caa a22 4500 445852348 CHVBK 20180317145417.0 cr unu---uuuuu 170323e20110701xx s 000 0 eng 10.1007/s11227-011-0572-x doi (NATIONALLICENCE)springer-10.1007/s11227-011-0572-x Statistical measures for quantifying task and machine heterogeneities [Elektronische Daten] [Abdulla Al-Qawasmeh, Anthony Maciejewski, Haonan Wang, Jay Smith, Howard Siegel, Jerry Potter] 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 this paper, we identify different statistical measures used to quantify the heterogeneity of HC systems, and show the correlation between the performance of the heuristics and these measures through simple mapping examples and synthetic data analysis. In addition, we illustrate how regression trees can be used to predict the most appropriate heuristic for an HC system based on its heterogeneity. Springer Science+Business Media, LLC, 2011 Distributed systems nationallicence Heterogeneity nationallicence Heterogeneous systems nationallicence Mapping heuristics nationallicence Task allocation nationallicence Regression trees nationallicence Al-Qawasmeh Abdulla Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut Maciejewski Anthony Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut Wang Haonan Department of Statistics, Colorado State University, Fort Collins, CO, USA aut Smith Jay Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut Siegel Howard Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut Potter Jerry Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut The Journal of Supercomputing Springer US; http://www.springer-ny.com 57/1(2011-07-01), 34-50 0920-8542 57:1<34 2011 57 11227 https://doi.org/10.1007/s11227-011-0572-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11227-011-0572-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Al-Qawasmeh Abdulla Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut NATIONALLICENCE 700 1- Maciejewski Anthony Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut NATIONALLICENCE 700 1- Wang Haonan Department of Statistics, Colorado State University, Fort Collins, CO, USA aut NATIONALLICENCE 700 1- Smith Jay Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut NATIONALLICENCE 700 1- Siegel Howard Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut NATIONALLICENCE 700 1- Potter Jerry Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA aut NATIONALLICENCE 773 0- The Journal of Supercomputing Springer US; http://www.springer-ny.com 57/1(2011-07-01), 34-50 0920-8542 57:1<34 2011 57 11227 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer