Green-aware workload scheduling in geographically distributed data centers
Renewable (or green) energy, such as solar or wind, has at least partially powered data centers to reduce the environmental impact of traditional energy sources (brown energy with high carbon footprint). In this paper, we propose a holistic workload scheduling algorithm to minimize the brown energy...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2013
|
Online Access: | https://hdl.handle.net/10356/97268 http://hdl.handle.net/10220/13075 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-97268 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-972682020-05-28T07:17:49Z Green-aware workload scheduling in geographically distributed data centers Chen, Changbing He, Bingsheng Tang, Xueyan School of Computer Engineering IEEE International Conference on Cloud Computing Technology and Science (4th : 2012 : Taipei, Taiwan) Renewable (or green) energy, such as solar or wind, has at least partially powered data centers to reduce the environmental impact of traditional energy sources (brown energy with high carbon footprint). In this paper, we propose a holistic workload scheduling algorithm to minimize the brown energy consumption across multiple geographically distributed data centers with renewable energy sources. While green energy supply for a single data center is intermittent due to daily/seasonal effects, our workload scheduling algorithm is aware of different amounts of green energy supply and dynamically schedules the workload across data centers. The scheduling decision adapts to workload and data center cooling dynamics. Our experiments with real workload traces demonstrate that our scheduling algorithm greatly reduces brown energy consumption by up to 40% in comparison with other scheduling policies. 2013-08-12T08:20:59Z 2019-12-06T19:40:44Z 2013-08-12T08:20:59Z 2019-12-06T19:40:44Z 2012 2012 Conference Paper https://hdl.handle.net/10356/97268 http://hdl.handle.net/10220/13075 10.1109/CloudCom.2012.6427545 en |
institution |
Nanyang Technological University |
building |
NTU Library |
country |
Singapore |
collection |
DR-NTU |
language |
English |
description |
Renewable (or green) energy, such as solar or wind, has at least partially powered data centers to reduce the environmental impact of traditional energy sources (brown energy with high carbon footprint). In this paper, we propose a holistic workload scheduling algorithm to minimize the brown energy consumption across multiple geographically distributed data centers with renewable energy sources. While green energy supply for a single data center is intermittent due to daily/seasonal effects, our workload scheduling algorithm is aware of different amounts of green energy supply and dynamically schedules the workload across data centers. The scheduling decision adapts to workload and data center cooling dynamics. Our experiments with real workload traces demonstrate that our scheduling algorithm greatly reduces brown energy consumption by up to 40% in comparison with other scheduling policies. |
author2 |
School of Computer Engineering |
author_facet |
School of Computer Engineering Chen, Changbing He, Bingsheng Tang, Xueyan |
format |
Conference or Workshop Item |
author |
Chen, Changbing He, Bingsheng Tang, Xueyan |
spellingShingle |
Chen, Changbing He, Bingsheng Tang, Xueyan Green-aware workload scheduling in geographically distributed data centers |
author_sort |
Chen, Changbing |
title |
Green-aware workload scheduling in geographically distributed data centers |
title_short |
Green-aware workload scheduling in geographically distributed data centers |
title_full |
Green-aware workload scheduling in geographically distributed data centers |
title_fullStr |
Green-aware workload scheduling in geographically distributed data centers |
title_full_unstemmed |
Green-aware workload scheduling in geographically distributed data centers |
title_sort |
green-aware workload scheduling in geographically distributed data centers |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/97268 http://hdl.handle.net/10220/13075 |
_version_ |
1681056304913711104 |