KubeEdge

Users need to communicate between the cloud and the edge. For example, the cloud calls the rest interface of the edge service. In this case, the routing management function of KubeEdge can be used. Currently, routing management function of KubeEdge has some problems in the case of multiple CloudCore copies. The main problem is that when there are multiple copies of CloudCore, whether the cloud sends messages to the edge or reports the message to the cloud, it is not known which CloudCore is sent to it for processing, and there is confusion in message management in the cloud. In this project, we hope router manager can be optimized to support multi-CloudCore scenario. Expected Outcome: Support using router manager in multi-CloudCore scenario.

keink (KubeEdge IN kind) is a project for running local KubeEdge clusters using Docker container "nodes", so developers can install a multi-node edge cluster in one node. Now we need to support the latest version installation in keink. - Expected Outcome: keink can install the latest version of KubeEdge and developers can quickly use keink to run kubeedge, and then develop applications on KubeEdge.

We need to do some designs for adding more tests especially for the specific scenarios of edge computing, eg: - Application migration when the network is disconnected - System stability when the network is unstable - Run large-scale cluster tests periodically

<p>This project focuses on validating KubeEdge as an edge-native infrastructure for lightweight large language model inference. As more AI services move from the cloud to edge devices, lightweight LLMs provide a practical way to reduce latency, protect data privacy, and support local intelligence under limited resources.</p><p>The mentee will deploy one or more lightweight LLMs on KubeEdge edge nodes and evaluate the complete workflow, including model packaging, workload scheduling, service exposure, lifecycle management, and performance measurement.</p><p>The project is expected to produce a practical reference for running generative AI workloads on edge nodes managed by KubeEdge.</p><p><br></p><p>Expected Outcome:</p><p>&nbsp;- Successfully deploy and run one or more small-parameter large models on KubeEdge edge nodes, and complete end-to-end inference workflow validation.</p><p>&nbsp;- Explore deployment methods and best practices for managing lightweight model services based on KubeEdge.</p><p>&nbsp;- Evaluate basic performance on edge devices, including startup time, memory usage, inference latency, and runtime stability.</p><p>&nbsp;- Fix issues discovered during validation, and submit PRs to KubeEdge or related example repositories when necessary.</p><p>&nbsp;- Publish a blog or document to kubeedge/website introducing how to deploy and run small-parameter large models based on KubeEdge.</p><p>&nbsp;- Optional: complete example validation with a lightweight scenario, such as local Q&amp;A, document summarization, or lightweight multimodal inference.</p><p><br></p>

KubeEdge-Ianvs currently focuses on edge-cloud collaborative learning (training and inference) for a single modality of data. However, edge devices, such as those in autonomous vehicles, often capture multimodal data, including GPS, LIDAR, and Camera data. Single-modal learning can no longer meet the precise inference requirements of edge devices. Therefore, this project aims to integrate mainstream multimodal large model joint learning algorithms into KubeEdge-Ianvs edge-cloud collaborative learning, providing multimodal learning capabilities. Expected Outcome: A benchmark suite for multimodal large language models deployed at the edge using KubeEdge-Ianvs - Modify and adapt the existing edge-cloud data collection interface to meet the requirements of multimodal data collection - Implement a Multimodal Large Language Model (MLLM) benchmark suite based on Ianvs - Reproduce mainstream multimodal joint learning (training and inference) algorithms and integrate them into Ianvs single-task learning - (Advanced) Test the effectiveness of multimodal joint learning in at least one of Ianvs' advanced paradigms (lifelong learning, incremental learning, federated learning, etc.).