AWS, Intel, Adlink Join Forces on AI at the Edge

The integrated product combines AWS, Intel, and Adlink software and services to automate edge...

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Juniper CTO Bikash Koley Calls It Quits

The company today said it hired Raj Yavatkar as the new CTO. Like Koley, Yavatkar previously headed...

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Automation Analytics: Part 2 – Looking at Data Collection

blog_getting-started-Automation-Analytics-Part-2

We recently released Red Hat Ansible Automation Platform which now includes multiple Software-as-a-Service (SaaS) offerings, one of which is Automation Analytics.  This offering provides a visual dashboard, health notifications and organization statistics for your Ansible Automation. Automation Analytics works across multiple Ansible Tower clusters allowing holistic analytics across your entire automation infrastructure.

In a previous blog I wrote about Getting Started with Automation Analytics, but now want to expand on what data is collected and how to gain access to that data.  I highly recommend reading the previous blog if you are new to Red Hat Ansible Automation Platform, Ansible Tower concepts and our SaaS offerings. This is important to many customers because they all have their own security concerns with what data leaves their premises as well as  obligations to their own customers and stakeholders to make sure data sent will not be compromised in any way.

 

Retrieving the data:

Analyzing the data

config.json

counts.json

cred_type_counts.json

events_table.csv

instance_info.json

inventory_counts.json

job_counts.json

job_instance_counts.json

manifest.json

org_counts.json

projects_by_scm_type.json

query_info.json

unified_jobs_table.csv

unified_job_template_table.csv

Where to go next?



Retrieving the data:

Login to the Ansible Tower host with Continue reading

The Week in Internet News: $1,200 a Month for Internet Service?

Gold-plated Internet access: Ulukhaktok, a small town in Canada’s Northwest Territories, is exploring ways to build its own broadband network after complaints of slow speeds and data caps, Vice.com reports. The price for exceeding the 10 GB data cap cost one resident $1,200 for the month. As part of the community-led effort, several residents have completed training on community networks with the Internet Society, which is supporting the project.

Editing ordered: Singapore’s government has ordered Facebook to “correct” a user’s post that contained accusations about the arrest of a supposed whistleblower and election rigging, in the first use of the country’s fake news law, Reuters says. The government called the allegations “false” and “scurrilous” and ordered blogger Alex Tan to issue a correction. But Tan does not live in Singapore and says he is an Australian citizen, and he refused to comply.

China joins in: Meanwhile, the Chinese government is targeting fake news and deep fake videos under new Internet content rules, Reuters reports. In addition, any use of AI or virtual reality needs to be clearly marked in a prominent manner in the government’s efforts against deep fakes. Failure to follow the rules could be considered a criminal Continue reading

Keeping Pace In A Fast-Moving AI Space

During the Intel AI Summit earlier this month where the company demonstrated its initial processors for artificial intelligence training and inference workloads, Naveen Rao, corporate vice president and general manager of the Artificial Intelligence Products Group at Intel, spoke about the rapid pace of evolution in the AI space that also includes machine learning and deep learning.

Keeping Pace In A Fast-Moving AI Space was written by Jeffrey Burt at The Next Platform.

Questions to Ask About Product Using Overhyped Technology

I stumbled upon a great MIT Technology Review article (warning: regwall ahead) with a checklist you SHOULD use whenever considering a machine-learning-based product.

While the article focuses on machine learning at least some of the steps in that list apply to any new product that claims to use a brand new technology in a particular problem domain like overlay virtual networking with blockchain:

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Efficient lock-free durable sets

Efficient lock-free durable sets Zuriel et al., OOPSLA’19

Given non-volatile memory (NVRAM), the naive hope for persistence is that it would be a no-op: what happens in memory, stays in memory. Unfortunately, a very similar set of issues to those concerned with flushing volatile memory to persistent disk exist here too, just at another level. Memory might be durable, but…

…it is expected that caches and registers will remain volatile. Therefore the state of data structures underlying standard algorithms might not be complete in the NVRAM view, and after a crash this view might not be consistent because of missed writes that were in the caches but did not reach the memory. Moreover, for better performance, the processor may change the order in which writes reach the NVRAM, making it difficult for the NVRAM to even reflect a consistent prefix of the computation.

Plus ça change, plus c’est la même chose.

