-driving vehicles, the Data Engineering and Data Science teams at Uber ATG (Advanced Technologies Group) contribute to the data processing and analysis that help make these interactions safe.

Scaling Erlang Cluster to 10,000 Nodes

Maxim Fedorov demonstrates an example of a live Erlang cluster being scaled from just a few nodes to 10,000 machines with no service interruption.

Open Banking

Anca Zaharia and Jason Maude focus on the successes and pitfalls Starling Bank encountered in building Open Banking. They cover topics such as the OAuth security flow, the permissions-controlled API, obtaining permissions for third party actions, and verifying payees between banks.

Minimize the blast radius of changes with Solo.io Gloo Gateway and Weaveworks Flagger

Chaos Engineering Kubernetes with the Litmus Framework

Litmus is an open source chaos engineering framework for Kubernetes environments running stateful applications. Created by MayaData, Litmus enables users to run test suites, capture logs, generate rep The book Mastering Collaboration by Gretchen Anderson provides techniques and exercises that can be used to improve collaboration in teams and between teams and their environment.

Argo CD v1.0 Released

We’ve just released Argo CD v1.0, which contains several big new features, nearly 30 enhancements, and bug fixes. This release also introduces API stability and a Custom Resource Definition for the application spec. Special thanks to the following contributors (amongst others) for their work on v1.0: Alexander Matyushentsev, Alex Collins, Simon Behar, Paul Brit, Danny Thomson, Jesse Suen, tom-256, Omer Kahani, Sahil Ahuja, and jpresky.

Scio 0.7: a deep dive

Large-scale data processing is a critical component of Spotify’s business model. It drives music recommendations, artist payouts based on stream counts, and insights about how users interact with Spotify. Every day we capture hundreds of terabytes of event data, in addition to database snapshots and derived datasets.

KubeCon EU 2019: Top 10 Takeaways

The Datawire team and I have returned home from an awesome time last week where we attended KubeCon and CloudNativeCon in Barcelona. Together, we were part of six talks at KubeCon, staffed a packed booth with amazing T-shirts (if I do say so myself!), spoke to dozens of community members, and attended some fantastic talks. As there was so much goodness on offer at KubeCon EU, I’ve tried to summarise some of my key observations in this blog post.