Abstract
The volume of information that needs to be processed in big data clusters increases rapidly nowadays. It is critical to execute the data analysis in a time-efficient manner. However, simply adding more computation resources may not speed up the data analysis significantly. The data analysis jobs usually consist of multiple stages which are organized as a directed acyclic graph (DAG). The precedence relationships between stages cause scheduling challenges. General DAG scheduling is a well-known NP-hard problem. Moreover, we observe that in some parallel computing frameworks such as Spark, the execution of a stage in DAG contains multiple phases that use different resources. We notice that carefully arranging the execution of those resources in pipeline can reduce their idle time and improve the average resource utilization. Therefore, we propose a resource pipeline scheme with the objective of minimizing the job makespan. For perfectly parallel stages, we propose a contention-free scheduler with detailed theoretical analysis. Moreover, we extend the contention-free scheduler for three-phase stages, considering the computation phase of some stages can be partitioned. Additionally, we are aware that job stages in real-world applications are usually not perfectly parallel. We need to frequently adjust the parallelism levels during the DAG execution. Considering reinforcement learning (RL) techniques can adjust the scheduling policy on the fly, we investigate a scheduler based on RL for online arrival jobs. The RL-based scheduler can adjust the resource contention adaptively. We evaluate both contention-free and RL-based schedulers on a Spark cluster. In the evaluation, a real-world cluster trace dataset is used to simulate different DAG styles. Evaluation results show that our pipelined scheme can significantly improve CPU and network utilization.
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