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Self adaptive workload classification and forecasting in multi-tiered storage system using ARIMA time series modeling
Self adaptive workload classification and forecasting in multi-tiered storage system using ARIMA time series modeling
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机译:使用ARIMA时间序列建模的多层存储系统中的自适应工作量分类和预测
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摘要
Techniques are described data storage optimization that determine predicted values for I/O statistics using an ARIMA (auto-regressive integrated moving average) model. The ARIMA model may be used to capture periodic patterns and trends of workload I/O access to predict the future load demand. A current set of I/O statistics is collected for a current time period T. Using the current set and one or more ARIMA models, a predicted set of I/O statistics is determined for a next time period T+1. Each of the ARIMA models is characterized by model parameters including P denoting a number of auto-regressive terms, D denoting a number of nonseasonal difference needed for stationarity, and Q denoting a number of lagged forecast errors of prediction. A data storage optimizer may determine one or more data portions for movement from a current storage tier to a target storage tier using the predicted set of I/O statistics.
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