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As China's leading video platform, Bilibili faces 4 key challenges in multi-cluster AI workloads management: 1. Workload Diversity: Training/inference/video processing workloads have different scheduling requirements. 2. Cross-Cluster Complexity: Managing workloads across multiple Kubernetes clusters in expanding IDCs with SLAs. 3. Performance Demands: Minimal startup latency and best scheduling efficiency for short-running tasks e.g. video processing. 4. Efficiency-QoS Balance: maximizing resource utilization while ensuring priority workload stability.
This talk will share experiences and delve specific optimization techniques: 1. Leveraging and optimizing CNCF projects such as Karmada and Volcano to build a unified, high-performance AI workload scheduling platform. 2. Integrating technologies such as KubeRay to schedule various AI online and offline workloads. 3. Maximizing resource efficiency through online and offline hybrid scheduling, tidal scheduling and other technologies.
Technical Expert, Lead of Cloud Native Open Source, Huawei
Kevin Wang has been an outstanding contributor in the CNCF community since its beginning and is the leader of the cloud native open source team at Huawei. Kevin has contributed critical enhancements to Kubernetes, led the incubation of the KubeEdge, Volcano, Karmada projects in CNCF... Read More →
Long Xu is a Senior Software Engineer in the Infrastructure Department at Bilibili. He has rich experiences in the Kubernetes field, including scheduling, autoscaling and system stability.
