TY - GEN
T1 - Energy-aware resource management for computing systems
AU - Siegel, Howard Jay
AU - Khemka, Bhavesh
AU - Friese, Ryan
AU - Pasricha, Sudeep
AU - Maciejewski, Anthony A.
AU - Koenig, Gregory A.
AU - Powers, Sarah
AU - Hilton, Marcia
AU - Rambharos, Rajendra
AU - Okonski, Gene
AU - Poole, Steve
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [1], [2]. We address the problem of assigning dynamically-arriving tasks to machines in a heterogeneous computing environment. These machines execute a workload composed of different tasks, where the tasks have diverse computational requirements. Each task has a utility function associated with it that represents the value of completing that task, and this utility decreases the longer it takes a task to complete. The goal of our resource manager is to maximize the sum of the utilities earned by all tasks arriving in the system over a given interval of time, while satisfying an energy constraint. We describe example energy-aware resource management methods to accomplish this goal, and compare their performance. We also study the bi-objective problem of maximizing system utility and minimizing the system energy consumption. This analysis technique allows system administrators to investigate the trade-offs between these conflicting goals.
AB - This corresponds to the material in the invited keynote presentation by H. J. Siegel, summarizing the research in [1], [2]. We address the problem of assigning dynamically-arriving tasks to machines in a heterogeneous computing environment. These machines execute a workload composed of different tasks, where the tasks have diverse computational requirements. Each task has a utility function associated with it that represents the value of completing that task, and this utility decreases the longer it takes a task to complete. The goal of our resource manager is to maximize the sum of the utilities earned by all tasks arriving in the system over a given interval of time, while satisfying an energy constraint. We describe example energy-aware resource management methods to accomplish this goal, and compare their performance. We also study the bi-objective problem of maximizing system utility and minimizing the system energy consumption. This analysis technique allows system administrators to investigate the trade-offs between these conflicting goals.
KW - bi-objective optimization
KW - energy-aware computing
KW - heterogeneous distributed computing
KW - high performance computing system
KW - resource management
UR - http://www.scopus.com/inward/record.url?scp=84908612519&partnerID=8YFLogxK
U2 - 10.1109/IC3.2014.6897139
DO - 10.1109/IC3.2014.6897139
M3 - Conference contribution
AN - SCOPUS:84908612519
T3 - 2014 7th International Conference on Contemporary Computing, IC3 2014
SP - 7
EP - 12
BT - 2014 7th International Conference on Contemporary Computing, IC3 2014
A2 - Parashar, Manish
A2 - Madhu Kumar, S.D.
A2 - Madduri, Kamesh
A2 - Narendra, Nanjangud C.
A2 - Krishnan, Murali
A2 - Prasad, Sushil K.
A2 - Chandran, Priya
A2 - Chandra Sekhar, C.
A2 - Li, Xiaolin
A2 - Valera, Carlos
A2 - Chaudhary, Sanjay
A2 - Bellur, Umesh
A2 - Arya, Kavi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 7th International Conference on Contemporary Computing, IC3 2014
Y2 - 7 August 2014 through 9 August 2014
ER -