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Category Archives for "Networking"

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

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

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:

  1. Pick one small network task to automate
  2. Configure it manually on a small scale
  3. Mimic the manual configuration in Ansible
  4. 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