Edge computing is the next big thing to hit IT, and component virtualization plays a crucial role in the architecture that will leverage all the benefits it promises.
By 2022, spending on edge computing is predicted to grow 30% from 2018 levels, mostly due to the increasing data flows that may render traditional centralized infrastructure nonoptimal and thus obsolete.
Edge computing is indeed going to transform data centers as we know them, mostly due to the gargantuan increase in the amount of data they process. Spreading computing power out over an ecosystem of devices may optimize data center performance. This will be done by building local autonomy instead of transferring all the data to and from data centers. According to research carried out by Juniper, the number of connected devices will triple from 2018 to 2021, resulting in over 46 billion units online. Processing all the data coursing through traditional infrastructure will be virtually impossible.
That’s why edge computing is on the rise.
What is edge computing?
At the center of edge computing is the notion of processing data as close to its source as possible. As the volumes of data to process rise, the trend takes off right alongside them. Processing or preprocessing data requires devices to be packed with the technology that makes data transfer unnecessary, or at least reduces the need. Heavy data processing is moved out to the edges of the system.
Axis Communications is a surveillance system vendor, famous for high-tech IP cameras. When it comes to designing an IP-based surveillance system, the need for storage is among the more serious obstacles. After all, – cameras gather high-quality videos that usually register nothing significant – a darkness-covered warehouse or an empty parking lot. On the other hand, when something interesting occurs, higher quality video enables the company to gather better information about the event.
By transferring compression and video recognition capabilities to the camera instead of processing the images in the cloud, the company reduces the data it must transfer by up to 50%.
Enabling the camera to perform heavy computations is just one possible edge computing application. But definition of edge computing vary by industry.
Micro data centers are on the rise
Micro data centers are used wherever access to a full-scale database is limited, yet there is a great need for computing power. They can be installed in remote facilities, in the factories to power up the IoT devices or in any location where putting up a traditional data center would be impossible and moving computations into the cloud would be costly.
Micro data centers are even more reliant on SDN and NFV technologies than traditional data centers, as they need to be space-effective. Given this, virtualization is one of the best ways (and clearly the most convenient one) to run all the necessary devices (router, load-balancer, firewall etc.) inside the data center.
Due to their versatility, micro data centers are predicted to grow exponentially from 2024.
Software Defined Networking as an edge solution
As a more uncommon understanding of edge computing, SDN and NFV techniques may be leveraged within the data center to optimize its base performance.
Cloud computing tends to be seen as infinite computing power that sits a mere arm’s length away. And from the client’s point of view, it is indeed that. But the provider or operator sees the other side, and is burdened with the need to provide computing power by utilizing hardware to the greatest extent possible. Empowering the cloud with SDN and NFV at the hardware level leads to improved asset management and a reduced need for computing power for operations.
Considering that, leading hardware providers deliver SDN-enabled edge devices like routers that can be reprogrammed while their internal operations are redesigned with SDN and NFV techniques. The final goal is to extend the cloud beyond the data center and stop thinking about the network of devices as a sum of separate machines–but rather much more. We need seamless and secure connectivity to the cloud, so we can easily migrate and manage workloads. Edge can be treated as an extension of the Cloud.
Conclusion – the many faces of edge computing
In the most common understanding, edge computing is moving computations closer to the data source and the edges of the system. But sometimes it is hard to determine where the edge actually is. One idea is to place computing closer to the chip instead of building additional layers of abstraction. Is that edge,or is it something entirely new?
For some, the edge is a distant base station. For others, a camera or an industrial machine, while in databases and cloud providers it may be at the processor’s fringe. SDN and NFV are the best tools available for managing the edge of the network, optimal when considering performance and the cost of a solution, even if the network of devices and the computing power is scattered through the world.
Take a look how CodiLime brings the power of virtualization to your network.