Modeling the relative fitness of storage
- 12 June 2007
- journal article
- Published by Association for Computing Machinery (ACM) in ACM SIGMETRICS Performance Evaluation Review
- Vol. 35 (1), 37-48
- https://doi.org/10.1145/1269899.1254887
Abstract
Relative fitness is a new black-box approach to modeling the performance of storage devices. In contrast with an absolute model that predicts the performance of a workload on a given storage device, a relative fitness model predicts performance differences between a pair of devices. There are two primary advantages to this approach. First, because are lative fitness model is constructed for a device pair, the application-device feedback of a closed workload can be captured (e.g., how the I/O arrival rate changes as the workload moves from device A to device B). Second, a relative fitness model allows performance and resource utilization to be used in place of workload characteristics. This is beneficial when workload characteristics are difficult to obtain or concisely express (e.g., rather than describe the spatio-temporal characteristics of a workload, one could use the observed cache behavior of device A to help predict the performance of B. This paper describes the steps necessary to build a relative fitness model, with an approach that is general enough to be used with any black-box modeling technique. We compare relative fitness models and absolute models across a variety of workloads and storage devices. On average, relative fitness models predict bandwidth and throughput within 10-20% and can reduce prediction error by as much as a factor of two when compared to absolute models.Keywords
This publication has 11 references indexed in Scilit:
- Synthesizing representative I/O workloads using iterative distillationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Issues and challenges in the performance analysis of real disk arraysIEEE Transactions on Parallel and Distributed Systems, 2004
- Capturing the spatio-temporal behavior of real traffic dataPerformance Evaluation, 2002
- MinervaACM Transactions on Computer Systems, 2001
- The Elements of Statistical LearningSpringer Series in Statistics, 2001
- Using system-level models to evaluate I/O subsystem designsIEEE Transactions on Computers, 1998
- An analytic behavior model for disk drives with readahead caches and request reorderingACM SIGMETRICS Performance Evaluation Review, 1998
- Using attribute-managed storage to achieve QoSPublished by Springer Science and Business Media LLC ,1997
- Analytic modeling of clustered RAID with mapping based on nearly random permutationIEEE Transactions on Computers, 1996
- Shoring up persistent applicationsACM SIGMOD Record, 1994