Parallel Scheduling of Data-Intensive Tasks

Workloads with precedence constraints due to data dependencies are common in various applications. These workloads can be represented as directed acyclic graphs (DAG), and are often data-intensive, meaning that data loading cost is the dominant factor and thus cache misses should be minimized We address the problem of parallel scheduling of a DAG of data-intensive tasks to minimize makespan. To do so, we propose greedy online scheduling algorithms that take load balancing, data dependencies, and data locality into account. Simulations and an experimental evaluation using an Apache Spark cluster demonstrate the advantages of our solutions.