Software Defined Operations reports, articles, and presentations
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FREE EVENT REPORT
Free Event Report by Event Host/Heavy Reading Analyst, James Crawshaw
This special Light Reading report explores the key themes under discussion at this year's event; including the importance of open source initiatives for NFV, the increasing role that AI/ML plays in network automation, the need for open APIs to support network programmability, and the role that blockchain can play in telecom automation.
TOP COLUMNS FROM LIGHT READING
Colt Ramps Its Blockchain Efforts, Explores SDN Federation Use Cas
Colt, often at the vanguard of new technology deployments, has invested considerable time and effort during the past year in figuring out how blockchain technology can help boost operational efficiencies, improve customer experience and open up new revenue opportunities. And now it's looking at how the distributed ledger technology, best known for underpinning cryptocurrencies such as Bitcoin, could make SDN interoperability easier to manage.
Verizon: Vendor AI Not Ready for Prime Time
For all the buzz around artificial intelligence, vendors have yet to produce practical approaches that live up to that hype, says a Verizon executive engaged with his own team developing internal approaches that ultimately will use AI.
Matt Tegerdine, director of network performance analytics for Verizon Communications Inc. (NYSE: VZ)'s wireline side, says there is no shortage of vendors willing to pitch AI products that they claim will dramatically cut network operations costs. But in his experience, most of those claims fall apart in the face of the size and complexity of the Verizon network and the amount of data it generates
Open APIs: The Key to the Platform Business Model
The Application Programming Interface (API) is a key computer science concept that allows developers to build rich applications that tap into and provide a mash-up of other, typically web-based, applications. By exposing their applications through well-documented and easy to understand interfaces (definitions and protocols), Facebook, Google and others are able to consolidate their position at the center of the web and smartphone app economy.
Is Open Source the Right Approach for NFV Orchestration?
Once upon a time there was a maharaja who decided to raise a baby elephant as a pet (stick with me…). As the elephant grew, it became more and more expensive to feed and created such a mess that eventually the maharaja told his courtiers that he was gifting them the elephant out of the generosity of his heart. In return they would have to look after the elephant and bring it back to him when it was a bit more mature and stable enough for him to ride.
Some might say that, in the context of NFV MANO (management and orchestration), the elephant is Open Network Automation Platform (ONAP) and the maharaja is AT&T Inc. (NYSE: T). But that would be unfair. In reality there are two maharajas -- AT&T and China Mobile Ltd. (NYSE: CHL) -- and two elephants that have been merged into a six-legged Loxodonta with two tails and three tusks. (See MANO Marriage: ECOMP, OPEN-O Converge as ONAP.)
How to Prevent Your Data Lake From Turning Into a Swamp
In their book Building the Network of the Future, Mazin Gilbert and Mark Austin of AT&T describe the big data framework that the operator has adopted to process the 118 petabytes of data that pass through its networks each day (as of 2016).
The operator not only tracks the payload of data that traverses its networks but also captures and stores, for later analysis, myriad data from user devices, radio access infrastructure, core network elements (such as XDRs), Internet cloud infrastructure (for example, CDN logs) as well as the application data itself (such as website logs). Much of this data stored in a Hadoop-based system running on common, off-the-shelf hardware. Feeding the Hadoop distributed file systems is a data ingestion engine based on open-source tools such as Kafka, Flume and Scoop. Sitting on top of Hadoop (figuratively) are modules for analytics (SPARK), batch processing (Map Reduce), search (SOLR) and NoSQL (e.g., MongoDB, Cassandra).