coflow scheduling frameworks
时间: 2023-10-01 17:07:07 浏览: 39
Coflow scheduling frameworks are used in distributed computing environments to optimize the transmission of data between different data centers, clusters, or nodes. Some of the popular coflow scheduling frameworks are:
1. Apache Tez: Tez is a popular open-source framework that provides a generalized dataflow framework to process large-scale data in Hadoop. It supports the execution of complex DAGs and can optimize the processing of coflows.
2. Apache Spark: Spark is another popular open-source framework that supports distributed data processing. It has a built-in mechanism to optimize data transfer between tasks and nodes, which makes it suitable for processing coflows.
3. Apache Flink: Flink is a distributed data processing framework that supports stream and batch processing. It provides a powerful mechanism to optimize data transfer between tasks and nodes, which makes it ideal for processing coflows.
4. Google’s TensorFlow: TensorFlow is a popular deep learning framework that supports distributed training. It has a built-in mechanism to optimize data transfer between nodes, which makes it ideal for processing coflows.
5. CoflowKit: CoflowKit is a coflow scheduling framework that provides a simple API to schedule coflows. It supports both centralized and decentralized scheduling and can optimize the transmission of data between nodes.
6. Dhalion: Dhalion is a coflow scheduling framework that provides a distributed scheduling algorithm to optimize the transmission of data between nodes. It supports both batch and stream processing and can handle failures and load balancing.