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Going beyond edge computing

with distributed cloud

Edge computing with distributed cloud

With an increasing interest in new use cases such as smart manufacturing, augmented reality and a multitude of IoT applications, there is a need for an infrastructure with edge computing and distributed cloud capabilities.

Edge computing and distributed cloud

Edge computing provides compute and storage resources with adequate connectivity (networking) close to the devices generating traffic. The benefit is the ability to provide new services with high requirements on e.g. latency or on local break-out possibilities to save bandwidth in the network – data should not have to travel far in the network to reach the server. Regulatory compliance and network scalability are also important edge computing drivers.

Many use-cases for IoT and 5G span the device, access-, distributed-, national- and global sites. For example, an augmented reality solution comprises a client on a device, a component supporting video processing, a CDN/caching function at a distributed site and a backend at a national- or global site. This requires a solution that can handle any workload, anywhere in the network, with end to end orchestration. Distributed cloud is doing this - managing different types of sites where the location of the edge depends on the use case.

Edge computing orchestration and intelligent placement of workloads

Distributed cloud goes along with automated deployment of applications at just the right location in the network to optimize resource efficiency and user experience. For that reason, orchestration is a key capability providing end to end management of networking, cloud infrastructure and workload placement. The dynamic orchestration simplifies the complexity of the network enabling intelligent placement of workloads to everyone. The intelligent placement is policy driven and based on criteria such as latency, geolocation, and throughput. Going forward, finding the optimal placement in the network will typically be enhanced by using artificial intelligence and machine learning.

Implementing Edge Computing

Edge computing and 5G

With increasing interest in new use cases like smart manufacturing and AR/VR, as well as upcoming 5G radio networks, there is a clear need for distributed networks. Download our paper on Edge Computing and 5G to learn more.

Paper - Edge Computing and 5G

Webinar: Edge computing value generation

How Distributed Cloud and 5G help speed up edge application development and generate value for communication service providers.

Register here

Edge computing use cases

There are several interesting use cases related to manufacturing, automotive, and the media and entertainment industries for edge computing and distributed cloud. As an example - the automotive industry with connected vehicles is an industry demanding edge computing. 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.

Read about edge for automotive industry

Take a deep dive into edge computing architecture

Distributed cloud is a cloud execution environment that is geographically distributed across multiple sites, including required connectivity in between, managed as one entity and perceived as such by applications.

The key characteristic of distributed cloud is abstraction of cloud infrastructure resources, where the complexity of resource allocation is hidden to an application.

Distributed cloud is based on SDN, 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.

To learn about the top 5 pitfalls in edge deployments, download the Edge Computing research brief.

Read the research brief
Edge computing in use

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