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Data Management: Data-Centric Computing with Computational Storage
“Data! Data! Data! I cannot make bricks without clay,” cries Sherlock Holmes in a line from the movie. His sentiment surely rings true for many a data manager. The growth of data volumes and the increasing need to be able to access and process data as close to real-time makes the task of data management ever more complex.
What’s wrong with how data is handled today? latency, volume, power consumption, and costs
- Researchers have found that moving data is very expensive. A study by Google researchers estimates that 62.7 percent of computing energy is used to just move data back and forth between memory, storage, and the CPU.
- Internet traffic has grown by a factor of 16 over the past decade. A lot of that growth can be attributed to moving data from edge locations to the cloud and on-prem data centers.
One solution proposed to improve efficiency and limit data movement is to make storage systems smarter so that they can process data on the storage device itself. Computational Storage devices come with built-in memory, I/O, processors, storage capacity, and software to do just that.
The SNIA website says that “Computational Storage solutions typically target applications where the demand to process ever-growing storage workloads is outpacing traditional compute server architectures. These applications include AI, big data, content delivery, database, machine learning, and many others that are used industry-wide.”
Onur Celebioglu, Dell senior director of Engineering for HPC, told theRegister that “the growth in data, how we move data, and how we handle it if you have a very large volume of data — that’s going to continue being a challenge, not just for HPC, but for traditional IT as well. The sheer volume of data will risk creating a bottleneck. If we can analyze data where it is, that’s going to be one way to shift the paradigm, and I think computational storage technologies are going to start playing a bigger role, both in HPC and general IT.”
