Community Networks Can Bridge the Digital Divide, But Some Still Need to Be Convinced

Community networks can help bring connectivity to many of world’s population still without it, but some governments, ISPs, and some potential users need to be convinced of their benefits, connectivity experts said.

Community networks can bring huge economic, educational, and social opportunities to areas without Internet access, Raul Echeberria, the Internet Society’s vice president for global engagement, said Wednesday.

With nearly half the world’s population still lacking Internet access, “this is creating a huge gap of opportunities,” he said during a community networking roundtable discussion hosted by the Internet Society.

Through community network projects such as a year-old network in the mountainous region of Tusheti in the nation of Georgia, the Internet Society has seen the proof that existing technologies can bring Internet service to some of the most remote areas on Earth, Echeberria said.

After a year of operation, the Georgian network is providing new economic opportunities to inn keepers and other tourism-related businesses in the region, said Ucha Seturi, director of the community network project there. Demand for Internet service is growing, he added.

With the technology questions largely solved, a key piece of the puzzle for community networks is getting the buy-in of the unserved communities and Continue reading

Thanks, Arista! We’ll Leave a Light on for You

Wow.  Just, wow.  Here we were at Pica8, ten days away from announcing a screamingly simplified white box switching architecture for enterprise campus networks – one that makes legacy switch stack and chassis switch replacement with disaggregated white box switches ridiculously easy — when Arista suddenly pops up and says that the success white box switches are having in the data center is now well and truly annoying to them, so they are starting to plan a future expedition to the enterprise campus in search of replacing lost revenue.

But after blowing straight past total market validation for us – we already have a number of ongoing enterprise campus network deployments inside some of Cisco’s oldest and largest accounts – Arista then went on to articulate the changes they envision for their future campus network switching push and, in doing so, came very close to a one-to-one mapping with what we’ve just announced as an orderable solution with our new PicaPilot switch orchestration software (see link below). (I really must order a case of champagne for my friends over there.)

Just how close is the mapping?  Well, let’s start with solving the urgent requirement to simplify Continue reading

Thanks, Arista! We’ll Leave a Light on for You

Wow.  Just, wow.  Here we were at Pica8, ten days away from announcing a screamingly simplified white box switching architecture for enterprise campus networks – one that makes legacy switch stack and chassis switch replacement with disaggregated white box switches ridiculously easy — when Arista suddenly pops up and says that the success white box switches are having in the data center is now well and truly annoying to them, so they are starting to plan a future expedition to the enterprise campus in search of replacing lost revenue.

But after blowing straight past total market validation for us – we already have a number of ongoing enterprise campus network deployments inside some of Cisco’s oldest and largest accounts – Arista then went on to articulate the changes they envision for their future campus network switching push and, in doing so, came very close to a one-to-one mapping with what we’ve just announced as an orderable solution with our new PicaPilot switch orchestration software (see link below). (I really must order a case of champagne for my friends over there.)

Just how close is the mapping?  Well, let’s start with solving the urgent requirement to simplify Continue reading

Data-center management: What does DMaaS deliver that DCIM doesn’t?

Data-center downtime is crippling and costly for enterprises. It’s easy to see the appeal of tools that can provide visibility into data-center assets, interdependencies, performance and capacity – and turn that visibility into actionable knowledge that anticipates equipment failures or capacity shortfalls.Data center infrastructure management (DCIM) tools are designed to monitor the utilization and energy consumption of both IT and building components, from servers and storage to power distribution units and cooling gear.[ Learn how server disaggregation can boost data center efficiency and how Windows Server 2019 embraces hyperconverged data centers . | Get regularly scheduled insights by signing up for Network World newsletters. ] DCIM software tackles functions including remote equipment monitoring, power and environmental monitoring, IT asset management, data management and reporting. With DCIM software, enterprises can simplify capacity planning and resource allocation as well as ensure that power, equipment and floor space are used as efficiently as possible.To read this article in full, please click here

