Local resource shaper for MapReduce

Resource capacity is often over provisioned to primarily deal with short periods of peak load. Shaping these peaks by shifting them to low utilization periods (valleys) is referred to as 'resource consumption shaping'. While originally aimed at the data center level, the resource consumption shaping we consider focuses on local resources, like CPU or I/O as we have identified that individual jobs also incur load peaks and valleys on these resources. In this paper, we present Local Resource Shaper (LRS), which limits fairness in resource sharing between co-located MapReduce tasks. LRS enables Hadoop to maximize resource utilization and minimize resource contention independently of job type. Co-located MapReduce tasks are often prone to resource contention (i.e., Load peak) due to similar resource usage patterns particularly with traditional fair resource sharing. In essence, LRS differentiates co-located tasks through active and passive slots that serve as containers for interchangeable map or reduce tasks. LRS lets an active slot consume as much resources as possible, and a passive slot make use of any unused resources. LRS leverages such slot differentiation with its new scheduler, Interleave. Our results show that LRS always outperforms the best static slot configuration with three Hadoop schedulers in terms of both resource utilization and performance.