Two new abstraction frameworks ensure interoperability between OpenFlow v1.3-enabled hardware-based switches from different vendors.
The beauty of software-defined networks is that they give you the freedom to program your network, down to individual flows, based on business requirements. However, too much freedom can be overwhelming.
The OpenFlow protocol provides a rich set of control capabilities, not all of which are supported by all switches. To date, SDN applications, controllers, and switches have had to sort out feature options at run-time, which has made interoperability (and predictable operation) difficult. For example, a switch typically includes one or more flow tables, which are organized as a pipeline. Currently, applications must be “pipeline aware,” which effectively makes them dependent on specific hardware.
The Open Networking Foundation and other SDN innovators recognized that some type of abstraction layer was needed to support hardware independence, and two major interoperability enablers have been developed: Table Type Patterns (TTPs) and flow objectives. These abstraction frameworks provide a foundation for full interoperability between OpenFlow v1.3-enabled switches — including hardware-based switches–making it safe for network operators of all types to start investing in SDN built on such hardware.
From Picasso’s “The Young Ladies of Avignon” to Munch’s “The Scream,” what was it about some paintings that arrested people’s attention upon viewing them, that cemented them in the canon of art history as iconic works?
In many cases, it’s because the artist incorporated a technique, form or style that had never been used before. They exhibited a creative and innovative flair that would go on to be mimicked by artists for years to come.
Throughout human history, experts have often highlighted these artistic innovations, using them to judge a painting’s relative worth. But can a painting’s level of creativity be quantified by Artificial Intelligence (AI)?
At Rutgers’ Art and Artificial Intelligence Laboratory, my colleagues and I proposed a novel algorithm that assessed the creativity of any given painting, while taking into account the painting’s context within the scope of art history.
In the end, we found that, when introduced with a large collection of works, the algorithm can successfully highlight paintings that art historians consider masterpieces of the medium.
The results show that humans are no longer the only judges of creativity. Computers can perform the same task – and may even be more objective.
Of course, the algorithm depended on addressing a central