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All of these characteristics pose unique challenges while developing emerging 5G/AI applications at the network edge. Supporting such an extensive feature-set at the required flexibility, dynamicity, performance, and efficiency requires careful and expensive engineering effort and needs adoption of new ways of architecting the enabler enabling technology landscape.

To this regards, our proposed “Federated Multi-Access Edge Cloud Platform” blueprint enables desired abstractions in order to address these challenges and, as a result, ushers in an application development environment that enables support for ease of development and deployment of these emerging applications landscape. Subsequent sections delve deep into the proposed “Federated Multi-Access Edge Cloud Platform” blueprint details.

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Karmada (Kubernetes® Armada) is a Kubernetes®  management system that enables cloud-native applications to run across multiple Kubernetes® clusters and clouds with no changes to the underlying applications. By using Kubernetes®-native APIs and providing advanced scheduling capabilities, Karmada truly enables multi-cloud Kubernetes® environment. It aims to provide turnkey automation for multi-cluster application management in multi-cloud and hybrid cloud scenarios with key features such as centralized multi-cloud management, high availability, failure recovery, and traffic scheduling. More details related to Karmada project can be found here.

EdgeMesh (Included in Release-5)

EdgeMesh provides support for service mesh capabilities for the edge clouds in support of microservice communication cross cloud and edges. EdgeMesh provides a simple network solution for the inter-communications between services at edge scenarios (east-west communication).

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The network topology for edge cloud computing scenario is quite complex. Various Edge nodes are often not interconnected and the direct inter-communication of traffic between applications on these edge nodes is highly desirable requirement for businesses. EdgeMesh addresses these challenges by shielding the complex network topology at the edge applications scenario. More details related to EdgeMesh project can be found here.

Service Discovery (Not included in Release-5)

Service Discovery retrieves the endpoint address of the edge cloud service instance depending on the UE location, network conditions, signal strength, delay, App QoS requirements etc.

Mobility Management (Not included in Release-5)

Cloud Core side mobility service subscribes to UE location tracking events or resource rebalancing scenario. Upon UE mobility or resource rebalancing scenario, mobility service uses Cloud core side Service Discovery service interface to retrieve the address of new appropriate location-aware edge node. Cloud Core side mobility service subsequently initiates UE application state migration process between edge nodes. Simple CRIU container migration strategy may not be enough, it is much more complex than typical VM migration.

Multi-Access Gateway (Not included in Release-5)

Multi access gateway controller manages Edge Data Gateway and Access APIG of edge nodes. Edge data gateway connects with edge gateway (UPF) of 5G network system, and routes traffic to containers on edge nodes. Access APIG connects with the management plane of 5G network system (such as CAPIF), and pulls QoS, RNIS, location and other capabilities into the edge platform.

AutoScaling (Not included in Release-5)

Autoscaling provides capability to automatically scale the number of Pods (workloads) based on observed CPU utilization (or on some other application-provided metrics). Autoscaler also provides vertical Pod autoscaling capability by adjusting a container’s ”CPU limits” and ”memory limits” in accordance to the autoscaling policies.

Service Catalog (Not included in Release-5)

Service Catalog provides a way to list, provision, and bind with services without needing detailed knowledge about how those services are created or managed.

Detail Flow of various Architectural Components

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What is included in Release-5

As mentioned earlier that the purpose of this blueprint is an end-to-end technology solution for mobile game deployed across multiple heterogeneous edge nodes using various network access protocols such as mobile and WiFi and others. This blueprint demonstrates how an application leverages a distributed and multi access network edge environment for realizing all the benefits of edge computing.

This is the very first release of this new blueprint as part of the Akraino PCEI family. Current focus for this release is to enable only the following two key architectural components:

1. Open source Karmada based Cloud Federation
2. EdgeMesh functionality

This blueprint will evolve as we incorporate remaining architectural components as part of the subsequent Akraino releases. More information on this blueprint can be found here.

Acknowledgements

Project Technical Lead: Deepak Vij, KubeEdge MEC-SIG member - Principal Cloud Technology Strategist at Futurewei Cloud Lab.

Contributors:

• Peng Du, Futurewei Cloud Lab.
• Hao Xu, Futurewei Cloud Lab.
• Qi Fei, KubeEdge MEC-SIG member - Huawei Technologies Co., Ltd.
• Xue Bai, KubeEdge MEC-SIG member - Huawei Technologies Co., Ltd.
• Gao Chen, KubeEdge MEC-SIG member - China Unicom Research Institute
• Jiawei Zhang, KubeEdge MEC-SIG member - Shanghai Jiao Tong University
• Ruolin Xing, KubeEdge MEC-SIG member - State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications
• Shangguang Wang, KubeEdge MEC-SIG member - State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications
• Ao Zhou, KubeEdge MEC-SIG member - State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications
• Jiahong Ning, KubeEdge MEC-SIG member - Southeastern University
• Tina Tsou, Arm