Green Scheduling: A Scheduling Policy for Improving the Energy Efficiency of Fair Scheduler

Abstract

Energy efficiency of data centers has draw a great attention due to the cost of power consumption increases dramatically as the size of data center grows. Nowadays, Map Reduce is a framework widely used for processing large data sets in data center, its energy efficiency directly affects the energy efficiency of data center. MapReduce's energy efficiency is closely tied to its scheduler, we find that fair scheduler outperforms FIFO scheduler in energy efficiency when CPU-intensive job and IO-intensive job running simultaneously on the cluster, because fair scheduler achieves better resource utilization by overlapping resource complementary tasks on slaves. However this behavior is occasional, because fair scheduler has no information about task's resource requirement. This occasional behavior lets us identify the area that energy efficiency of fair scheduler can be improved. We propose an energy-efficient scheduling policy called green scheduling which relaxes fairness slightly to create as many opportunities as possible for overlapping resource complementary tasks. The results show that green scheduling can save between 7% and 9% energy consumption of fair scheduler.