A survey of design techniques for system-level dynamic power management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on low-power electronics and design
Critical power slope: understanding the runtime effects of frequency scaling
ICS '02 Proceedings of the 16th international conference on Supercomputing
The case for power management in web servers
Power aware computing
Dynamic cluster reconfiguration for power and performance
Compilers and operating systems for low power
Managing server energy and operational costs in hosting centers
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Energy conservation in heterogeneous server clusters
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Energy-Efficient Real-Time Heterogeneous Server Clusters
RTAS '06 Proceedings of the 12th IEEE Real-Time and Embedded Technology and Applications Symposium
Ensemble-level Power Management for Dense Blade Servers
Proceedings of the 33rd annual international symposium on Computer Architecture
On evaluating request-distribution schemes for saving energy in server clusters
ISPASS '03 Proceedings of the 2003 IEEE International Symposium on Performance Analysis of Systems and Software
Power provisioning for a warehouse-sized computer
Proceedings of the 34th annual international symposium on Computer architecture
No "power" struggles: coordinated multi-level power management for the data center
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
MEC-IDC: joint load balancing and power control for distributed Internet Data Centers
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
INFOCOM'10 Proceedings of the 29th conference on Information communications
Modelling of staged routing for reduced carbon footprints of large server clusters
International Journal of Communication Networks and Distributed Systems
Markov Model Based Power Management in Server Clusters
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Benefits and limitations of tapping into stored energy for datacenters
Proceedings of the 38th annual international symposium on Computer architecture
The case for sleep states in servers
HotPower '11 Proceedings of the 4th Workshop on Power-Aware Computing and Systems
Handling more data with less cost: taming power peaks in MapReduce clusters
Proceedings of the Asia-Pacific Workshop on Systems
AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers
ACM Transactions on Computer Systems (TOCS)
Handling more data with less cost: taming power peaks in mapreduce clusters
APSys'12 Proceedings of the Third ACM SIGOPS Asia-Pacific conference on Systems
Optimal Server Allocation and Frequency Modulation on Multi-Core Based Server Clusters
International Journal of Green Computing
SOFTScale: stealing opportunistically for transient scaling
Proceedings of the 13th International Middleware Conference
On understanding the energy consumption of ARM-based multicore servers
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Exact analysis of the M/M/k/setup class of Markov chains via recursive renewal reward
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Power-aware optimization for heterogeneous multi-tier clusters
Journal of Parallel and Distributed Computing
Dynamic right-sizing for power-proportional data centers
IEEE/ACM Transactions on Networking (TON)
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This paper presents an energy management policy for reconfigurable clusters running a multi-tier application, exploiting DVS together with multiple sleep states. We develop a theoretical analysis of the corresponding power optimization problem and design an algorithm around the solution. Moreover, we rigorously investigate selection of the optimal number of spare servers for each power state, a problem that has only been approached in an ad-hoc manner in current policies. To validate our results and policies, we implement them on an actual multi-tier server cluster where nodes support all power management techniques considered. Experimental results using realistic dynamic workloads based on the TPCW benchmark show that exploiting multiple sleep states results in significant additional cluster-wide energy savings up to 23% with little or no performance degradation.