Best Ways To Improve Data Processing at the Edge
Implementing edge computing best practices helps optimize edge environments. Here are six best practices for improving data processing at the edge.
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Edge computing provides an answer to several of the traditional cloud’s shortcomings. Data generation will only continue growing, and data processing operations need the edge’s lower latencies, scalability, and resilience. However, these advantages won’t come without effort.
Creating an edge environment in and of itself won’t deliver on the loftiest promises of this technology. These are complex networks, and as such, require careful planning for organizations to make the most of them.
With that in mind, here are some best practices for improving data processing at the edge.
Move Data Processing Closer to End-Users
The first step to optimizing edge data processing is also the most straightforward. Organizations can make the most of their edge environments by leaning into one of the edge’s biggest benefits: processing data closer to its end-use.
Just because data isn’t a physical object doesn’t mean the laws of physics don’t apply. Fiber-optic technologies can transmit information at two-thirds the speed of light, but network congestion and low latencies will hinder that, especially across long distances. If organizations shorten the distance this data must travel, they can process it faster.
The edge devices closest to the point of data collection should perform the bulk of the computation. Organizations should consider this when planning the physical placement of data centers and products. Not every process can happen right next to the data it uses, but it should be as close as possible.
Simplify Computation Distribution
Another factor to consider with edge computing is how networks distribute computation across devices. Splitting up workloads between these items and micro data centers helps account for each hub’s limited resources, but it’s easy to overcomplicate networks in doing so. The more distributed computation is, the more complex the system becomes, introducing vulnerabilities.
Visibility is already difficult to achieve in edge environments. However, considering the average data breach in 2019 took 206 days to detect, organizations should strive to maximize network transparency. Simplifying computation distribution will help in that endeavor.
This strategy goes hand-in-hand with moving data processing tasks closer to their end-use. Less distribution means less distance to cover, helping reduce latency while improving network visibility. Organizations don’t have to avoid computation distribution altogether, but it should remain as simple as possible.
Distribute Workloads According to End-Use
One of the best ways teams can reduce complexity and optimize data processing locations is by distributing according to end-use. Every workflow has different immediacy needs, and edge computing environments should account for these varying requirements. Businesses can do this by keeping data’s end-use in mind when assigning it a processing location.
Take predictive maintenance, for example. Given the high cost of machine breakdowns, machine health analysis should happen as quickly as possible to alert workers to problems in real-time. As a result, this analysis should occur on or near the sensors that gather this equipment information.
Other operations, like long-term analysis or machine learning algorithms, may require less immediacy but more diverse data. Consequently, sending relevant information to a centralized point away from the devices themselves makes more sense for analysis.
Make Sure Edge Data Is Secure
If edge data isn’t secure, companies can’t expect to process it effectively. Considering how edge computing places critical processing activities on IoT devices, that can be a challenge. Since there were 1.5 billion IoT breaches in the first half of 2021 alone, that doesn’t inspire much confidence.
Some of the above steps, like simplifying edge environments and moving data closer to end-users, will help improve security. Teams should also consider how they encrypt information. Encrypting it both at rest and in transit is essential. Confidential computing can take it a step further and encrypt it during data processing, providing maximum security.
Automated monitoring solutions are another helpful step, as edge environments are too complex for manual oversight to be sufficient. Similarly, zero-trust architecture can help secure these complex networks without massive human teams.
Embrace SASE Architecture
One step that improves security and data processing performance is implementing a secure access service edge (SASE). Running an edge computing environment involves a lot of dynamic needs, so teams need the right architecture to manage it. SASE combines SD-WAN functions with cloud security tools in a single SaaS model to provide that.
SASE provides a single window to manage a cloud environment, simplifying otherwise overcomplicated edge networks. Gaining these services through a SaaS model also reduces infrastructure requirements, which may already be high with edge computing.
One caveat is that SASE is a relatively new technology, with less than 1% of enterprises having an implementation strategy in 2018. However, it’s rapidly growing and becoming more accessible as more people realize how challenging edge management can be.
Capitalize on Containerization
As these other steps highlight, many edge data processing best practices boil down to simplifying and streamlining these systems. One helpful way to do that on the software side of things is to rely on containerization. Containers are an excellent way to apply standardization to an otherwise eclectic environment.
Developers should be able to use the same tool across all disparate devices and applications on the edge. Containers provide that consistency. This will help with scalability, too, as it removes the requirement for specialized skills or tools to create edge applications.
Organizations will shift to the edge gradually, so they should have tools that work across all environments. Developers can do that by using containers, making the transition from the traditional cloud to the edge smoother.
Thoughtful Edge Computing Optimizes Data Processing
Edge computing could revolutionize many data processing operations, but not on its own. It will take careful planning and implementation to create suitable edge environments for an organization’s processing needs. Without this forethought, users will struggle to attain the technology’s most enticing promises.
These six best practices should help businesses create an edge network they can manage and implement effectively. They can then experience the full benefits of data processing, taking their operations into the future.
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