Edge computing will play a key enabling role in the fourth industrial revolution. The automotive industry and other manufacturing industries already have use cases that make them very likely to be early adopters of distributed cloud technology. Augmented Reality (AR) and Machine Learning (ML) will play a key role.
The automotive industry with connected vehicles is one of the fastest growing industries demanding distributed cloud. The creation and distribution of advanced maps with real-time data, and advanced driving assistance using cloud-based analytics of video streams are all examples of emerging services that require vehicles to be connected to the cloud. This will require networks that can facilitate the transfer of a large amount of data between vehicles and the cloud, often with real-time characteristics.
Market forecasts of connected vehicles indicates that the global number of connected vehicles will grow to approximately 700 million by 2025 and that the data volume transmitted between vehicles and the cloud will be around 100 petabytes per month according to Ericsson forecasts.
Other example are AR, where the components of the AR application could be executed either on the device itself, the edge server or in the central cloud, and machine learning for industrial applications.
Key requirements for industrial applications
Distributed cloud is a key component in 5G networks to be able to run industrial applications.
There are two main challenges to mobile network operators’ ability to deliver industrial applications with good quality to its customers. The first is the tough latency, reliability and security requirements of these new use cases. The second is figuring out how to shield the industries from the complexity of the infrastructure, to enable ease of use when programming and operating networks.
Some other requirements:
- Security and data privacy: In some cases, regulations or company policies stipulate that the data must not leave the enterprise premises. In other cases, industrial environments has multiple applications deployed and operated by different third parties.
- multi-tenancy for both the devices and the infrastructure.
- topology-aware cloud computing and storage
- open architecture, industry initiatives and standardization to enable the ecosystem
A distributed cloud is based on a localized network, distributed computing and data exposure:
- A localized network is a local network that covers a limited number of connected devices in a certain area, which splits the huge amount of data traffic into reasonable volumes per area.
- Edge computing refers to the geographical distribution of computation resources
- Data exposure secures integration of the data produced locally by utilizing the combination of the localized network and the distributed computation
The distributed cloud relies on efficient management and orchestration capabilities that enable automated application deployment in heterogeneous clouds supplied by multiple actors.
To be part of the globally distributed cloud, the edge clouds that CSPs provide at access and local sites must support a stringent set of functions and APIs. This implies that CSPs must join forces to create a federated model.
Distributed cloud provides edge computing and meets end-to-end network requirements as well as offering management, orchestration and exposure for the network and cloud resources together
Distributed cloud solution is based on software-defined networking, Network Functions Virtualization (NFV) and 3GPP edge computing technologies to enable multi-access and multi-cloud capabilities and unlock networks to provide an open platform for application innovations. In the management dimension, distributed cloud offers automated deployment in heterogeneous clouds.
Edge computing is the ability to provide execution resources (specifically compute and storage) with adequate connectivity at close proximity to the data sources.
An automotive use case
Learn more about how the automotive industry being connected will put new requirements on the networks and clouds to support their new emerging use cases, and how distributed cloud will improve the execution: