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Optimize Big Data Performance with Software-Defined Networking


Last March I wrote a blog explaining how software-defined networking is enabling highly adaptable and programmable networks that are ideally suited for today’s cloud-based applications and on-demand services.  Hadoop clusters are a perfect example. 


Many OEMs are leveraging Hadoop to maximize the economics and performance of their Big Data solutions. The popular open source software contains costs and improves scalability by enabling distributed, parallel processing of massive data sets across clusters of commodity x86 servers.  But all that server-to-server traffic has to be transported quickly and efficiently alongside other data center flows.  Bandwidth contention or network latency can degrade Hadoop performance and impair business-critical analytics.


SDN provides abstract, standards-based APIs for orchestrating network flows in real-time, making it easy for applications to classify and prioritize traffic flows on-demand.  The technology is perfect for optimizing the performance of data intensive, delay-sensitive applications like Hadoop.  In fact, an experiment conducted by Infoblox, revealed an SDN-aware version of Hadoop achieved a 40% improvement against a key performance benchmark, compared to a non-SDN-aware version.  In the SDN test, Hadoop traffic was forwarded ahead of other, less critical traffic, and so the job completed faster, even when executed on a congested network.


Srujan Sama, HP Networking’s SDN Product Marketing Manager, recently posted a blog recapping the benefits of SDN for Big Data.  Check out Srujan’s post to learn how SDN can help improve the performance of your Big Data and Analytics solutions.  And be sure to visit HP’s SDN information center and SDN App Store to read about the latest innovations in software-defined networking.


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