Servers: IBM/Red Hat, CentOS, CNCF and SUSE
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Microservices, and the Observability Macroheadache
Moving to a microservice architecture, deployed on a cloud platform such as OpenShift, can have significant benefits. However, it does make understanding how your business requests are being executed, across the potentially large numbers of microservices, more challenging.
If we wish to locate where problems may have occurred in the execution of a business request, whether due to performance issues or errors, we are potentially faced with accessing metrics and logs associated with many services that may have been involved. Metrics can provide a general indication of where problems have occurred, but not specific to individual requests. Logs may provide errors or warnings, but cannot necessarily be correlated to the individual requests of interest.
Distributed tracing is a technique that has become indispensable in helping users understand how their business transactions execute across a set of collaborating services. A trace instance documents the flow of a business transaction, including interactions between services, internal work units, relevant metadata, latency details and contextualized logging. This information can be used to perform root cause analysis to locate the problem quickly.
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5 AI fears and how to address them
Most people don’t know what microservices architecture is, for example, even if some of the apps they use every day were built in decoupled fashion. But technical evolutions like microservices don’t tend to cause the kinds of emotional responses that AI does around potential social and economic impacts. Nor have microservices haven’t been immortalized in popular culture: No one is lining up at the box office for "Terminator: Rise of the Cloud-Native Apps."
This speaks mainly to fears about AI’s nebulous future, and it can be tough to evaluate their validity when our imaginations run wild. That’s not particularly useful for IT leaders and other execs trying to build a practical AI strategy today. Yet you will encounter fears – many of them well-founded. The trick is to focus on these real-world concerns, not the time-traveling robot assassins. For starters, they’re much easier to defeat – er, address – because they’re often based in current reality, not futuristic speculation.
“The types of fears [people have about AI] depend on the type of AI that we are talking about,” says Keiland Cooper, a neuroscience research associate at the University of California Irvine and co-director of ContinualAI. “The more theoretical and far off ‘general AI’ – a computer that can do all the things that humans can do – will raise more fears than those from a more realistic AI algorithm like we see being commonly used today.”
Let’s look at five legitimate concerns about AI today – and expert advice for addressing them so that they don’t derail your AI plans.
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CentOS 8 "Gnome Desktop" overview | The community enterprise operating system
In this video, I am going to show an overview of CentOS 8.0.1905 "Gnome" and some of the applications pre-installed.
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How deep does the vDPA rabbit hole go?
In this post we will be leading you through the different building blocks used for implementing the virtio full HW offloading and the vDPA solutions. This effort is still in progress, thus some bits may change in the future, however the governing building blocks are expected to stay the same.
We will be discussing the VFIO, vfio-pci and vhost-vfio all intended on accessing drivers from the userspace both in the guest and the host. We will also be discussing MDEV, vfio-mdev, vhost-mdev and virtio-mdev transport API constructing the vDPA solutions.
The post is a technical deep dive and is intended for architects, developers and those who are passionate about understanding how all the pieces fall into place. If you are more interested in understanding the big picture of vDPA, the previous post "Achieving network wirespeed in an open standard manner - introducing vDPA" is strongly recommended instead (we did warn you).
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CNCF’s Envoy report card shows Google, Lyft are top of contributing class
The CNCF has delivered a report card on Envoy, the open source edge and service proxy which is usually mentioned alongside the words Kubernetes or service mesh.
The report comes a year after Envoy graduated from the CNCF incubation process, and the headline scores are 1,700 contributors, who have made 10,300 code commits, 5,700 pull requests and 51,000 contributions overall.
Envoy was initially developed in 2016 at Lyft, the ridesharing giant which isn’t Uber, and this is reflected in the CNCF report. Lyft still accounts for 30.4 of the Envoy code, though Google is the biggest contributor overall, with 42.8 per cent of the code.
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Highly Automated and Secured Multi-Tenancy Using SUSE CaaS Platform 4
I notice that when it comes to using a XaaS solution, clients and solution architects are typically concerned about multi-tenancy. This post attempts to decipher why that is and how SUSE CaaS Platform helps make this a reality.
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Dilution and Misuse of the "Linux" Brand
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