Performance Analysis of Mass Storage Service Alternatives for Distributed Systems
IEEE Transactions on Software Engineering
Calibration and predictive modeling of computer systems
Calibration and predictive modeling of computer systems
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)
CSIM: a C-based process-oriented simulation language
WSC '86 Proceedings of the 18th conference on Winter simulation
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Probability and Statistics with Reliability, Queuing and Computer Science Applications
Single Query Optimization for Tertiary Memory
Single Query Optimization for Tertiary Memory
An Analysis of File Migration in a Unix Supercomputing Environment
An Analysis of File Migration in a Unix Supercomputing Environment
Rob-line Storage: Low Latency, High Capacity
Rob-line Storage: Low Latency, High Capacity
A Model of a Microprocessor with a Wide Command Word
Cybernetics and Systems Analysis
Mean value analysis of re-entrant line with batch machines and multi-class jobs
Computers and Operations Research
| Hi-index | 14.98 |
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 computations. This paper presents a queuing network model that can be used to carry out capacity planning studies of hierarchical mass storage systems. Measurements taken on a Unitree mass storage 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 multiclass Mean Value Analysis of queuing networks. The approximations were validated through the use of discrete event simulation and the complete model was validated and calibrated through measurements. The resulting model was used to analyze three different scenarios: effect of workload intensity increase, use of file compression at the server and client, and use of file abstractions.