Efficiency: Designing and implementing software platforms and monitoring frameworks for efficient, automated, and intelligent service-oriented architecture (SOA) governance.
Efficiency: Designing and implementing software platforms and monitoring frameworks for efficient, automated, and intelligent service-oriented architecture (SOA) governance.
Efficiency: Designing and implementing software platforms and monitoring frameworks for efficient, automated, and intelligent service-oriented architecture (SOA) governance.
Efficiency: Designing and implementing software platforms and monitoring frameworks for efficient, automated, and intelligent service-oriented architecture (SOA) governance.
Design and implement workload scheduling across multiple data centers, regions, and cloud environments, ensuring balanced resource utilization and efficient placement of both online services and offline batch jobs.
Design and implement workload scheduling across multiple data centers, regions, and cloud environments, ensuring balanced resource utilization and efficient placement of both online services and offline batch jobs.
Design and implement workload scheduling across multiple data centers, regions, and cloud environments, ensuring balanced resource utilization and efficient placement of both online services and offline batch jobs.
Design and implement workload scheduling across multiple data centers, regions, and cloud environments, ensuring balanced resource utilization and efficient placement of both online services and offline batch jobs.
Design and implement workload scheduling across multiple data centers, regions, and cloud environments, ensuring balanced resource utilization and efficient placement of both online services and offline batch jobs.
For inference track, develop the Volcano Ark MaaS (Model-as-a-Service) inference platform, optimizing the performance, cost efficiency, and reliability of large language model inference across hyperscale heterogeneous GPU clusters.
For inference track, develop the Volcano Ark MaaS (Model-as-a-Service) inference platform, optimizing the performance, cost efficiency, and reliability of large language model inference across hyperscale heterogeneous GPU clusters.