Someday soon, personal sensors, wearable gadgets, and embedded devices and services may make today’s PCs, laptops, tablets, and smartphones look quaint by comparison. But as the Internet of Everything (IoE) ─ with its diverse array of devices accessing a plethora of existing and new services ─ continues to rapidly evolve, user friendly interfaces mask growing complexity within networks. An article on today’s digital designers in the September 2013 issue of Wired captured how the focus is now “creating not products or interfaces but experiences, a million invisible transactions” and that “even as our devices have individually gotten simpler, the cumulative complexity of all of them is increasing.”
Which inevitably takes us behind the curtain to the exciting challenge of building hyper-efficient programmable networks using virtualization, the cloud, Software Defined Networking (SDN), and other technologies, architectures, and standards.
So far, this blog series on The Programmable Network has described various new and exciting capabilities leading to greater efficiencies and cost benefits. We’ve shared with you how you can now:
Visualize and control traffic using path computation via a network controller
Monitor and optimize traffic flows across network connections
Order services through an easy-to-use online portal which then launches automated service creation tasks
How do you get a feel for things? Perhaps a little research online, a review or two, maybe a referral from a friend or co-worker. But big purchases, such as a new car may require more; more information. So you go to take a test drive. Well, we have something similar to a test drive.
As you may know, it is not often you get a chance to check out how an IT device’s graphical user interface (GUI) looks and feels. Sure you might see a couple of static screen capture and be able to point how the navigation menu is laid out. But beyond that, it is not until the device is purchased and in the installation process, that the real user experience is realized. It’s hard to get a grasp on on the level of complexity for set-up and deployment, let alone configure a VLAN or set-up a secure VPN.
Well, we have offered something better. Our team has delivered a set of device emulators, including switches, access points and routers. You can actually navigate through the actual menus, see how the wizards look and work, and truly get a sense of how easy the small business products are to configure, install, deploy and manage.
You will notice that all of the small business product user interfaces share the same look and feel, as well as similar general navigation principles. With our Small Business product line, we truly take to heart the need for a great user experience and are always looking to make our products easier to use.
Please, leave us a comment or suggestion good, bad or otherwise to help us improve our products.
Within the coming decade, Internet Protocol version 6 (IPv6) will be key to enabling 50 billion connections among people, processes, data, and things in the Internet of Everything (IoE). But how we get there from here is not a simple matter.
I’m very pleased to invite Mark Townsley, Cisco Fellow and recognized industry expert on IP, to discuss this important transition in the second of our three-part blog series on IPv6. The first blog in Mark’s series was “Demystifying IPv6”.
Three years ago, I organized a conference in Paris where I thought it would be fascinating to bring together the original designers of IPv6 alongside the engineers who were finally deploying it at scale more than a decade later. During this discussion, Steve Deering, one of the “fathers” of IPv6 in the 1990s, was asked one of the most common questions about IPv6: Why wasn’t it designed for backward compatibility with IPv4? After all, wouldn’t it be easier to make the transition if the two versions could transparently coexist? Steve answered that the problem is not that IPv6 wasn’t designed to be backward-compatible—the real problem is that IPv4 wasn’t designed to be forward-compatible.
Steve was making the point that IPv4 was designed with a fixed address space. Given the number of computers connected to the Arpanet throughout the 1970s, this fixed-length address field seemed to be sufficient—at least for that version of IP. IP had been replaced before, and it seemed perfectly reasonable at the time that it might be replaced again. Read More »
At the recent Cisco Live 2013 event in Orlando, I talked about the business value of converging operations technology (OT)—used for industrial automation systems—with IT business networks, in order to create more secure, end-to-end, standard communications and control. Regarding business value of IT/OT convergence for machine builders/integrators and consequently their manufacturing customers, I referenced a case study involving Comau Group that Al Presher from DesignNews recently picked up in a blog entitled “Connectivity Enabling Smart Manufacturing.”
