Racing and pacing to idle: An evaluation of heuristics for energy-aware resource allocation

Proceedings of the Workshop on Power-Aware Computing and Systems, HotPower 2013

Volumn , Issue , 2013, Pages

  Hoffmann, Henry ^a  

^a UNIVERSITY OF CHICAGO   (United States)

Author keywords

energy aware computing; optimization; power aware computing; resource allocation

Indexed keywords

COMPUTING RESOURCE; ENERGY AWARE; ENERGY-AWARE COMPUTING; HEURISTIC SOLUTIONS; LINEAR PROGRAMS; OPTIMAL SOLUTIONS; POWER-AWARE COMPUTING; REAL SYSTEMS;

ENERGY UTILIZATION; OPTIMAL SYSTEMS; OPTIMIZATION;

RESOURCE ALLOCATION;

EID: 84889653189     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2525526.2525854     Document Type: Conference Paper

References (22)

1. R. Balasubramonian et al. Memory hierarchy reconfiguration for energy and performance in general-purpose processor architectures. In MICRO, 2000.
2. C. Bienia et al. The PARSEC benchmark suite: Characterization and architectural implications. In PACT, Oct 2008.
3. J. Chen and L. K. John. Predictive coordination of multiple on-chip resources for chip multiprocessors. In ICS, 2011.
4. R. Cochran et al. Pack & cap: adaptive dvfs and thread packing under power caps. In MICRO, 2011.
5. B. Diniz et al. Limiting the power consumption of main memory. In ISCA, 2007.
6. W. Felter et al. A performance-conserving approach for reducing peak power consumption in server systems. In ICS, 2005.
7. A. Gandhi et al. Power capping via forced idleness. In Workshop on Energy-Efficient Design, June 2009.
8. U. Hoelzle and L. A. Barroso. The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines. Morgan and Claypool Publishers, 1st edition, 2009.
9. H. Hoffmann et al. Self-aware computing in the angstrom processor. In DAC, 2012.
10. H. Hoffmann et al. A generalized software framework for accurate and efficient managment of performance goals. In EMSOFT, 2013.
11. X. Li et al. Performance directed energy management for main memory and disks. Trans. Storage, 1(3):346-380, Aug. 2005.
12. X. Li et al. Cross-component energy management: Joint adaptation of processor and memory. ACM Trans. Archit. Code Optim., 4(3), Sept. 2007.
13. M. Maggio et al. Controlling software applications via resource allocation within the heartbeats framework. In CDC, 2010.
14. M. Maggio et al. Power optimization in embedded systems via feedback control of resource allocation. IEEE Transactions On Control Systems Technology, 21(1):239-246, 2013.
15. D. Meisner et al. Powernap: eliminating server idle power. 2009.
16. D. Meisner et al. Power management of online data-intensive services. ISCA, 2011.
17. T. Pering et al. The simulation and evaluation of dynamic voltage scaling algorithms. In ISLPED, 1998.
18. SWISH++. http://swishplusplus.sourceforge.net/.
19. M. Weiser et al. Scheduling for reduced CPU energy. Mobile Computing, 1996.
20. J. A. Winter et al. Scalable thread scheduling and global power management for heterogeneous many-core architectures. In PACT, 2010.
21. X. Zhang et al. A flexible framework for throttling-enabled multicore management (temm). In ICPP, 2012.
22. H. Zheng et al. Mini-rank: Adaptive dram architecture for improving memory power efficiency. In MICRO, 2008.