Organizations have increasingly set their sights on edge computing as a solution to their data storage needs. According to a survey from Forrester Analytics Global Business Technographics® Mobility, 57% of mobility decision-makers want to incorporate edge computing into their plans by the end of 2020.
Because it uses a number of smart devices around the “edge” of a network to host data rather than at the core of a single cloud, edge computing is typically thought of as a decentralized alternative to cloud computing, However, more companies are now considering using edge computing and cloud computing in tandem. Combining these technologies would move certain facets of mobile and Internet of Things (IoT) applications closer to the edge of the network. This would lower latency and quicken response times for users, while still giving other components of those applications access to the cloud, where enterprises can collect and analyze data in in a central location.
While combing edge computing with the cloud offers many benefits, it also poses some security risks.
“Devices have to be authenticated. Applications need to be authorized. Access to edge through APIs needs to be regulated and secured. The fact that the device and the edge need to authenticate each other is critical to ensure secure computing at the edge of the network,” Shamik Mishra, vice president of technology and innovation at Paris-based engineering company Altran, told Tech Target.
How Nanoscale Technology Can Sharpen Edge Computing
Data has exploded in recent years. By the end of 2020, the internet is expected to hold a massive 40 zettabytes of data. With 5G on the horizon, the importance of efficient data storage will continue to grow. Meanwhile, the computing and bandwidth capabilities needed to maintain all of that data will grow alarmingly slim. While edge computing poses a possible solution, experts doubt it will be enough. To solve the problem, the computational power of devices will need to grow while the devices themselves will need to get smaller, according to recent research from ORNL.
Nanoscale devices offer a possible solution. While additional research and development is needed, these tiny applications could deliver profound computational functions at an infinitesimal scale through the capability to securely transport information up to 1,400 kilometers in free-space channels.
“We need communications across disciplines,” said Ali Passian, one of the researchers behind the ORNL study. “Just as math is transforming biology and vice versa, edge computing and nanoscience are transforming each other.”
How Artificial Intelligence Can Enhance Edge Computing
Artificial intelligence (AI) is also expected to enhance edge computing by allowing it to better handle the bandwidth, latency, and storage issues created by the continuous growth of sensors. For instance, a super processor with embedded AI could help oversee devices around the “edge”. It could govern data throughout the network, enhance decision making locally, and filter out unnecessary information from the cloud while siphoning in necessary information, thereby enhancing the network’s overall efficiency, speed, and capability.
Get Close to the Edge
Many organizations don’t fully know what impact edge computing can have on their business. For one company, it could mean installing on-site servers that are capable of nearly real-time IoT data analysis. For another company, it could mean reducing organizational costs by using smaller deployments. One key benefit to edge computing is that it can be customized to meet an organization’s specific needs.
Prepare your organization for edge computing integration. Designed to train your entire team to support edge computing, IEEE Introduction to Edge Computing is an online five-course program. To learn more about getting access to these courses for your organization, connect with an IEEE Content Specialist today.
Interested in the course for yourself? Visit the IEEE Learning Network (ILN) to learn more.
Carew, Joseph M. (28 February 2020). Edge computing use cases led by autonomous cars and coffee bars. TechTarget.
ORNL. (25 February 2020). ORNL researchers identify the most promising tech to advance edge computing. Inside HPC.
Sunil, Abhijit. (4 November 2019). Predictions 2020: Edge Computing Makes The Leap. Forrester.
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