@InCollection{PuntelCharPetr:2020:CoPeAn,
               author = "Puntel, Fernando Emilio and Char{\~a}o, Andrea Schwertner and 
                         Petry, Adriano",
                title = "Comparative performance analysis of job scheduling algorithms in a 
                         real-world scientific application",
            booktitle = "Computational Science and Its Applications – ICCSA 2020",
            publisher = "Springer International Publishing",
                 year = "2020",
               editor = "Gervasi, O. and Murgante, B. and Misra, S. and Garau, C. and 
                         Blecic, I. and Taniar, D. and Apduhan, B. O. and Rocha, A. M. A. 
                         C. and Tarantino, E. and Torre, C. M. and Karaca, Y.",
                pages = "447--462",
             keywords = "Job scheduling · High performance computing · SLURM and resource 
                         management system.",
             abstract = "In High Performance Computing, it is common to deal with 
                         substantial computing resources, and the use of a Resource 
                         Management System (RMS) becomes fundamental. The job scheduling 
                         algorithm is a key part of a RMS, and the selection of the best 
                         job scheduling that meets the user needs is of most relevance. In 
                         this work, we use a real-world scientific application to evaluate 
                         the performance of 4 different job scheduling algorithms: First 
                         in, first out (FIFO), Shortest Job First (SJF), EASYbackfilling 
                         and Fattened-backfilling. These algorithms worked with RMS SLURM 
                         workload manager, considering a scientific application that 
                         predicts the earths ionosphere dynamics. In the results we 
                         highlight each algorithms strength and weakness for different 
                         scenarios that change the possibility of advancing smaller jobs. 
                         To deepen our analysis, we also compared the job scheduling 
                         algorithms using 4 jobs of Numerical Aerodynamic Sampling (NAS) 
                         Parallel Benchmarks in a controlled scenario.",
          affiliation = "{Universidade Federal de Santa Maria (UFSM)} and {Universidade 
                         Federal de Santa Maria (UFSM)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                  doi = "10.1007/978-3-030-58799-4_33",
                  url = "http://dx.doi.org/10.1007/978-3-030-58799-4_33",
                 isbn = "9783030587987",
                label = "lattes: 3638070053255922 3 PuntelCharPetr:2020:CoPeAn",
             language = "en",
          seriestitle = "Lecture Notes in Computer Science",
           targetfile = "puntel_comparative.pdf",
                  url = "http://link.springer.com/10.1007/978-3-030-58799-4_33",
               volume = "12249",
        urlaccessdate = "28 abr. 2024"
}