A great essay by Bruce Schneier about (lack of) security in IoT and why things won’t improve without some serious intervention.
While the importance of the cloud is obvious to anyone, the increasing importance of the edge is often overlooked. As digitization and the Internet of Things are leading to an exponential growth in the number of devices, the amount of data that is being generated by sensors in devices such as self-driving-cars, mobile endpoints and people tracking systems for retail is astronomical. Analyzing and turning that data into immediate actions is key to success in the era of digitization. The cloud enables massive data storage and processing, but it does not always lend itself to real time processing and immediate actions. Latency and the sheer amount of data to be transmitted are much less of a factor for the edge compared to the data center. In order to make instant decisions, some of the data processing needs to happen at the edge. At the same time, a large number of employees no longer work form the corporate HQ, but have ever increasing expectations with regards to application access regardless of their physical location. Distributed computing across the edge, along with high performance cloud access and distributed security enforcement give organizations “the edge”. Centralizing management and operations with distributed control and Continue reading
The science fiction of a generation ago predicted a future in which humans were replaced by the reasoning might of a supercomputer. But in an unexpected twist of events, it appears the it is the supercomputer’s main output—scientific simulations—that could be replaced by an even higher order of intelligence.
While we will always need supercomputing hardware, the vast field of scientific computing, or high performance computing, could also be in the crosshairs for disruptive change, altering the future prospects for scientific code developers, but opening new doors in more energy-efficient, finer-grained scientific discovery. With code that can write itself based …
When Will AI Replace Traditional Supercomputing Simulations? was written by Nicole Hemsoth at The Next Platform.
Apteligent can predict how application performance impacts revenue within the enterprise.
Effective network troubleshooting requires experience and a detailed understanding of a network’s design. And while many great network engineers possess both qualities, they still face the daunting challenge of manual data collection and analysis.
The storage and backup industries have long been automated, yet, for the most part, automation has alluded the network, forcing engineering teams to troubleshoot and map networks manually. Estimates from a NetBrain poll indicate that network engineers spend 80% of their troubleshooting time collecting data and only 20% analyzing it. With the cost of downtime only getting more expensive, an opportunity to significantly reduce the time spent collecting data is critical.
Effective network troubleshooting requires experience and a detailed understanding of a network’s design. And while many great network engineers possess both qualities, they still face the daunting challenge of manual data collection and analysis.
The storage and backup industries have long been automated, yet, for the most part, automation has alluded the network, forcing engineering teams to troubleshoot and map networks manually. Estimates from a NetBrain poll indicate that network engineers spend 80% of their troubleshooting time collecting data and only 20% analyzing it. With the cost of downtime only getting more expensive, an opportunity to significantly reduce the time spent collecting data is critical.
To read this article in full or to leave a comment, please click here
Effective network troubleshooting requires experience and a detailed understanding of a network’s design. And while many great network engineers possess both qualities, they still face the daunting challenge of manual data collection and analysis.
The storage and backup industries have long been automated, yet, for the most part, automation has alluded the network, forcing engineering teams to troubleshoot and map networks manually. Estimates from a NetBrain poll indicate that network engineers spend 80% of their troubleshooting time collecting data and only 20% analyzing it. With the cost of downtime only getting more expensive, an opportunity to significantly reduce the time spent collecting data is critical.
To read this article in full or to leave a comment, please click here