Comau is a leading supplier and partner for most global automakers, integrating welding and assembly lines that coordinate dozens of robots and ancillary automation across multiple stations.
The order-to-engineering sign-off cycle requires months and the consequent build and commissioning to full production adds many more months for a new or refreshed manufacturing line.
Multiple fieldbus protocols at the device level complicate both design and implementation, requiring more integration services—time and money—to make the system work.
By designing a converged IT/OT “Connected Machine” solution that utilizes IP-standards-based, off-the-shelf modularity with a network architecture validated for both business and controls topologies, Comau has been able to reduce engineering cycles and cut integration time by more than two-thirds. Quoting an Engineering Manager from the company, “Installation, commissioning and debugging for 10 stations with 12-15 robots takes a couple days, rather than 1-2 weeks.” Read More »
Much has been written about the vast number and variety of things that will soon be connected to the Internet—from milk cartons and alarm clocks to sensors and trains. Already in 2008, that number exceeded the number of people on earth. By 2020, when the next incarnation of the Internet—aka the Internet of Things—is in full swing, the number is expected to reach 50 billion. And it’s not just things that will add value and relevance to networked connections, but also people, data and processes.
Think about it. Through their interactions with the Web, social networks and devices—especially mobile devices—people have a massive multiplier effect on the amount of IP traffic traversing the network. In 2012 alone, new, more powerful smartphone technologies combined with growth in both mobile bandwidth and apps produced annual mobile data traffic nearly 12 times greater than the total Internet traffic in 2000 (Cisco Mobile VNI 2013).
Add to that a coming tsunami of constantly streaming data as sensors in just about everything become the norm—not just wearable sensors attached to our bodies, clothes and shoes, but also sensors, meters and actuators in our cars, machinery and infrastructure. And let’s not forget the critical role that processes will play in managing and automating this explosive growth in connections as well as in the collection, analysis and communication of data. People, data, processes and things. Together, they will make up the next phase of the Internet of Things—the Internet of Everything.
Data in Motion vs. Data at Rest
Zooming in on data in the age of the Internet of Everything, there’s another critical distinction that needs to be made. You see, not all data is created equal. Most of the new data being generated today is real-time data that fits into a broad category called Data in Motion. This refers to the constant stream of sensor-generated data that defies traditional processes for capture, storage and analysis, and requires a fundamentally different approach.
Let’s back up a minute. Historically, in order to find gems of actionable insight, enterprises have tended to focus their analytics or business intelligence applications on data captured and stored using traditional relational data warehouses or “enterprise historian” technologies.
However, the limits of this approach have been tested by the increase in volume of this so-called Data at Rest. The challenges inherent in collecting, searching, sharing, analyzing and visualizing insights from these ever-expanding data sets have led to the development of massively parallel computing software running on tens, hundreds, or even thousands of servers. As innovative and adaptive as these Big Data technologies are, they still rely on historical data to find the proverbial needle in the haystack.
This rising tide of Data in Motion is not going to slow down. In fact, as the Internet of Everything gathers momentum, the vast number of connections will trigger a zettaflood of data, at an even more accelerated pace. While this new Data in Motion has huge potential, it also has a very limited shelf life. As such, its primary value lies in its being captured soon after it is created—in many cases, immediately after it is created.
For instance, real-time traffic information from cameras, sensors and connected cars allows drivers to avoid traffic jams and use suggested alternate routes, potentially reducing hours of unproductive time spent behind the wheel. Similarly, manufacturers can connect their stock inventory with their suppliers’ production systems so that potential delays can be identified as early as possible and corrective actions taken on their respective shop floors to better prioritize people’s activities. In each of these cases, it’s easy to see the added value of connecting not just things, but also people, data and processes.
The real challenge for data-driven organizations is how to manage and extract value from this constant stream of information, and turn it to competitive advantage. Data in Motion represents a new type of data whose value can not always be extracted through traditional analytics. In a next post, we will look at examples of Data in Motion and how to extract value from it.