Using machine learning and analytics to help developers

It was the talk title that caught my eye – “Developer Insights: ML and Analytics on src/”. I was intrigued. I had a few ideas of how machine learning techniques could be used on source code, but I was curious to see what the state of the art looked like now. I attended the session at DevConf.cz 2020 by Christoph Görn and Francesco Murdaca of the AI and ML Center of Excellence in Red Hat to hear more.

The first question I had was “where did they come up with the project name Thoth?” My initial guess was that “Thoth” was an ice moon from the Star Wars universe, or maybe a demon from Buffy the Vampire Slayer. It turns out that Thoth is the Ancient Egyptian god of writing, magic, wisdom, and the moon. The Egyptian deity theme runs through the project, with components called Thamos, Kebechet, Amun, and Nepthys, among others.

The set of problems that Thoth aims to solve is an important one. Can we help developers identify the best library to use, by looking at what everyone else is using for a similar job? Can we help identify the source of common performance issues, and suggest speed-ups? Can we create a framework that can enforce compliance, and help minimize risk, as applications grow?

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Size matters: how Fedora approaches minimization

As part of a modern IT environment, Linux distributions can look to optimizing their size to be better suited for container use. One of the ways this improvement can happen is through reducing the size of a distribution, a process known as minimization. A new tool is being put together that will enable developers and operators to create minimal images of the appropriate size for the container use cases they need.

Graphic represents the relationships between all of the software repositories in Fedora Linux, many thousands of green dots cross-connected to appear like a cloud nebula.
Graphical representation of Fedora repository relationships. Image by: Adam Šamalík

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Managing application and data portability at scale with Rook-Ceph

One of the key requirements for Kubernetes in multi-cluster environments is the ability to migrate an application with all of its dependencies and resources from one cluster to another cluster. Application portability gives application owners and administrators the ability to better manage applications for common needs such as scaling out applications, high availability for applications, or just simply backing up applications for disaster recovery. This post is going to present one solution for enabling storage and data mobility in multicluster/hybrid cloud environments using Ceph and Rook.

Containerization and Container Native Storage has made it easier for developers to run applications and get the storage they need, but as this space evolves and matures it is becoming increasingly important to move your application and data around, from cluster to cluster and cloud to cloud.

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Kiali: An observability platform for Istio

Istio exists to make life easier for application developers working with Kubernetes. But what about making Istio easier? Well, that’s Kiali’s job. Read on to learn more about making Istio even more pleasant to use.
Deploying and managing microservice applications is hard. When you break down an application into components, you add complexity in how those components communicate with each other. Getting an alert when something goes wrong, and figuring out how to fix it, is a challenge involving networking, storage, and potentially dozens of different compute nodes.

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Current Trusted Execution Environment landscape

If you run software on someone’s servers, you have a problem. You can’t be sure your data and code aren’t being observed, or worse, tampered with — trust is your only assurance. But there is hope, in the form of Trusted Execution Environments (TEEs) and a new open source project, Enarx, that will make use of TEEs to minimize the trust you need to confidently run on other people’s hardware. This article delves into this problem, how TEE’s work and their limitations, providing a TEE primer of sorts, and explaining how Enarx aims to work around these limitations. It is the next in a series that started with Trust No One, Run Everywhere–Introducing Enarx.

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Scaling workload storage requirements across clusters

A number of multi-cloud orchestrators have promised to simplify deploying hundreds or thousands of high-availability services.  But this comes with massive infrastructure requirements. How could we possibly manage the storage needs of a thousand stateful processes?  In this blog, we’ll examine how we can leverage these orchestrators to address our dynamic storage requirements.

Currently in Kubernetes, there are two approaches in how a control plane can scale resources across multiple clusters.  These are commonly referred to as the Push and Pull models, referring to the way in which configurations are ingested by a managed cluster.  Despite being antonyms in name, these models are not mutually exclusive and may be deployed together to target separate problem spaces in a managed multi-cluster environment.

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Prometheus anomaly detection

With an increase in the number of applications being deployed on Red Hat OpenShift, there is a strong need for application monitoring. A number of these applications are monitored via Prometheus metrics, resulting in an accumulation of a large number of time-series metrics stored in a TSDB (time series database). Some of these metrics can have anomalous values, which may indicate issues in the application, but it is difficult to identify them manually. To address this issue, we came up with an AI-based approach of training a machine-learning model on these metrics for detecting anomalies.

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Sentiment analysis with machine learning

When developing a new technology, it really helps if you are also a user of that new tech. This has been an approach of Red Hat around artificial intelligence and machine learning — develop openly on one hand, exchanging knowledge across the organization to use the same tools in the other hand to work on interesting business problems. All while keeping a two-way exchange to and from the open source commons.

This is the sort of left-hand/right-hand move that data scientist Oindrilla Chatterjee began using as part of a project she originally started during an internship, then later in a full-time role at Red Hat. Chatterjee and her team are looking at how to do sentiment analysis using machine learning on a dataset consisting of customer and partner surveys regarding a service offering.

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Red Hat and NVIDIA bring scalable, efficient edge computing to smart cities

Teams from Red Hat and NVIDIA have collaborated on creating a scalable hybrid cloud application that could revolutionize smart city initiatives such as traffic-flow monitoring and transportation management around the world. By working together, the two companies are creating solutions that make cities smarter and more efficient by taking sensor data and processing it in real-time to provide insights for traffic congestion, pedestrian flow, and infrastructure maintenance.

Running on top of the NVIDIA EGX platform with the NVIDIA GPU Operator, the application is built with NVIDIA’s Metropolis application framework for IoT that brings together innovative capabilities for real-time image processing where NVIDIA DeepStream SDK is used to extract metadata from live video streams at the edge. It then forwards the right metadata to the cloud for deeper analytical processing and further representation in an information dashboard depicted below.

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Passing Go: polyglot Kubernetes Operators

Operators within Kubernetes are useful tools, designed to extend the container orchestration platform with additional resources. More directly, an Operator, sometimes referred to as custom controllers, is a method of packaging, deploying, and managing a Kubernetes application. 

As useful as Operators are, they have had one limitation: originally they all had to be written in the Go programming language. Thanks to the Operator SDK, you do not need to develop your Operators in Go. The Operator SDK has options for Ansible and Helm that may be better suited for the way you or your team work. But, it can still be limiting for dev teams trying to build an operator if they don’t happen to be skilled in Helm or Ansible.

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