The adoption of IoT is increasing rapidly. By 2021, it is expected that 35 billion IoT devices will be installed worldwide. While there is an opportunity for businesses to utilise all the benefits of IoT however, many traditional data centres don’t have the bandwidth to handle the large volumes of data collected by IoT devices. Furthermore, IoT applications require quality features such as low latency with high scalability, reliability and availability. To accommodate this, IoT deployments will need to operate within network infrastructures that have the capacity to deliver such demands.
As the use of IoT escalates, we see two universal requests from internet users across the globe – more and faster. As organisations from a wide range of industries clamoured for this, the edge was born.
So, what exactly is the edge? Traditional data centres are located at the middle of a network. Now, significant processing can be completed at smaller local sites which are effectively at the edge of the network. As the amount of data being used across IoT continues to grow, organisations are processing data closer to where it’s generated – the network’s edge.
Edge computing is all about location. IoT devices transfer data to a local connection point. Once the data has been processed on the edge, a portion or all of that information is then sent to a data repository located within the cloud, at a co-location facility or corporate data centre. For over a decade, the standard IT delivery platform has been considered centralised cloud computing. However, with new applications, workload services are demanding an architecture built to support a distributed infrastructure – enter the age of edge computing, and its four key user benefits: security, speed, cost and scalability.
With the number of connected devices across the IoT growing all the time, the risk of cyber-attacks grows in tandem. Yet, while cloud computing applications can be especially vulnerable to DDoS attacks and failures on account of the centralised structure used, edge computing is often more secure. That is because it distributes processes and applications across different devices, making it much more difficult for attackers to infiltrate the network. Moreover, sensitive data can be separated from the main data flow and transferred through a more secure connection (e.g. MPLS), further enhancing security. Consequently, less data can be intercepted, and security standards compliance becomes easier.
The relevance of data declines over time, so speed of data processing is critically important. On the one hand, while the boom in IOT devices and data collected at the edge means overall data volumes will increase, edge computing helps restrict data transmitted by only sending data that’s required centrally back to the data centre. That helps to protect the performance of services and applications. On the other hand, edge computing can speed up applications by reducing network latency. For example, autonomous vehicles are fitted with multiple monitoring devices which constantly collect data. Yet, there is no affordability for long lag times when processing this data, given the need for fast reaction/ decision making. As the smart city increasingly moves towards autonomous vehicles, edge computing allows this to be a realistic option. Large amounts of data being transmitted can cause everything to slow down. In this context edge computing improves latency by processing the data closer to where it is being used, providing a faster and better experience for users.
An edge computing approach allows organisations to scale their capacity needs efficiently and at any time by combining edge data centres and IoT devices. The use of edge devices optimises the scaling costs because every extra device added is associated with a far lower bandwidth requirement for the network.
However, as any network scales at the edge, it needs to have network resilience and edge infrastructure management built in to ensure it doesn’t fail and that any outages are not sustained. Network redundancy is one option to prevent a total shutdown but it is expensive and it does not provide the necessary tools to fix a network. A scalable Smart Out-of-Band Management (OOB) solution can provide secondary, dedicated access to network devices separate to the primary network that the system operates on. In this context, Smart OOB removes the need for expensive site visits to remediate the network, and minimises the likelihood of a reduction in services.
With edge computing, the data can be filtered at the point of origin and does not have to be sent to a data centre. Organisations consequently have the choice of using a blend of local services and cloud-based applications in order to build a cost-effective IoT solution. Costs can be further lessened as the amount of data that is needed to be copied, processed and analysed from one system to another is greatly reduced – which in turn supports lower bandwidth costs.
Only the best
Edge computing is indispensable for many IoT scenarios, because with the optimal use of the data, the added value needed to create decisive competitive advantages can effectively be achieved. It is consequently all the more important to use the best, smartest solutions for the implementation. Businesses will need to put reliable and robust wide area networks (WANs) in place. But they will also need to back their approach up with a manageable remote Smart OOB solution that simplifies dealing with a dispersed network and provides true network resilience, including the ability to detect issues sooner and remotely resolve and remediate them faster when issues occur.