Trade-Off Between Prediction Accuracy and Underestimation Rate in Job Runtime Estimates

• Published in: 2017 IEEE International Conference on Cluster Computing (CLUSTER) pp. 530 - 540
• Main Authors: Yuping Fan, Rich, Paul, Allcock, William E., Papka, Michael E., Zhiling Lan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Blue Gene systems; Data models; Estimation; job scheduling; Mathematical model; Processor scheduling; Runtime; runtime prediction; Scheduling; Support vector machines

Job runtime estimates provided by users are widely acknowledged to be overestimated and runtime overestimation can greatly degrade job scheduling performance. Previous studies focus on improving accuracy of job runtime estimates by reducing runtime overestimation, but fail to address the underestima...