Edge computing and cloud computing are sometimes discussed as if they’re mutually exclusive approaches to network infrastructure. While they may function in different ways, utilizing one does not preclude the use of the other. In practice, they actually complement one another quite effectively.
As Internet of Things (IoT) devices become more common and incorporate more processing power, vast amounts of data is being generated on the outer “edge” of computing networks. Traditionally, the data produced by IoT devices is relayed back to a central network server, usually housed in a data center. Once that data is processed, further instructions are sent back to the devices out on the edge of the network.
There are two problems with this setup. First, it takes time for data to travel from the edge device back to the center for processing. This delay might only be a matter of milliseconds, but it can be critical. Secondly, all that data traveling back and forth between the edge and the center of the network puts tremendous strain on bandwidth. This combination of distance and high volume traffic can slow the network down to a crawl.
Network latency can have serious consequences for IoT devices. Take, for example, self-driving cars. Autonomous vehicles gather a tremendous amount of data from their surroundings and from other devices nearby. If the vehicle’s reaction time is dependent upon instructions from the computing resources at the core of the network, the slightest delay could literally be a matter of life and death.
Edge computing offers a solution to the latency problem by relocating crucial data processing to the edge of the network. Rather than constantly delivering data back to a central server, edge enabled devices can gather and process data in real time, allowing them to respond faster and more effectively. When used in tandem with edge data centers, edge computing is a versatile approach to network infrastructure that takes advantage of the abundant processing power afforded by modern IoT devices.
In a cloud computing architecture, all data is gathered and processed in a centralized location, usually in a data center. All devices that need to access this data or use applications associated with it must first connect to the cloud. Since everything is centralized, the cloud is generally easy to secure and control while still allowing for reliable remote access.
Public cloud services such as Microsoft Azure, Amazon Web Services, and Google Cloud provide tremendous benefits for organizations that use a traditional client/server network. By storing assets and information in a centralized cloud, they ensure that authorized users can access the information and tools they need from anywhere at any time.
As already discussed, the centralized nature of cloud computing makes it difficult to process data gathered from the edge of the network quickly and effectively. What the cloud lacks in speed, however, it makes up for in power and capacity. Since cloud computing is based upon a scalable data center infrastructure, it can expand its storage and processing capacity as needed.
One major limitation of edge devices is that they only accumulate locally collected data, making it difficult for them to utilize any kind of “big data” analytics. Cloud computing allows for a level of large scale data analysis that simply isn’t possible at the edge of the network. With its unparalleled storage and processing potential, the cloud can gather massive amounts of data and analyze it in a variety of ways to produce valuable insights, trends, and solutions.
Cloud computing will continue to be a valuable resource for more traditional data center infrastructures. While IoT devices represent an exciting new frontier in the tech industry, not all businesses will see much benefit from moving their assets to the edge of the network. Software as a Service providers, for instance, can provide better service and security by hosting their products in a centralized cloud.
Worlds Fortunately, choosing to emphasize edge or cloud computing isn’t an “either/or” proposition. As IoT devices become more widespread and powerful, organizations will need to implement effective edge computing architectures to leverage the potential of this technology. By incorporating edge computing with centralized cloud computing (a network infrastructure sometimes called fog computing), companies can maximize the potential of both approaches while minimizing their limitations.
Considering that IoT devices are expected to generate a staggering 1.6 zettabytes of data by 2020, storage concerns aren’t likely to disappear anytime soon. In most edge computing architectures, much of that data is eventually deleted since the devices collecting it don’t have enough storage to spare. Even if they could store it all, accessing and securing it would be a logistical nightmare. By combining the data-gathering potential of edge computing with the storage capacity and processing power of the cloud, companies can keep their IoT devices running fast and efficient without sacrificing valuable analytical data that could help them to improve services and drive innovation.
The future of network infrastructure is unlikely to be found solely on the edge or in the cloud, but rather somewhere in between the two. As companies become more effective at incorporating these two models, they will surely find new ways to get the most out of their respective advantages and use them to overcome their weaknesses.
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