Volcano

Volcano supports unified scheduling of online and offline workloads, provides a wealth of scheduling plugins and algorithms, and can distinguish different tenants through queue distinction. The current scheduling policy is a global configuration, and all jobs in the queue use the same scheduling policy, but in actual scenarios, different tenants may need to use different scheduling policies due to different usage scenarios. Therefore, volcano needs to support setting and using different scheduling policies at the queue level instead of using a globally unified scheduling policy. Expected Outcome: 1. A new field is added to the queue CRD, and users can set scheduling policies at the queue level. 2. Volcano scheduler implements different scheduling policies based on the queue in which the job is located.

Add more UT/E2E to cover more classical scenarios. Conduct complete stress testing and regression testing, Offer test report, give the improvement plan and put it into practice.

Volcano provides rich scheduling capabilities for AI workloads in the field of single cluster. In large model training scenarios, a single cluster cannot meet the computing power requirements of jobs, more and more users hope to submit jobs uniformly on multiple clusters for large model training, volcano needs to provide various scheduling capabilities, such as job management, gang scheduling, queue management, etc., and select the appropriate cluster for jobs to cope with the requirements of large model training. Expected Outcome: - Implement a basic multi-clusters scheduling framework integrated with multi-clusters scheduler like [Karmada](https://github.com/karmada-io/karmada) or other multi-cluster orchestration. - Implement gang scheduling, fair scheduling in multi-cluster. - Implement queue management in multi-cluster.

thena now has components such as router, ModelServing controller, etc. However, it lacks necessary e2e test coverage. - Expected Outcome: - 1.E2E Test of modelserving Router and modelBooster. - 2.Write an E2E test for each of the three on GitHub workflows. Run them separately. - 3.Developer Guide of How to run and expend E2E test

Volcano-global is a multi-cluster scheduling project designed for cross-cluster resource management. Currently, the project lacks a comprehensive end-to-end (e2e) test suite to ensure stability across complex multi-cluster environments. This project aims to build a reproducible e2e test framework using Ginkgo and Kind, ensure each feature of Volcano-global is covered by test cases. - Expected Outcome: - A functional e2e test framework integrated with GitHub Actions workflows. - Scripts for automated deployment of Volcano-global and bootstrapping of Karmada multi-cluster environments. - Test cases covering key scenarios: resource quota & priority, cross-cluster vcjob scheduling, data dependency aware scheduling, and hyperjob scheduling. - Comprehensive documentation, including test design docs and guidelines for extending e2e tests in the future.