Storage device performance prediction with CART models

ABSTRACT

This work explores the application of a machine learning tool, CART modeling, to storage devices. We have developed approaches to predict a device's performance as a function of input workloads, requiring no knowledge of the device internals. Two uses of CART models are considered: one that predicts per-request response times (and then derives aggregate values) and one that predicts aggregate values directly from workload characteristics. After training on the device in question, both provide reasonably-accurate black box models across a range of test traces from real environments. An expanded version of this paper is available as a technical report [1].

REFERENCES

• 1: Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger. Storage device performance prediction with CART models. Technical Report CMU-PDL-04-103, 2004.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.4 PERFORMANCE OF SYSTEMS
      Subjects: Modeling techniques

Additional Classification:
  D. Software
  D.4 OPERATING SYSTEMS
      D.4.8 Performance
          Subjects: Modeling and prediction

General Terms:
Experimentation, Management, Performance

Keywords:
performance prediction, storage device modeling