DEPARTMENT OF COMPUTER SCIENCE

DOCTORAL DISSERTATION DEFENSE

Candidate: Elizabeth Shriver

Advisors: Alan Siegel and John Wilkes

DOCTORAL DISSERTATION DEFENSE

Candidate: Elizabeth Shriver

Advisors: Alan Siegel and John Wilkes

**Performance modeling for realistic storage devices**

3:00 p.m., Thursday, January 30, 1997

12th floor conference room, 719 Broadway

Abstract

Managing large amounts of storage is difficult and becoming more so as
both the complexity and number of storage devices are increasing. One
approach to this problem is a *self-managing storage system*.
Since a self-managing storage system is a real-time system,
it requires a model that quickly approximates the behavior of the
storage device in a workload-dependent fashion.
We develop such a model.

Our approach to modeling devices is to model the individual
*components* of the device, such as queues, caches,
and disk mechanisms, and then *compose* the components.
To determine the performance of a component,
each component modifies the entering workload use patterns
and determines the performance from the workload use patterns
and the lower-level device behavior.
For example, modifying the use patterns allows us to
capture the altered spatial locality
that occurs when queues reorder their requests.

Our model predicts the device behavior in terms of response time within a 8% relative error for an interesting subset of the domain of devices and workloads. To demonstrate this, the model has been validated with synthetic traces of parallel scientific file system applications and traces of transaction processing applications.

Our contributions to the area of performance modeling for storage devices include the following:

- 1.
- Methods to approximate the positioning time for the disk head of a magnetic disk.
- 2.
- Methods to approximate the queue delay for non-FCFS scheduling algorithms.
- 3.
- Methods to approximate the cache-miss probabilities and the full and partial cache-hit probabilities in the data caches in the I/O path using measures of workload spatial locality.
- 4.
- Methods to approximate the mean seek time and rotational latency of the disk mechanism using measures of workload spatial locality.
- 5.
- An infrastructure for developing a composite model. The infrastructure supports the development of more complicated devices and workloads than we have validated.