Fundamentally, edge computing makes processing and storage resources available in close proximity to edge devices or sensors, complementing centralized cloud resources and allowing for analytics close to those end devices. This results in a number of benefits that can be very relevant in an enterprise context, including: • (Near) real-time responsiveness Analytics that may have previously been undertaken in offsite cloud locations can potentially be supported locally, avoiding the need for raw data to be transferred to a cloud location and for results of any analyses to follow the same path back to a local device.
Accordingly, the time taken for a system to respond to new input information can be reduced to near real-time. • Improved device-to-device communications Communications and the exchange of data between devices that are collocated together can be routed more directly, and without need to transit cloud infrastructure. In fact, edge intelligence can potentially allow processing resources to be shared between a number of local devices, with certain devices able to call on processing resources residing in other nearby devices in a seamless way. • Improved robustness, resilience and reliability With more analytics undertaken locally to data sources, systems are not as susceptible to disruption in the case that a connection to a remote cloud location fails.
Effectively, edge computing can allow local devices to operate to some extent autonomously of any cloud infrastructure. In some situations, edge devices can operate almost completely autonomously and independently of any connection to cloud infrastructure.
• Improved security and data protection
With more data processed locally, many security and privacy issues associated with transmitting data to cloud locations can potentially be mitigated, and it can be easier for enterprises to demonstrate compliance with data privacy and data sovereignty requirements. Alternatively, edge computing can be used to anonymize data locally before onward transmission to cloud infrastructure.
• Regulatory compliance
Locally managed information potentially only needs to comply with local regulations, rather than multijurisdictional regulations that might apply in a cloud environment.
• Reduced operating costs
Undertaking more analytics locally, supported by edge computing, can reduce the amount of data that needs to be sent to cloud locations for processing, so reducing communications costs associated with data carriage. It also reduces the burden of processing that must be supported by cloud infrastructure and more importantly the amount of data that needs to be stored in the cloud, reducing costs for cloud infrastructure. Edge locations, data lakes and data streams Thus far, we’ve referred to ‘the edge’ in quite general terms as being characterized by deploying compute power closer to edge devices or sensors.