December 15, 2012
J. Basak, K. Wadhwani, K. Voruganti, S. Narayanamurthy, V. Mathur, and S. Nandi.
We present a machine learning based black-box modeling algorithm, M-LISP, which can predict system behavior in untrained regions for emerging multi-tenant and dynamic data center environments.
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 black-box modeling algorithm called M-LISP that can predict system behavior in untrained region for these emerging multi-tenant 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.
In ACM SIGOPS Operating Systems Review, Vol. 46, No. 3, December 2012, pp. 20-31
A copy of the paper is attached to this posting. The definitive version of the paper can be found at: https://dl.acm.org/citation.cfm?doid=2421648.2421653.