Edge computing is a distributed computing model in which computing takes place near the physical location where data is being collected and analysed, rather than on a centralised datacentre or in the cloud. This new infrastructure involves sensors to collect data and edge servers to securely process data in real-time on site, while also connecting other devices, such as laptops and smartphones, to the network.

The fragmentation and decentralization of these smaller datacentres could mean there are thousands of them to manage and the problem becomes how to connect them together in order to deliver a service that meets customer expectation. Unfortunately, the complexity doesn’t stop there. The services themselves are made up of lots of pieces of software which are deployed in multiple locations. All related to each other, these service pieces need to be able to talk to each other in order to function as a complete service.

The challenges

The networking that is required to connect and set up these elements and move workloads and services across them in an intelligent manner, in order to optimize the service experience, is becoming very complex and dynamic. Manual programming and relying on traditional tools is simply not possible due to the complexity and dynamic nature of Edge Cloud Networking. A new set of tools is required that fully automates the entire lifecycle of these Edge Cloud Networks.


Network Services can now run on a variety of Clouds in different locations, not just a central location with a mapped architecture and a known capacity. Understanding these connections and dependencies in real-time is essential for the life of the service.


Connectivity, data migration, bandwidth and latency features of cloud computing are expensive. Traditional tools based on pre-built and static workflow models are not sufficient to cope with the changes and dynamic environment. Managing different layers of connectivity in an ever-changing environment means that lifecycle management needs to be fully automated.


The shape of each network service can change. Capacity, changing customer patterns, time, failures, shared resources and services, and other environmental factors can all effect workload. This requires tools that can dynamically connect and manage an optimised service chain.

How can Stratoss Help?

Stratoss™ offers a set of software automation tools which orchestrate the cloud network service layers, to deliver an optimal service. Our Intent Engine dynamically establishes and manages all connectivity requirements without the need for manual programming. When changes occur, for example, due to new edge locations or workload placements, Stratoss automatically calculates and executes the best path to implement the required network changes from the current state to the intended state.

This allows the network to be dynamic and to automate itself with no highly skilled human intervention. Behaviour Driven Testing tools combined with the CI/CD capabilities ensure that cloud network services can be rapidly designed and automatically tested. This further reduces manual effort and significantly lowers the amount of errors of services in production

IT and networking layers must be co‑ordinated but managed separately.

  • Enterprise Edge Cloud IT and Networking workloads must co‑operate across independent layers and connected locations to deliver an end to end service 
  • Each layer is designed and managed independently of the others delivering a service to the layer above.

The Benefits

  • Reduced complexity
  • Operational automation
  • Reduced operational errors
  • Reduced cost of operations