AI boosts data-center availability, efficiency

Artificial intelligence is set to play a bigger role in data-center operations as enterprises begin to adopt machine-learning technologies that have been tried and tested by larger data-center operators and colocation providers.Today’s hybrid computing environments often span on-premise data centers, cloud and collocation sites, and edge computing deployments. And enterprises are finding that a traditional approach to managing data centers isn’t optimal. By using artificial intelligence, as played out through machine learning, there’s enormous potential to streamline the management of complex computing facilities.Check out our review of VMware’s vSAN 6.6 and see IDC’s top 10 data center predictions. Get regularly scheduled insights by signing up for Network World newsletters. AI in the data center, for now, revolves around using machine learning to monitor and automate the management of facility components such as power and power-distribution elements, cooling infrastructure, rack systems and physical security.To read this article in full, please click here

Data-center management: What does DMaaS deliver that DCIM doesn’t?

Data-center downtime is crippling and costly for enterprises. It’s easy to see the appeal of tools that can provide visibility into data-center assets, interdependencies, performance and capacity – and turn that visibility into actionable knowledge that anticipates equipment failures or capacity shortfalls.Data center infrastructure management (DCIM) tools are designed to monitor the utilization and energy consumption of both IT and building components, from servers and storage to power distribution units and cooling gear.[ Learn how server disaggregation can boost data center efficiency and how Windows Server 2019 embraces hyperconverged data centers . | Get regularly scheduled insights by signing up for Network World newsletters. ] DCIM software tackles functions including remote equipment monitoring, power and environmental monitoring, IT asset management, data management and reporting. With DCIM software, enterprises can simplify capacity planning and resource allocation as well as ensure that power, equipment and floor space are used as efficiently as possible.To read this article in full, please click here

AI boosts data-center availability, efficiency

Artificial intelligence is set to play a bigger role in data-center operations as enterprises begin to adopt machine-learning technologies that have been tried and tested by larger data-center operators and colocation providers.Today’s hybrid computing environments often span on-premise data centers, cloud and collocation sites, and edge computing deployments. And enterprises are finding that a traditional approach to managing data centers isn’t optimal. By using artificial intelligence, as played out through machine learning, there’s enormous potential to streamline the management of complex computing facilities.Check out our review of VMware’s vSAN 6.6 and see IDC’s top 10 data center predictions. Get regularly scheduled insights by signing up for Network World newsletters. AI in the data center, for now, revolves around using machine learning to monitor and automate the management of facility components such as power and power-distribution elements, cooling infrastructure, rack systems and physical security.To read this article in full, please click here

Data center management: What does DMaaS deliver that DCIM doesn’t?

Data-center downtime is crippling and costly for enterprises. It’s easy to see the appeal of tools that can provide visibility into data-center assets, interdependencies, performance and capacity – and turn that visibility into actionable knowledge that anticipates equipment failures or capacity shortfalls.Data center infrastructure management (DCIM) tools are designed to monitor the utilization and energy consumption of both IT and building components, from servers and storage to power distribution units and cooling gear.[ Learn how server disaggregation can boost data center efficiency and how Windows Server 2019 embraces hyperconverged data centers . | Get regularly scheduled insights by signing up for Network World newsletters. ] DCIM software tackles functions including remote equipment monitoring, power and environmental monitoring, IT asset management, data management and reporting. With DCIM software, enterprises can simplify capacity planning and resource allocation as well as ensure that power, equipment and floor space are used as efficiently as possible.To read this article in full, please click here

AI boosts data center availability, efficiency

Artificial intelligence is set to play a bigger role in data-center operations as enterprises begin to adopt machine-learning technologies that have been tried and tested by larger data-center operators and colocation providers.Today’s hybrid computing environments often span on-premise data centers, cloud and collocation sites, and edge computing deployments. And enterprises are finding that a traditional approach to managing data centers isn’t optimal. By using artificial intelligence, as played out through machine learning, there’s enormous potential to streamline the management of complex computing facilities.Check out our review of VMware’s vSAN 6.6 and see IDC’s top 10 data center predictions. Get regularly scheduled insights by signing up for Network World newsletters. AI in the data center, for now, revolves around using machine learning to monitor and automate the management of facility components such as power and power-distribution elements, cooling infrastructure, rack systems and physical security.To read this article in full, please click here

Data center management: What does DMaaS deliver that DCIM doesn’t?

