Modeling the impact of workload on cloud resource scaling
Anshul Gandhi, Parijat Dube, et al.
SBAC-PAD 2014
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.
Anshul Gandhi, Parijat Dube, et al.
SBAC-PAD 2014
Anton Riabov, Zhen Liu, et al.
ICDCS 2004
Yingdong Lu, Ana Radovanović, et al.
SOLI 2006
Yingdong Lu, Siva Theja Maguluri, et al.
ACC 2018