coflow scheduling frameworks

Coflow scheduling frameworks are software tools that manage the scheduling and allocation of resources for coflows, which are a type of data flow pattern in distributed computing. Some popular coflow scheduling frameworks include:

1. Apache Tez: An open-source framework that supports efficient execution of complex data processing workflows, including coflows.

2. Apache Flink: A distributed data processing system that supports coflow scheduling and management.

3. Apache Spark: A popular open-source framework that supports batch processing, stream processing, and machine learning workloads, and includes support for coflow scheduling.

4. TensorFlow: A popular open-source machine learning framework that includes support for distributed training of models using coflows.

5. Hadoop: A widely used distributed computing framework that supports coflow scheduling and management through the YARN resource manager.

6. Mesos: A distributed systems kernel that supports coflow scheduling and management through a variety of resource schedulers.

7. Kubernetes: A popular container orchestration system that includes support for coflow scheduling and management through its scheduling and resource management features.