By Oscar Martinez, key account manager of Advantech
There’s a revolution in the data center industry, stemming from new technology developments in IoT. Deep learning, artificial intelligence (AI) and machine learning (ML) are not only providing an abundance of data, but are also having a significant impact on how that data is leveraged. Most centralized cloud computing platforms are restricted by their limited ability to process these large amounts of data. However, a distributed architecture that incorporate IoT devices and edge servers can handle larger amounts of data and provide more detailed processing than ever before.
That said, with the capability to collect a wide array of data points from a limitless plethora of endpoints and an infinite amount of storage, we might be collecting and saving data without purpose. The cloud can basically become data’s garage, where you store things that you don’t have space for in the house. The question becomes whether the data is meaningful enough to justify use of the storage space. To solve for this, using edge computing and running inference engines on-premise with low-power gateways, you can process data before sending it to the cloud. This will ensure that the data being sent to storage is significant and meaningful, giving deep learning programs an opportunity to make more substantial connections and provide greater insights that could have previously been left on the table.
The data center industry is being greatly influenced by edge computing, allowing for better workflows and better allocated assets. Since edge computing takes some of the burden from the cloud, the cloud is then able to perform more detailed analytics while still giving the edge access to pull from the cloud and implement changes and updates. This transformative structure is giving the data center industry the ability to increase capabilities and provide valuable information and insights in much quicker timeframes.
For many businesses that are working with basic data sets, a traditional data center may provide incredible computing capacity, but it is a costly endeavor. It would be like having a doctor also check people into their office, process insurance information and take vitals. The important roles of the front desk staff and nursing assistants are there so that the doctor can focus on the most important part of their job – caring for patients. Traditional data centers can utilize edge computing so they can then focus on the most demanding and highest priority items.
The rise of edge computing is fueled by automation, with AI and ML managing workflows and processes without needing oversight and supervision. Greater focus and more meaningful insights are pulled because automation is taking the bulk of the work, with management and monitoring of the system being completed by human operators. This gives the flexibility for technical staff to work on running the data center and helping to expand capabilities. Edge computing and maintenance tasks require a separate set of tools, so critical assets are free from the task of operating the data center to focus on other priorities.
Edge computing is a large investment when you consider the technical requirements and human power to move everything over. While the proof of concept (POC) is there, it can be hard to manifest this setup into a physical deployment. Capital investment and the drive to have these long-term benefits are needed to scale the technology and overcome the temptation to fall back on traditional cloud computing. After initial distribution, the unknowns of installation and scalability are often resolved.
We mustn’t forget the various infrastructure teams that need to be merged for edge computing. While it is a clichéd picture, information technology (IT) and operational technology (OT) teams might have different points of view when it comes to installing a new system. It will be vital that IT teams are able to demonstrate understanding and offer a slow and steady approach to implementation, so that the concerns of OT teams are addressed, their questions are answered and needs are met.
As with any technology, the more it is implemented and used, the more innovation and feature investments emerge. With critical mass, we’ll see greater recognition of the benefits of edge computing while also creating a significant reduction in installation and transition costs. With inference engines and analytics taking place at the edge, we’ll see decreases in consumption cloud processing. This will all lead to a greater number of people having access to the technology.
Because operators can control physical access points, it’s believed that data stored on the premises (at the edge) will be more secure. Greater amounts of work will be completed more quickly, and edge computing could drive the adoption of cloud computing platforms.
There’s more to come for the future of data centers, IoT and edge computing technologies. As successful use cases emerge, more wide-scale deployments will take place, pushing the technology from initial buzz to proven results. As those case studies provide continued proof for the benefits of these models and as more is invested into making the technology successful, it will become less challenging for edge computing to be integrated into traditional data centers. Establishing a sufficient level of readability across major partner channels is already well underway, and this will start to become less of a custom deployment and more of a plug and play solution. Scalability will lead to a degree of commoditization, resulting in exponential growth.