IDG Contributor Network: 3 types of IoT platform analytics

Enterprises rely on their IoT platforms for many services. One of the most important is analytics. In layman’s terms, IoT analytics is the science and art of trying to find patterns in the massive quantity of data generated by connected assets. Or a more careful definition from MachNation’s IoT platform testing lab might be, analytics is the ability of a platform administrator or operator to monitor trends, identify abnormalities, and produce business insights from ingested IoT data.As a first step to identifying a best-in-class IoT platform for analytics, an enterprise should deploy the various platform analytics services. In particular, the enterprise should at least configure an on-platform analytics service for live streaming and stored/historical data; configure a platform for live streaming external analytics service integrations; and then export on-platform data to an external analytics service. These configuration tests will help an enterprise determine if an IoT platform vendor has designed exceptional or lackluster management tools and usability into its platform.To read this article in full, please click here

BiB 059: Recover From Cyber Attacks & Ransomware With Dell EMC

In this briefing, Dell EMC focused on their Cyber Recovery 18.1 product. You might be thinking, “Oh, another backup product. I already have one of those.” Sort of. Cyber Recovery is more than simply backup, and it’s more than what a decent disaster recovery plan gets you. The Cyber Recovery Vault is an orchestrated Data Domain storage platform that provides an isolated copy of known good data that can be used to recover from a security breach.

The post BiB 059: Recover From Cyber Attacks & Ransomware With Dell EMC appeared first on Packet Pushers.

Lessons from Andromeda

A common complaint I hear among network engineers is that the lessons and techniques used by truly huge scale networks simply are not applicable to more “standard scale” networks. The key point, however, is balance—to look for the ideas and concepts that are interesting and at least somewhat novel, and then see how they might be applied to products and systems in all networks. Learning concepts can help you understand design patterns you might encounter almost anywhere. One recent paper, for instance, details Andromeda, a large scale networking system designed and operated by Google, one of the few truly huge networks in the world—

Andromeda is designed around a flexible hierarchy of flow processing paths. Flows are mapped to a programming path dynamically based on feature and performance requirements.

While the paper describes the general compute environment, and the forwarding process on individual nodes, the most interesting part from a network engineering perspective is hoverboard. While this concept behind hoverboard has been implemented in previous systems, it is usually hidden under the covers of a vertically integrated system, and therefore not normally something you see the inner workings of. To understand hoverboard, you have to begin with a little theory Continue reading

Mass data fragmentation requires a storage rethink

Companies are experiencing a growing problem of mass data fragmentation (MDF). Data is siloed and scattered all over the organization — on and off premises — and businesses are unable to use the data strategically.When data is fragmented, only a small portion of it is available to be analyzed. In my last post, I described MDF as a single trend, but it can occur in a number of ways. Below are the most common forms of MDF: Fragmentation across IT silos: Secondary IT operations such as backups, file sharing/storage, provisioning for test/development and analytics are typically being done in completely separate silos that don’t share data or resources, with no central visibility or control. This results in overprovisioning/waste, as well as a challenge to meet service-level agreements (SLAs) or availability targets.   Fragmentation within a silo: There are even "silos within silos." Example: backup, where it is not uncommon to have four to five separate backup solutions from different vendors to handle different workloads such as virtual, physical, satabase, and cloud. On top of that, each solution needs associated target storage, dedupe appliances, media servers, etc., which propagate the silo problem. Fragmentation due to copies: Continue reading

Mass data fragmentation requires a storage rethink

Companies are experiencing a growing problem of mass data fragmentation (MDF). Data is siloed and scattered all over the organization — on and off premises — and businesses are unable to use the data strategically.When data is fragmented, only a small portion of it is available to be analyzed. In my last post, I described MDF as a single trend, but it can occur in a number of ways. Below are the most common forms of MDF: Fragmentation across IT silos: Secondary IT operations such as backups, file sharing/storage, provisioning for test/development and analytics are typically being done in completely separate silos that don’t share data or resources, with no central visibility or control. This results in overprovisioning/waste, as well as a challenge to meet service-level agreements (SLAs) or availability targets.   Fragmentation within a silo: There are even "silos within silos." Example: backup, where it is not uncommon to have four to five separate backup solutions from different vendors to handle different workloads such as virtual, physical, satabase, and cloud. On top of that, each solution needs associated target storage, dedupe appliances, media servers, etc., which propagate the silo problem. Fragmentation due to copies: Continue reading

Murphy the Chaos Manager

I had the opportunity to sit in on a great briefing from Gremlin the other day about chaos engineering. Ken Nalbone (@KenNalbone) has a great review of their software and approach to things here. The more time I spent thinking about chaos engineering and IT, the more I realized that it has more in common with Murphy’s Law that we realize.

