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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

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

AI boosts data center availability, efficiency

ONIE and the Hammer of Thor

Someone left a comment on my Zero-Touch Provisioning post claiming how Big Switch Networks solved ZTP challenge using just IPv6 Link-Local Address and Neighbor Discovery instead of the complicated DHCP/TFTP/whatever sequence.

Here’s what he wrote:

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

HCI, Storage Revenue, and HPE Next Cost Cuts Boost HPE’s Bottom Line

The company saved about $83 million over the first half of fiscal 2018 though its HPE Next cost-cutting initiative.

Cato Networks Adds Threat Hunting Capability To Its SD-WAN Service

Cato Networks adds a threat-hunting capability to its SD-WAN service to detect compromised devices on customer networks.

EMA Report: Network Management Teams Shift Focus to SD-WAN, SDN, and SDDC

SD-WAN is priority for enterprises that want to make their networks more automated.

Kata Containers 1.0 Launch Hones In On Security, Interoperability Fears

The project claims greater security than traditional containers by tapping into virtual machine schema but remains compatible with Docker and Kubernetes in the container ecosystem.

Regular Expression for Network Engineer Part-2

This post is continuation of the Regular Expression for Network Engineer Part-1 , here we have a look for the different methods to find out the pattern in string.

Findall() – returns list of all the matches the pattern in a string without overlapping

EXAMPLE

[code language = “Python”]

re.findall(pattern, string[, flags])

In [118]: ip
Out[118]: ‘10.10.1.10,29.10.1.10,10.10.1.20,192.168.1.0,172.16.10.1,10.10.10.121’

In [119]: out= re.findall(r'(10.10.10.\d+)’ ,ip)
In [120]: out
Out[120]: [‘10,10.10.1’, ‘10.10.10.121’]

#Above example help us to find out all the IP’s of subnet 10.10.10.0/24 from group of ip’s.

[/code]

Match()-return a match object when pattern is found at the beginning of string, if no pattern is found ,result in None.

EXAMPLE

[code language = “Python”]

In [189]: text
Out[189]: ‘Cisco IOS Software, 7200 Software (C7200-SPSERVICESK9-M), Version 12.2(33)SRE, RELEASE SOFTWARE (fc1)’

In [190]: out = re.match(r”Cisco”,text)
In [191]: out
Out[191]: <_sre.SRE_Match object; span=(0, 5), match=’Cisco’>
In [192]: out = re.match(r” Software”,text)
In [193]: out
In [194]: out = re.search(r” Software”,text)
In [195]: out
Out[195]: <_sre.SRE_Match object; span=(9, 18), Continue reading

Regular Expression for Network Engineer Part-2

This post is continuation of the Regular Expression for Network Engineer Part-1 , here we have a look for the different methods to find out the pattern in string. Findall() – returns list of all the matches the pattern in a string without overlapping EXAMPLE Match()-return a match object when pattern is found at the […]

Automating Cumulus Linux with Ansible

Automating your network can seem like a daunting task. But the truth is that automating Cumulus Linux with Ansible can be easier than many of the things you’re probably already automating.

In this post, I’ll show you how to get started on your network automation journey using a simple, four-step process:

Pick one small network task to automate
Configure it manually on a small scale
Mimic the manual configuration in Ansible
Expand the automation to additional network devices

To illustrate, I’ll be using the following simple, bare-bones topology based on the Cumulus Reference topology. You can follow along by spinning up your own virtual data center for free using Cumulus in the Cloud.

Pick one network task to automate

The first step is to pick one thing to automate. Just one! The only caveat is that it needs to be something you understand and are comfortable with. Trying to automate a feature you’ve never used is sure to scare you away from automation forever, unless of course you have someone guiding you through the process.

Preferably, pick something that’s quick and simple when done manually. Configuring the OSPF routing protocol between two switches falls into this category. When done manually, Continue reading

Worth Reading: The Critical Web Page Performance Path

First of all, it would be useful to understand the steps the browser is actually going through. It starts with plain HTML, CSS, and JavaScript files goes through rendering and painting a page, and arrives in the end to become what the user sees. —Sibylle Sehl @Free Code Camp

C is to low level

I'm in danger of contradicting myself, after previously pointing out that x86 machine code is a high-level language, but this article claiming C is a not a low level language is bunk. C certainly has some problems, but it's still the closest language to assembly. This is obvious by the fact it's still the fastest compiled language. What we see is a typical academic out of touch with the real world.

The author makes the (wrong) observation that we've been stuck emulating the PDP-11 for the past 40 years. C was written for the PDP-11, and since then CPUs have been designed to make C run faster. The author imagines a different world, such as where CPU designers instead target something like LISP as their preferred language, or Erlang. This misunderstands the state of the market. CPUs do indeed supports lots of different abstractions, and C has evolved to accommodate this.

The author criticizes things like "out-of-order" execution which has lead to the Spectre sidechannel vulnerabilities. Out-of-order execution is necessary to make C run faster. The author claims instead that those resources should be spent on having more slower CPUs, with more threads. This sacrifices single-threaded performance in exchange Continue reading

Cisco Warns Massive Russian Malware Attack Hit 500k Routers Globally

Security researchers tied the malware to a Russian group responsible for hacking incidents during the 2016 U.S. presidential campaign.

Service Providers Grapple With ‘VNF Islands’

Newly released SDxCentral 2018 report on the VNF Ecosystem finds SD-WAN and vCPE are top use cases for NFV and the driving force behind service providers virtualizing their networks.

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