IoT security vs. privacy: Which is a bigger issue?

If you follow the news surrounding the internet of things (IoT), you know that security issues have long been a key concern for IoT consumers, enterprises, and vendors. Those issues are very real, but I’m becoming increasingly convinced that related but fundamentally different privacy vulnerabilities may well be an even bigger threat to the success of the IoT.In June alone, we’ve seen a flood of IoT privacy issues inundate the news cycle, and observers are increasingly sounding the alarm that IoT users should be paying attention to what happens to the data collected by IoT devices.[ Also read: It’s time for the IoT to 'optimize for trust' and A corporate guide to addressing IoT security ] Predictably, most of the teeth-gnashing has come on the consumer side, but that doesn’t mean enterprises users are immune to the issue. One the one hand, just like consumers, companies are vulnerable to their proprietary information being improperly shared and misused. More immediately, companies may face backlash from their own customers if they are seen as not properly guarding the data they collect via the IoT. Too often, in fact, enterprises shoot themselves in the foot on privacy issues, with practices that Continue reading

IoT security vs. privacy: Which is a bigger issue?

If you follow the news surrounding the internet of things (IoT), you know that security issues have long been a key concern for IoT consumers, enterprises, and vendors. Those issues are very real, but I’m becoming increasingly convinced that related but fundamentally different privacy vulnerabilities may well be an even bigger threat to the success of the IoT.In June alone, we’ve seen a flood of IoT privacy issues inundate the news cycle, and observers are increasingly sounding the alarm that IoT users should be paying attention to what happens to the data collected by IoT devices.[ Also read: It’s time for the IoT to 'optimize for trust' and A corporate guide to addressing IoT security ] Predictably, most of the teeth-gnashing has come on the consumer side, but that doesn’t mean enterprises users are immune to the issue. One the one hand, just like consumers, companies are vulnerable to their proprietary information being improperly shared and misused. More immediately, companies may face backlash from their own customers if they are seen as not properly guarding the data they collect via the IoT. Too often, in fact, enterprises shoot themselves in the foot on privacy issues, with practices that Continue reading

Switch Buffer Sizes and Fermi Estimates

In my quest to understand how much buffer space we really need in high-speed switches I encountered an interesting phenomenon: we no longer have the gut feeling of what makes sense, sometimes going as far as assuming that 16 MB (or 32MB) of buffer space per 10GE/25GE data center ToR switch is another $vendor shenanigan focused on cutting cost. Time for another set of Fermi estimates.

Let’s take a recent data center switch using Trident II+ chipset and having 16 MB of buffer space (source: awesome packet buffers page by Jim Warner). Most of switches using this chipset have 48 10GE ports and 4-6 uplinks (40GE or 100GE).

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DC/SP. Part 13/8. Real-time data analytics and closed-loop automation with InfluxData Kapacitor for the Data Centre Fabric

Hello my friend,

This article is kind of a special one for me. It doesn’t mean that everything I have written before has a little sense. Everything what I have written about the Data Centre Fabric project was steps towards fully automated data centre operation, and today we make a final step towards the closed-loop automation based using the real-time data analytics by InfluxData Kapacitor.

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Brief description

Read the previous article about the DC/SP naming

According to the official website, InfluxData Kapacitor is alerting system following publish-subscribe design pattern, which supports both steam and batch data processing. If we translate it from the geeks’ language, it means that Kapacitor can subscribe to a certain to topics in the data producer (e.g., time series database – InfluxDB or collector – Telegraf) and start getting information out of it:

  • Steam processing means that all the information coming to the subscribed Continue reading

Huawei may get a 2-year reprieve on Trump ban

While President Trump wants to ban sales of Huawei networking gear in the U.S., the acting head of the Office of Management and Budget recommends putting that action off for two years because it would place too great a burden on U.S. companies supplying Huawei and using its equipment.If the ban goes through it will put the squeeze on the Chinese vendor, and the U.S. action could influence its allies to follow suit, further pressuring the company.To read this article in full, please click here

Beyond data and model parallelism for deep neural networks

Beyond data and model parallelism for deep neural networks Jia et al., SysML’2019

I’m guessing the authors of this paper were spared some of the XML excesses of the late nineties and early noughties, since they have no qualms putting SOAP at the core of their work! To me that means the “simple” object access protocol, but not here:

We introduce SOAP, a more comprehensive search space of parallelization strategies for DNNs that includes strategies to parallelize a DNN in the Sample, Operator, Attribute, and Parameter dimensions.

