An analytic behavior model for disk drives with readahead caches and request reordering
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Analytic Modeling of Clustered RAID with Mapping Based on Nearly Random Permutation
IEEE Transactions on Computers
A Modular, Analytical Throughput Model for Modern Disk Arrays
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Storage device performance prediction with CART models
Proceedings of the joint international conference on Measurement and modeling of computer systems
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Modeling the relative fitness of storage
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
BASIL: automated IO load balancing across storage devices
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
Pesto: online storage performance management in virtualized datacenters
Proceedings of the 2nd ACM Symposium on Cloud Computing
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Increasingly, storage vendors are finding it difficult to leverage existing white-box and black-box modeling techniques to build robust system models that can predict system behavior in the emerging dynamic and multi-tenant data centers. White-box models are becoming brittle because the model builders are not able to keep up with the innovations in the storage system stack, and black-box models are becoming brittle because it is increasingly difficult to a priori train the model for the dynamic and multi-tenant data center environment. Thus, there is a need for innovation in system model building area. In this paper we present a machine learning based blackbox modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multitenant and dynamic data center environments. We have implemented and analyzed M-LISP in real environments and the initial results look very promising. We also provide a survey of some common machine learning algorithms and how they fare with respect to satisfying the modeling needs of the new data center environments.