Performance Analysis of Mass Storage Service Alternatives for Distributed Systems
IEEE Transactions on Software Engineering
Performance Analysis of Client-Server Storage Systems
IEEE Transactions on Computers
An analytic performance model of disk arrays
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Capacity planning and performance modeling: from mainframes to client-server systems
Capacity planning and performance modeling: from mainframes to client-server systems
RAID: high-performance, reliable secondary storage
ACM Computing Surveys (CSUR)
An Analysis of File Migration in a Unix Supercomputing Environment
An Analysis of File Migration in a Unix Supercomputing Environment
An analytic model of hierarchical mass storage systems with network-attached storage devices
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Performance Analysis of Storage Systems
Performance Evaluation: Origins and Directions
Resource sharing in performance models
EPEW'07 Proceedings of the 4th European performance engineering conference on Formal methods and stochastic models for performance evaluation
Hi-index | 0.00 |
Mass storage systems are finding greater use in scientific computing research environments for retrieving and archiving the large volumes of data generated and manipulated by scientific computation. The paper presents a queuing network model that can be used to carry out capacity planning studies of the Unitree mass storage system. Measurements taken on an existing system and a detailed workload characterization provided the workload intensity and resource demand parameters for the various types of read and write requests. The performance model developed here is based on approximations to multi-class mean value analysis of queuing networks. The approximations were validated through the use of discrete event simulation. The resulting baseline model was used to predict the performance of the system as the workload intensity increases.