The goal here is to reduce the training times of DNNs by finding efficient parallel execution strategies, and even including its search time, FlexFlow is able to increase training throughput by up to 3.3x compared to state-of-the-art approaches.

There are two key ideas behind FlexFlow. The first is to expand the set of possible solutions (and hence also the search space!) in the hope of covering more interesting potential solutions. The second is an efficient execution simulator that makes searching that space possible by giving a quick evaluation of the potential performance of a given parallelisation strategy. Combine those with an off-the-shelf Metropolis-Hastings MCMC search strategy and Bob’s your uncle.

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As workloads migrate back from public clouds, hybrid cloud grows

Cloud computing gained popularity for its ease-of-deployment and flexible resource consumption, and while that works for many critical applications, it is not a panacea for every app an enterprise supports. That’s why as cloud adoption continues to rise, some companies are opting to bring workloads back on-premise in certain scenarios.To read this article in full, please click here(Insider Story)

As workloads migrate back from public clouds, hybrid cloud grows

Cloud computing gained popularity for its ease-of-deployment and flexible resource consumption, and while that works for many critical applications, it is not a panacea for every app an enterprise supports. That’s why as cloud adoption continues to rise, some companies are opting to bring workloads back on-premise in certain scenarios.To read this article in full, please click here(Insider Story)

Uptick in cloud repatriation fuels rise of hybrid cloud

Cloud computing gained popularity for its ease-of-deployment and flexible resource consumption, and while that works for many critical applications, it is not a panacea for every app an enterprise supports. That’s why as cloud adoption continues to rise, some companies are opting to bring workloads back on-premise in certain scenarios.To read this article in full, please click here(Insider Story)

Uptick in cloud repatriation fuels rise of hybrid cloud

Cloud computing gained popularity for its ease-of-deployment and flexible resource consumption, and while that works for many critical applications, it is not a panacea for every app an enterprise supports. That’s why as cloud adoption continues to rise, some companies are opting to bring workloads back on-premise in certain scenarios.To read this article in full, please click here(Insider Story)

Free course – Ethical Hacking: Hacking the Internet of Things

IoT devices are proliferating on corporate networks, gathering data that enables organizations to make smarter business decisions, improve productivity and help avoid costly equipment failures, but there is one big downside – security of the internet of things remains a problem.It makes sense, then, for enterprises to try to spot vulnerabilities in the IoT gear in their networks before they can be exploited by malicious actors.[ For more on IoT security see tips to securing IoT on your network and 10 best practices to minimize IoT security vulnerabilities. | Get regularly scheduled insights by signing up for Network World newsletters. ] To help this along, Network World and Pluralsight have teamed up to present a free course, Ethical Hacking: Hacking the Internet of Things, that provides IT pros with skills they need to protect their network infrastructure.To read this article in full, please click here(Insider Story)

The carbon footprints of IT shops that train AI models are huge

A new research paper from the University of Massachusetts, Amherst looked at the carbon dioxide (CO2) generated over the course of training several common large artificial intelligence (AI) models and found that the process can generate nearly five times the amount as an average American car over its lifetime plus the process of making the car itself.The paper specifically examined the model training process for natural-language processing (NLP), which is how AI handles natural language interactions. The study found that during the training process, more than 626,000 pounds of carbon dioxide is generated.To read this article in full, please click here

The carbon footprints of IT shops that train AI models are huge

A new research paper from the University of Massachusetts, Amherst looked at the carbon dioxide (CO2) generated over the course of training several common large artificial intelligence (AI) models and found that the process can generate nearly five times the amount as an average American car over its lifetime plus the process of making the car itself.The paper specifically examined the model training process for natural-language processing (NLP), which is how AI handles natural language interactions. The study found that during the training process, more than 626,000 pounds of carbon dioxide is generated.To read this article in full, please click here

Network Protocols and their Use

In June I participated in a workshop, organized by the Internet Architecture Board, on the topic of protocol design and effect, looking at the differences between initial design expectations and deployment realities. These are my impressions of the discussions that took place at this workshop.
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