Anything That Can Go Wrong

If there’s more than one way to do a job and one of those ways will end in disaster, then somebody will do it that way. – Edward Murphy

 

Anything that can go wrong will go wrong. – Major John Paul Stapp

We live by the adage of Murphy’s Law in IT. Anything that can go wrong will go wrong. And usually it goes wrong at the worst possible time. Database query functions will go wrong when you need them the most. And usually at the height of something like Amazon Prime Day. Data center outages only seem to happen at 4 am on a Sunday during a holiday.

But why do things go wrong like this? Is it because the universe just has it out for IT people? Are we Continue reading

The Week in Internet News: France Approves Fake News Law, Looks to Regulate Further

France tackles the Internet: The French government has been making news in recent days for examining ways to regulate the Internet. Its parliament has passed a controversial new law that would allow judges to order the immediate removal of online articles they deem to be fake news, Euronews.com reports. Critics warned the law, which allows for jail sentences for fake news creators, could lead to censorship.

Regulation or autocracy? French President Emmanuel Macron pushed the fake news law, and he also called for more government regulation of the Internet at a recent Internet Governance Forum in Paris. Macron called for international cooperation on Internet issues, as a way to tame disinformation, with CFR.org suggesting governments must adapt to fight modern problems. But TechDirt suggested Macron was acting like an autocrat in his call for more regulation.

Broadband plan MIA: Canada’s government, meanwhile, has “no plan” to bring broadband to rural and remote areas, a government auditor said. Canada’s rural broadband efforts so far have led to “a series of moving targets, lofty proclamations, piecemeal programs, and ultimately big letdowns,” Motherboard says.

Where the money is: Research firm IDC expects investments in Artificial Intelligence to triple in the next three Continue reading

Selectively deploying your superpowers on Linux

The sudo command is very handy when you need to run occasional commands with superuser power, but you can sometimes run into problems when it doesn’t do everything you expect it should. Say you want to add an important message at the end of some log file and you try something like this:$ echo "Important note" >> /var/log/somelog -bash: /var/log/somelog: Permission denied OK, it looks like you need to employ some extra privilege. In general, you can't write to a system log file with your user account. Let’s try that again with sudo.$ sudo !! sudo echo "Important note" >> /var/log/somelog -bash: /var/log/somelog: Permission denied Hmm, that didn't work either. Let's try something a little different.To read this article in full, please click here

IDG Contributor Network: Getting smarter about managing the SD-WAN last-mile

Delivering global SD-WAN is very different from delivering local networks. Local networks offer complete control to the end-to-end design, enabling low-latency and predictable connections. There might still be blackouts and brownouts but you’re in control and can troubleshoot accordingly with appropriate visibility.With global SD-WANs, though, managing the middle-mile/backbone performance and managing the last-mile are, well shall we say, more challenging. Most SD-WAN vendors don’t have control over these two segments, which affects application performance and service agility.In particular, an issue that SD-WAN appliance vendors often overlook is the management of the last-mile. With multiprotocol label switching (MPLS), the provider assumes the responsibility, but this is no longer the case with SD-WAN. Getting the last-mile right is challenging for many global SD-WANs.To read this article in full, please click here

IDG Contributor Network: Getting smarter about managing the SD-WAN last-mile

Delivering global SD-WAN is very different from delivering local networks. Local networks offer complete control to the end-to-end design, enabling low-latency and predictable connections. There might still be blackouts and brownouts but you’re in control and can troubleshoot accordingly with appropriate visibility.With global SD-WANs, though, managing the middle-mile/backbone performance and managing the last-mile are, well shall we say, more challenging. Most SD-WAN vendors don’t have control over these two segments, which affects application performance and service agility.In particular, an issue that SD-WAN appliance vendors often overlook is the management of the last-mile. With multiprotocol label switching (MPLS), the provider assumes the responsibility, but this is no longer the case with SD-WAN. Getting the last-mile right is challenging for many global SD-WANs.To read this article in full, please click here

Hyperconvergence: Not just for specific workloads anymore

Hyperconvergence has come a long way in a relatively short time, and enterprises are taking advantage of the new capabilities.Hyperconverged infrastructure (HCI) combines storage, computing and networking into a single system; hyperconverged platforms include a hypervisor for virtualized computing, software-defined storage, and virtualized networking.HCI platforms were initially aimed at virtual desktop infrastructure (VDI), video storage, and other discrete workloads with predictable resource requirements. Over time, they have advanced to become suitable platforms for enterprise applications, databases, private clouds, and edge computing deployments.To read this article in full, please click here

Hyperconvergence: Not just for specific workloads anymore

Hyperconvergence has come a long way in a relatively short time, and enterprises are taking advantage of the new capabilities.Hyperconverged infrastructure (HCI) combines storage, computing and networking into a single system; hyperconverged platforms include a hypervisor for virtualized computing, software-defined storage, and virtualized networking.HCI platforms were initially aimed at virtual desktop infrastructure (VDI), video storage, and other discrete workloads with predictable resource requirements. Over time, they have advanced to become suitable platforms for enterprise applications, databases, private clouds, and edge computing deployments.To read this article in full, please click here

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