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LF Edge Cross Project Collaboration

DevOps MEC Infra Orchestration  Oleg Berzin

Public Cloud Edge Interface (PCEI) enables infrastructure orchestration and cloud native application deployment across public clouds (core and edge), edge clouds, interconnection providers and network operators. The notable innovations in PCEI are the integration of Terraform as a microservice to enable DevOps driven Infrastructure-as-Code provisioning of edge cloud resources (bare metal servers, operating systems, networking) public cloud IaaS/SaaS resources, private and public interconnection between edge cloud and public cloud, integration of Ansible as a microservice to enable automation of configuration of infrastructure resources (e.g., servers) and deployment of Kubernetes and its critical components (e.g., CNIs) on the edge cloud, and introduction of a workflow engine to manage the stages and parameter exchange for infrastructure orchestration and application deployment as part of a composable workflow. PCEI helps simplify the process of multi-domain infrastructure orchestration by enabling a uniform representation of diverse services, features, attributes, and APIs used in individual domains as resources and data in the code that can be written by developers and executed by the orchestrator, effectively making the infrastructure orchestration across multiple domains DevOps-driven. 

Show Case details:  khemendra kumar

Overview:

Demo of LFEdge Cross projects(Akraino EALTEdge + EdgeGallery + eKuiper + Fledge) collaboration to deliver Edge computing platform with IOT stack.

Background:

EALTEdge (Enterprise applications on lightweight 5G telco edge) BP from Akraino, integrate various open source projects to build a MEC based edge computing platform.

EALTEdge BP along with its upstream project EdgeGallery, provide an IOT stack which leverages Fledge(for IOT protocol and data collection) and eKuiper(Data Filter). 

For using the EALTEdge Edge computing platform and  experience the profile based IOT solution, EALTEdge is deployed in LFEdge Lab.

User with right permission to lab, can connect and try it.

Details about use case:

In this demo, we use a sample simulated IOT device.

Data from Device is processed in pipeline in multiple stages like data collection from devices, data filtration and transformation then store in DB for offline scenarios. 

Now IOT applications can access this data. It support http exporter to get data by application. 

Application like grafana can get data from DB as well.

In this Demo, we are using simulated MQTT device which produce readings every seconds and processed data is visualise in Grafana to monitor the device.

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