Scientists Say They Can Make Data Networks Faster And More Efficient

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Computer scientists at the Rice University just received a National Science Foundation grant to develop programming methods in order to analyze streaming data.

Ang Chen and Eugene Ng are the ones that received a three-year, $1.2 million grant to make data networks faster.

Chen is the lead investigator in the project, explaining that he and Ng can make all the components that are between end users and data servers to work in managing and analyzing big data. This way, data networks will not only be faster, but also more efficient. As a result, financial services, social networks, the internet and all applications that require a fast data network can benefit from their work.

Changing Where Processing is Done

Ng is a professor of computer science and electrical and computer engineering, explaining that they can program many elements in the data networks and they can change them to share some of the work done by the servers:

“Today, all the processing is done at the server, without any processing or computation along the path. We’re going to try to change that.”

Chen is an assistant professor of computer science and electrical and computer engineering, explaining that their approach matches different types of protocols:

“Our vision is to optimize all of these components to achieve a sweet spot in the design space for each application. We hope to have an approach that can work across different kinds of protocols.”

Ng explains that streaming data also includes other components like fraud detection, monitors, environmental sensors and others which also generate data and send it at high speed to servers around the world. They plan to create a “scalable platform that allows programmers to derive real-time insight from data utilizing the technologies we propose.”

Adapting the Physical Network

The approach Ng and Chen propose is to process and reduce the amount of data before it reaches the servers. They can program components to handle as much computation as they can to process the data and generate “a partial answer to your question.”

Ng explains that the emerging hardware for big data processing is new and people have not yet figured out how to tap its potential. They will study to see how data flows through networks and find a way to optimize it on the way, explains Chen:

“We’re looking at how an underlying physical network can adapt itself and change the network flow to optimize latency.”

Doris’s passion for writing started to take shape in college where she was editor-in-chief of the college newspaper. Even though she ended up working in IT for more than 7 years, she’s now back to what he always enjoyed doing. With a true passion for technology, Doris mostly covers tech-related topics.