Dubbo - Python integration & AI Traffic Management

The proposal aims to enhance Dubbo-python to meet the evolving needs of the AI sector by addressing compatibility issues, architectural limitations, and functionality gaps. Currently, Dubbo-python faces challenges due to its reliance on Python 2, deprecated libraries, and incomplete RPC communication capabilities, hindering its adoption within mainstream AI frameworks. To tackle these issues, the plan includes: 1. Upgrading Dubbo-python to support Python 3 and modernizing its architecture following the Microkernel + Plugin design principle. 2. Implementing complete RPC communication functionality, including server-side service exposure and client-side service invocation, with support for the new Triple protocol. 3. Developing service governance capabilities such as service discovery, registration, and load balancing, including the design of a GPU metric-based load balancing algorithm to address GPU workload imbalances in AI frameworks. 4. Writing comprehensive test codes to ensure the reliability of each functionality and providing detailed documentation to facilitate easier adoption and contribution to Dubbo-python. By accomplishing these objectives, the aim is to transform Dubbo-python into a robust, high-performance RPC framework compatible with mainstream AI frameworks, thus empowering developers to build scalable and efficient AI systems.