Fourier-assisted machine learning of hard disk drive access time models
PDSW '13 Proceedings of the 8th Parallel Data Storage Workshop
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Today's storage systems and database systems are highly complex and configurable, which makes storage management intricate and costly. One critical aspect of storage management, particularly in large storage infrastructures (e.g. cloud storage), is to determine which application data sets to store on which devices. With a mechanism which has the ability to predict the performance of the storage device for any given workload, administrator could automate this process. Therefore, storage device performance prediction has become a critical aspect of self-managed storage systems. To this end, we propose a general smoothing hybrid model (namely SRT-SVR) which combines regression tree (RT) and support vector regression (SVR) to accurately model storage device performance. With this new method, the advantages of the two techniques (i.e. RT and SVR) are completely amalgamated to obtain a more accurate and efficient model without compromising prediction time. In addition, we propose a new workload characterization method which can describe request more accurately. Experiments show that SRT-SVR method and the characterization method used in the storage device modeling can produce more accurate and stable predictions than RT and SVR.