TY - GEN
T1 - Simple and Effective Dynamic Provisioning for Power-Proportional Data Centers
AU - Lu, Tan
AU - Chen, Minghua
PY - 2012/3
Y1 - 2012/3
N2 - Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much gain knowing future workload information can bring to dynamic provisioning. In particular, we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the off-line dynamic provisioning problem, which allows us to characterize the optimal solution in a divide-and-conquer manner. We then exploit this insight to design two online algorithms with competitive ratios 2 α and e/ (e 1 + α), respectively, where 0 α 1 is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to α = 1) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to implement. We demonstrate their effectiveness in simulations using real-world traces. © 2012 IEEE.
AB - Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much gain knowing future workload information can bring to dynamic provisioning. In particular, we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the off-line dynamic provisioning problem, which allows us to characterize the optimal solution in a divide-and-conquer manner. We then exploit this insight to design two online algorithms with competitive ratios 2 α and e/ (e 1 + α), respectively, where 0 α 1 is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to α = 1) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to implement. We demonstrate their effectiveness in simulations using real-world traces. © 2012 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=84868551372&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84868551372&origin=recordpage
U2 - 10.1109/CISS.2012.6310753
DO - 10.1109/CISS.2012.6310753
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-1-4673-3139-5
T3 - 46th Annual Conference on Information Sciences and Systems, CISS
BT - 2012 46th Annual Conference on Information Sciences and Systems (CISS)
PB - IEEE
T2 - 46th Annual Conference on Information Sciences and Systems (CISS 2012)
Y2 - 21 March 2012 through 23 March 2012
ER -