So, we’re going to need to take care that everything we say is committed is truly durable, and that we can recover to a consistent state following a crash. The traditional way to accomplish this is with a write-ahead log. You’ll no doubt be familiar with the phrase Continue reading

HPE, DoE partner for AI-driven energy efficiency

HP Enterprise has partnered with the National Renewable Energy Laboratory (NREL), a unit of the Department of Energy, to create AI and machine learning-systems for greater data-center energy efficiency.The Department of Energy lab will provide HPE with multiple years’ worth of historical data from sensors within its supercomputers and in its Energy Systems Integration Facility (ESIF) High-Performance Computing (HPC) Data Center, one of the world's most efficient data centers. This information will help other organizations to optimize their own operations, said NREL.To read this article in full, please click here

HPE, DoE partner for AI-driven energy efficiency

HP Enterprise has partnered with the National Renewable Energy Laboratory (NREL), a unit of the Department of Energy, to create AI and machine learning-systems for greater data-center energy efficiency.The Department of Energy lab will provide HPE with multiple years’ worth of historical data from sensors within its supercomputers and in its Energy Systems Integration Facility (ESIF) High-Performance Computing (HPC) Data Center, one of the world's most efficient data centers. This information will help other organizations to optimize their own operations, said NREL.To read this article in full, please click here

SDxCentral’s Top 10 Articles — November 2019

Juniper Guns for Cisco, Aruba With Mist AI; Michael Dell: The Future of Tech Is Autonomous; and HPE...

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5G Is No Panacea for IoT

Industry observers agree that the outlook for IoT is up, but the trajectory of that growth and...

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© SDxCentral, LLC. Use of this feed is limited to personal, non-commercial use and is governed by SDxCentral's Terms of Use (https://www.sdxcentral.com/legal/terms-of-service/). Publishing this feed for public or commercial use and/or misrepresentation by a third party is prohibited.

Fast Friday- Perry Mason Moments

It’s the Thanksgiving holiday weekend in the US which means lots of people discussing things with their relatives. And, as is often the case, lots of arguments. It’s the nature of people to have a point of view and then to want to defend it. And it’s not just politics or other divisive topics. We see it all the time in networking too.

EIGRP vs OSPF. Cisco vs Juniper. ACI vs NSX. You name it and we’ve argued about it. Every viewpoint has a corresponding counterpart. Yes, there are good points for using one versus the other. But there are also times when every piece of factual information doesn’t matter because we “know” the right answer.

It’s those times when we run into what I call the “Perry Mason Problem”. It’s a reminder of the old Perry Mason TV show when the lawyer in the title would win a case with a carefully crafted statement that just ends any arguments. It’s often called a Wham Line or an Armor-Piercing Question. Basically, Mr. Mason would ask a question or make a statement that let all the air out of the argument. And often it would result in him winning the case Continue reading

Getting traffic into GKE Cluster

In this blog, I will talk about different options for getting traffic from external world into GKE cluster. The options described are: Network load balancer(NLB) Http load balancer with ingress Http load balancer with Network endpoint groups(NEG) nginx Ingress controller Istio ingress gateway For each of the above options, I will deploy a simple helloworld … Continue reading Getting traffic into GKE Cluster

[4/4] Composition & Service Function Chaining in Network Service Meshes

Following the Path

Welcome to part four of this series. This this final part, we will explore our options for networking a composed application, from a de-composed monolith or set of microservices.

Here is a logical set of options:

  1. Proxy: Having a network kernel, ADC or proxy for every component to handle implementation of the service chain. Sidecars quickly solve an issue, but double component count within a mesh. Proxies work well in public and private clouds, but for commercial applications may incur license costs as well as higher resource utilisation to cover the sidecar container.

  2. Language specific libraries: which wrap your application packets in a NSH handling outer encapsulation. No sidecar required, no modification of a host. This adds complexity to software development in terms of modified socket libraries, but a well designed and implemented library does not expose the complexity. All your code has to do, is accept connections through a modified socket library. This works in the cloud providing security policies and routing domains allow it.

  3. Overlay: Add flow data to forwarding entities. Let’s face it, this isn’t going to happen in a cloud environment unless you’ve implemented a full overlay. An OpenVSwitch (OVS) overlay network would Continue reading