Edge computing addresses infrastructure challenges such as network reliability, latency, congestion, or bandwidth limitations, but many additional benefits make it more appealing.
Fast processing and results are key to some businesses, such as autonomous vehicles, high-frequency trading, or the healthcare industry, where minor delays could result in expensive consequences.
Even with the latest network technology, such as fiber optic, which allows data to travel at 2/3 the speed of light, enormous data volumes lead to traffic congestion and latency.
Edge computing increases network performance by reducing latency as data is not required to travel the long distances inherent to a traditional client-server architecture.
Since minimal latency or downtime may cost thousands of dollars, gains in speed with edge computing are impactful.
With the availability of IoT edge computing devices and edge data centers closer to data generation, dependency on continuously strong networks and bandwidth has been dramatically reduced.
Reliability is improved, especially where internet connectivity or bandwidth is limited or unreliable, for example, on oil rigs, ships, or in rainforests and deserts.
With a great network of edge computing devices and edge data centers, there is no single point of failure, negating the chances of complete service failure.
As a result, reliability is improved, especially when internet connectivity or bandwidth is limited or unreliable, for example, on oil rigs, ships, rainforests, and deserts.
Many different compliance laws across the world apply to data security and sovereignty. Transmitting data across regions, countries, and continents may fall under different legalities, which could be an additional problem than transmitting large volumes of data across WAN and LAN.
One such example is the European Union's GDPR, which defines the guidelines for data storage, processing and exposing the data, etc.
By leveraging edge computing architecture, the data can be processed locally, and any sensitive information could be filtered out before transmission, complying with local laws.
Transmitting, processing, storing, managing, and securing large volumes of data is not cheap.
Data generated by IoT devices may not be critical to the operation. They can be filtered out at/or near the source before moving to centralized data centers, resulting in excellent cost cutting on bandwidth use.
In this way, edge computing helps to optimize the data flow, reduce redundancy costs, and maximize the organization's profit.
Despite the increased attack network surface (because of the number of IoT edge devices), edge computing provides some important security advantages.
Because of their “centralized processing” nature, traditional architectures have been vulnerable to DDoS (Distributed Denial of Service) attacks and power outages.
However, edge computing minimizes the chances of disruption and network outage by distributing computation, storage, and applications across a wide range of data centers and edge devices.
An IoT edge device could also become an entry point for an attack. Still, it can easily be identified and secured by implementing security measures in the affected area without taking down the entire network.
Since a lower volume of filtered data is transmitted to the centralized server because of local data storage and processing, the risk of data interruption reduces greatly.
Even if a device is compromised, only data available to that device is exposed for attack rather than the entire centralized server.