On the usability of shortest remaining time first policy in shared Hadoop clusters.

Hadoop has been recently used to process a diverse variety of applications, sharing the same execution infrastructure. A practical problem facing the Hadoop community is how to reduce job makespans by reducing job waiting times and execution times. Previous Hadoop schedulers have focused on improving job execution times, by improving data locality but not considering job waiting times. Even worse, enforcing data locality according to the job input sizes can be inefficient: it can lead to long waiting times for small yet short jobs when sharing the cluster with jobs with smaller input sizes but higher execution complexity. This paper presents hSRTF, an adaption of the well-known Shortest Remaining Time First scheduler (i.e., SRTF) in shared Hadoop clusters. hSRTF embraces a simple model to estimate the remaining time of a job and a preemption primitive (i.e., kill) to free the resources when needed. We have implemented hSRTF and performed extensive evaluations with Hadoop on the Grid'5000 testbed. The results show that hSRTF can significantly reduce the waiting times of small jobs and therefore improves their makespans, but at the cost of a relatively small increase in the makespans of large jobs. For instance, a time-based proportional share mode of hSRTF (i.e., hSRTF-Pr) speeds up small jobs by (on average) 45% and 26% while introducing a performance degradation for large jobs by (on average) 10% and 0.2% compared to Fifo and Fair schedulers, respectively.