Directed acyclic graph (DAG)-aware task scheduling algorithms have been studied extensively in recent years, and these algorithms have achieved significant performance improvements in data-parallel analytic platforms. However, current DAG-aware task scheduling algorithms, among which HEFT and GRAPHENE are notable, pay little attention to the cache management policy, which plays a vital role in in-memory data-parallel systems such as Spark. Cache management policies that are designed for Spark exhibit poor performance in DAG-aware task-scheduling algorithms, which leads to cache misses and performance degradation. In this study, we propose a new cache management policy known as Long-Running Stage Set First (LSF), which makes full use of the task dependencies to optimize the cache management performance in DAG-aware scheduling algorithms. LSF calculates the caching and prefetching priorities of resilient distributed datasets according to their unprocessed workloads and significance in parallel scheduling, which are key factors in DAG-aware scheduling algorithms. Moreover, we present a cache-aware task scheduling algorithm based on LSF to reduce the resource fragmentation in computing. Experiments demonstrate that, compared to DAG-aware scheduling algorithms with LRU and MRD, the same algorithms with LSF improve the JCT by up to 42% and 30%, respectively. The proposed cache-aware scheduling algorithm also exhibits about 12% reduction in the average job completion time compared to GRAPHENE with LSF.
Efficient Inter-Device Task Scheduling Schemes for Multi-Device Co-Processing of Data-Parallel Kernels on Heterogeneous Systems
