A new survey of more than 300 storage professionals by computational storage company NGD Systems reportedly has found that bottlenecks and compute problems continue to plague IT pros as they struggle to support growing edge workloads. In the study entitled The State of Storage and Edge Computing conducted by Dimensional Research, barely one in 10 respondents gave themselves an “A” grade for their compute and storage capabilities.
The study, which was designed to understand the adoption and challenges of edge-related workloads, indicates that while enterprises are rapidly deploying technologies for real-time analytics, machine learning and IoT, they are still using legacy storage solutions that are not designed for such data-intensive workloads. In fact, 54 percent of respondents said their processing of edge applications is a bottleneck, and they desire faster and more intelligent storage solutions.
Edge platforms are driving the need for more localized compute capability, and 60 percent of storage professionals reported they are using NVMe SSDs to speed up the processing of large data sets; however, this has not solved their needs. As AI and other data-intensive deployments increase, data needs to be moved over increasingly longer distances, which causes network bottlenecks and delays analytic results.
“We were not surprised to find that while more than half of respondents are actively using edge computing, more than 70 percent are using legacy GPUs, which will not reduce the network bandwidth, power and footprint necessary to analyze mass data-sets in real time,” said Nader Salessi, CEO and founder, NGD Systems. “Computational Storage provides an innovative solution to today’s architecture, in which compute moves closer to where data is generated, rather than the data being moved up to compute. This is why computational storage is ideal for any organization deploying edge computing as its new model; it makes it possible to process data right where it’s created and needed, speeding up the time to analyze petabytes of data.”
The survey revealed that with the rapid growth of edge computing, compute cost and speed are still major challenges, as more organizations look for faster and more intelligent storage solutions:
Methodology
Storage professionals and software developers responsible for data-intensive workloads were invited to participate in a survey on their companies’ approach to AI, ML, Edge computing and real-time analytics. A total of 307 participants that develop, deploy, or manage data-intensive applications completed the survey.
Edited by
Ken Briodagh
