Title
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.
Year
DOI
Venue
2011
10.1109/PDCAT.2011.42
PDCAT
Keywords
Field
DocType
complementary task,energy consumption,green scheduling,energy efficiency,fifo scheduler,large data set,fair scheduler,data center,overlapping resource,better resource utilization,scheduling policy,distributed processing,resource manager,resource utilization,scheduling,resource management,energy efficient
Resource management,Fixed-priority pre-emptive scheduling,Scheduling (computing),Efficient energy use,Computer science,Real-time computing,Job scheduler,Data center,Energy consumption,Proportionally fair,Distributed computing
Conference
Citations 
PageRank 
References 
4
0.64
9
Authors
3
Name
Order
Citations
PageRank
Tao Zhu15812.63
Chengchun Shu2374.51
Haiyan Yu340.64