Applying the Heterogeneity Level Metric in a Distributed Platform
Main Article Content
Abstract
Heterogeneity Level (HL) metric has been developed by our research-group to help scheduling algorithms to adapt themselves to the existent heterogeneity in the platforms. This paper presents our results considering the HL’s behaviour in a real adaptive scheduling. HL metric quantifies qualitative aspects from heterogeneity in order to provide efficient performances and lower cost to the execution in both heterogeneous and homogeneous platforms. HL use is investigated under different perspectives:CPU, memory, network and considering benchmarks results. A simple but effective adaptive scheduling using HL is proposed and its results point out to performance-gains around 53% when a non-adaptive scheduling algorithm is used. Our case studies show that the HL was efficient, flexible and easily used for scheduling policies.
Article Details
How to Cite
Souza, P. S. L., Histoshi, F., Santana, M. J., Santana, R. H. C., Bruschi, S. M., & Branco, K. R. L. J. C. (2011). Applying the Heterogeneity Level Metric in a Distributed Platform. INFOCOMP Journal of Computer Science, 10(2), 17–25. Retrieved from https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/329
Section
Articles
Upon receipt of accepted manuscripts, authors will be invited to complete a copyright license to publish the paper. At least the corresponding author must send the copyright form signed for publication. It is a condition of publication that authors grant an exclusive licence to the the INFOCOMP Journal of Computer Science. This ensures that requests from third parties to reproduce articles are handled efficiently and consistently and will also allow the article to be as widely disseminated as possible. In assigning the copyright license, authors may use their own material in other publications and ensure that the INFOCOMP Journal of Computer Science is acknowledged as the original publication place.