A Highly Efficient Data Locality Aware Task Scheduler for Cloud-Based Systems

Abstract

Scheduling tasks in the vicinity of stored data can significantly diminish network traffic. Scheduling optimisation can improve data locality by attempting to locate a task and its related data on the same node. Existing schedulers tend to ignore overhead and tradeoff between data transfer and task placement, and bandwidth consumption, by only emphasising data locality without considering other factors. We present a novel data locality aware scheduler for balancing time consumption and network bandwidth traffic - DLAforBT - to improve data locality for tasks and throughput, with the optimal placement policy exhibiting a threshold-based structure. DLAforBT uses bipartite graph modelling to represent data placement, adopts a judgment mechanism and a precise prediction model to determine moving data or moving computation. It integrates an improved Dominant Resource Fairness (DRF) resource allocation to capture tenants' resource allocation and run as many jobs as possible. DLAforBT improves by 16% of data locality rate, and 25% of throughput.