ARIMA time series modeling and forecasting for adaptive I/O prefetching
ICS '01 Proceedings of the 15th international conference on Supercomputing
Markov model prediction of I/O requests for scientific applications
ICS '02 Proceedings of the 16th international conference on Supercomputing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Capturing the spatio-temporal behavior of real traffic data
Performance Evaluation
Analysis of Self-Similarity in I/O Workload Using Structural Modeling
MASCOTS '99 Proceedings of the 7th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
The Mathematica Book
Storage Device Performance Prediction with CART Models
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
File system design for an NFS file server appliance
WTEC'94 Proceedings of the USENIX Winter 1994 Technical Conference on USENIX Winter 1994 Technical Conference
Characterization of the E-commerce Storage Subsystem Workload
QEST '08 Proceedings of the 2008 Fifth International Conference on Quantitative Evaluation of Systems
Evaluation of disk-level workloads at different time-scales
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
Response time distribution of flash memory accesses
Performance Evaluation
Performance implications of flash and storage class memories
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
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A workload analysis technique is presented that processes data from operation type traces and creates a hidden Markov model (HMM) to represent the workload that generated those traces. The HMM can be used to create representative traces for performance models, such as simulators, avoiding the need to repeatedly acquire suitable traces. It can also be used to estimate the transition probabilities and rates of a Markov modulated arrival process directly, for use as input to an analytical performance model of Flash memory. The HMMs obtained from industrial workloads-both synthetic benchmarks, preprocessed by a file translation layer, and real, time-stamped user traces-are validated by comparing their autocorrelation functions and other statistics with those of the corresponding monitored time series. Further, the performance model applications, referred to above, are illustrated by numerical examples.