Walter Scott, Jr. College of Engineering

Graduate Exam Abstract

Abdulla Al-Qawasmeh
Ph.D. Final
Jun 29, 2012, 10 AM
ISTeC Conference Room - CS305
Heterogeneous Computing Environment Characterization and Thermal-Aware Scheduling Strategies to Optimize Data Center Power Consumption
Abstract: We study heterogeneous computing (HC) systems that
consist of a set of different machines that have
varying capabilities. These machines are used to
execute a set of heterogeneous tasks that vary in
their computational complexity. Finding the
optimal mapping of tasks to machines in an HC
system has been shown to be, in general, an NP-
complete problem. Therefore, heuristics have been
used to find near-optimal mappings. The
performance of allocation heuristics can be
affected significantly by factors such as task and
machine heterogeneities. In our research, we
identify different measures used to quantify the
heterogeneity of HC systems, and show the
correlation between the performance of the
heuristics and these measures. The power
consumption of data centers has been increasing at
a rapid rate over the past few years. Motivated by
the need to reduce the power consumption of data
centers, many researchers have been investigating
methods to increase the energy efficiency in
computing at different levels: chip, server, rack,
and data center. In this research, we study the
problem of optimizing the power and performance of
a data center. We consider a power model for a
data center that includes power consumed in both
Computer Room Air Conditioning (CRAC) units and
compute nodes.
Adviser: H.J. Siegel
Co-Adviser: Anthony A. Maciejewski
Non-ECE Member: Haonan Wang, Statistics
Member 3: Sudeep Pasricha, ECE
Addional Members: N/A
Publications:
A. M. Al-Qawsmeh and Said Bettayeb, “A proactive distance-based flooding technique for MANETs with heterogeneous radio ranges,” Local Area Networks 2008, pp.648-654, 2008.
A. M. Al-Qawasmeh, A. A. Maciejewski, H. J. Siegel, J. Smith, and J. Potter, “Task and Machine heterogeneities: Higher Moments Matter,” International Conference on Parallel and Distributed Processing Techniques and Applications 2009. pp.3-9, 2009.
A. M. Al-Qawasmeh, A. A. Maciejewski, and H. J. Siegel. “Characterizing heterogeneous computing environments using singular value decomposition,” 19th Heterogeneity in Computing Workshop (HCW 2010), cosponsors: IEEE Computer Society and Office of Naval Research, International Parallel and Distributed Processing Symposium IPDPS 2010 Workshops & PhD Forum, 9 pp., 2010.
L. D. Briceo, J. Smith, H. J. Siegel, A. A. Maciejewski, P. Maxwell, R. Wakefield, A. M. Al-Qawasmeh, R. C.-L. Chiang, and J. Li. “Robust resource allocation of DAGs in a heterogeneous multicore system,” 19th Heterogeneity in Computing Workshop (HCW 2010), cosponsors: IEEE Computer Society and Office of Naval Research, International Parallel and Distributed Processing Symposium IPDPS 2010 Workshops & PhD Forum, 11 pp., 2010.
A. M. Al-Qawasmeh, A. A. Maciejewski, R. G. Roberts, and H. J. Siegel, “Characterizing Task-Machine Affinity in Heterogeneous Computing Environments,” 20th Heterogeneity in Computing Workshop (HCW 2011), cosponsors: IEEE Computer Society and Office of Naval Research, International Parallel and Distributed Processing Symposium IPDPS 2011 Workshops & PhD Forum (IPDPSW), 11 pp., May. 2011.
A. M. Al-Qawasmeh, A. A. Maciejewski, H. Wang, J. Smith, H. J. Siegel, and J. Potter, “Statistical Measures for Quantifying Task and Machine Heterogeneities,” The Journal of Supercomputing, Special Issue on Advances in Parallel and Distributed Computing, Vol. 57, No. 1, July 2011, to appear.
Abdulla M. Al-Qawasmeh, Sudeep Pasricha, Anthony A. Maciejewski, and Howard Jay Siegel, “Thermal-Aware Performance Optimization in Power Constrained Heterogeneous Data Centers,” 21st Heterogeneity in Computing Workshop (HCW 2012), cosponsors: IEEE Computer Society and U.S. Office of Naval Research, in the proceedings of 2012 International Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), May 2012.
Program of Study:
ECE554
ECE514
GRAD510
ECE520
ECE658
ECE674
N/A
N/A