Risk-based dynamic allocation of computing resources
Yingdong Lu, Siva Theja Maguluri, et al.
MAMA/Greenmetrics 2016
In this paper we present a study of the job arrival patterns from a parallel computing system and the impact of such arrival patterns on the performance of parallel scheduling strategies. Using workload data from the Cornell Theory Center, we develop a class of traffic models to characterize these arrival patterns. Our analysis of the job arrival data illustrates traffic patterns that exhibit heavy-tailed behavior and other characteristics which are quite different from the arrival processes used in previous studies of parallel scheduling. We then investigate the impact of these arrival traffic patterns on the performance of parallel space-sharing strategies, including the derivation of some scheduling optimality results.
Yingdong Lu, Siva Theja Maguluri, et al.
MAMA/Greenmetrics 2016
Eugenio Gianniti, Li Zhang, et al.
CLOSER 2019
Jianying Hu, Yingdong Lu, et al.
Performance Evaluation Review
Jian Tan, Li Zhang, et al.
IFIP WG 7.3 Performance 2015