Data-center downtime is crippling and costly for enterprises. It’s easy to see the appeal of tools that can provide visibility into data-center assets, interdependencies, performance and capacity – and turn that visibility into actionable knowledge that anticipates equipment failures or capacity shortfalls.Data center infrastructure management (DCIM) tools are designed to monitor the utilization and energy consumption of both IT and building components, from servers and storage to power distribution units and cooling gear.[ Learn how server disaggregation can boost data center efficiency and how Windows Server 2019 embraces hyperconverged data centers . | Get regularly scheduled insights by signing up for Network World newsletters. ] DCIM software tackles functions including remote equipment monitoring, power and environmental monitoring, IT asset management, data management and reporting. With DCIM software, enterprises can simplify capacity planning and resource allocation as well as ensure that power, equipment and floor space are used as efficiently as possible.To read this article in full, please click here

AI boosts data center availability, efficiency

Artificial intelligence is set to play a bigger role in data-center operations as enterprises begin to adopt machine-learning technologies that have been tried and tested by larger data-center operators and colocation providers.Today’s hybrid computing environments often span on-premise data centers, cloud and collocation sites, and edge computing deployments. And enterprises are finding that a traditional approach to managing data centers isn’t optimal. By using artificial intelligence, as played out through machine learning, there’s enormous potential to streamline the management of complex computing facilities.Check out our review of VMware’s vSAN 6.6 and see IDC’s top 10 data center predictions. Get regularly scheduled insights by signing up for Network World newsletters. AI in the data center, for now, revolves around using machine learning to monitor and automate the management of facility components such as power and power-distribution elements, cooling infrastructure, rack systems and physical security.To read this article in full, please click here

Algorithmic glass ceiling in social networks: the effects of social recommendations on network diversity

Algorithmic glass ceiling in social networks: the effects of social recommendations on network diversity Stoica et al., WWW’18

(If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site, or from the WWW 2018 proceedings page).

Social networks were meant to connect us and bring us together. This paper shows that while they might be quite successful at doing this in the small, on a macro scale they’re actually doing the opposite. Not only do they reinforce and sustain disparities among groups, but they actually reinforce the rate at which disparity grows. I.e., they’re driving us apart. This happens due to the rich-get-richer phenomenon resulting from friend/follow recommendation algorithms.

… we find that prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy… Our mathematical analysis demonstrates the existence of an algorithmic glass ceiling that exhibits all the properties of the metaphorical social barrier that hinders groups like women or people of colour from attaining equal representation.

Organic growth vs algorithmic growth

“In the social networks now governing the knowledge, Continue reading

Quick Post: Parsing AWS Instance Data with JQ

I recently had a need to get a specific subset of information about some AWS instances. Naturally, I turned to the CLI and some CLI tools to help. In this post, I’ll share the command I used to parse the AWS instance data down using the ever-so-handy jq tool.

What I needed, specifically, was the public IP address and the private IP address for each instance. That information is readily accessible using the aws ec2 describe-instances command, but that command provides a ton more information than I needed. So, I decided to try to use jq to parse the JSON output from the AWS CLI. If you’re not familiar with jq, I recommend you take a look at this brief introductory post I wrote back in 2015.

After some trial and error, here’s the final command I used:

aws ec2 describe-instances | jq '.Reservations[] | .Instances[] | \
{Id: .InstanceId, PublicAddress: .PublicIpAddress, \
PrivateAddress: .PrivateIpAddress}'

I’ll refer you to the jq manual for details on breaking down how this filter works. I’ll also point out that there’s nothing terribly groundbreaking or revolutionary about this command; I wanted to share it here just in case it may save someone Continue reading

The devil wears Pravda

Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to "core truth" of journalists. He is, without any sense of irony, going to call this "Pravda". This is not simply wrong but evil.


Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.

But what he's doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn't measured by whether they follow established journalism ethics, but whether their "core truths" agree with Musk's "core truths".

An example of the problem is how the press fixates on Tesla car crashes due to its "autopilot" feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He's got good reason to be concerned about this.

He argues that autopilot is safer Continue reading
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