2013/06/25 by Matteo Dell’Amico, Matteo Dell'Amico, Dell'Amico, Matteo
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1306.6023
openalex publication_date 2013/06/25 · arxiv created 2013/08/21 · arxiv updated 2013/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Despite the fact that size-based schedulers can give excellent results in terms of both average response times and fairness, data-intensive computing execution engines generally do not employ size-based schedulers, mainly because of the fact that job size is not known a priori. In this work, we perform a simulation-based analysis of the performance of size-based schedulers when they are employed with the workload of typical data-intensive schedules and with approximated size estimations. We show results that are very promising: even when size estimation is very imprecise, response times of size-based schedulers can be definitely smaller than those of simple scheduling techniques such as processor sharing or FIFO.