Relative fitness models for storage
ACM SIGMETRICS Performance Evaluation Review - Design, implementation, and performance of storage systems
Modeling the relative fitness of storage
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Observer: keeping system models from becoming obsolete
HotAC II Hot Topics in Autonomic Computing on Hot Topics in Autonomic Computing
Communications of the ACM - A Direct Path to Dependable Software
BASIL: automated IO load balancing across storage devices
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
Storage device performance prediction with selective bagging classification and regression tree
NPC'10 Proceedings of the 2010 IFIP international conference on Network and parallel computing
Differentiated storage services
ACM SIGOPS Operating Systems Review
IO performance prediction in consolidated virtualized environments
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
YouChoose: Choosing your Storage Device as a Performance Interface to Consolidated I/O Service
ACM Transactions on Storage (TOS)
Pesto: online storage performance management in virtualized datacenters
Proceedings of the 2nd ACM Symposium on Cloud Computing
Differentiated storage services
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
QoS support for end users of I/O-intensive applications using shared storage systems
Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Storage workload modelling by hidden Markov models: Application to Flash memory
Performance Evaluation
An autonomic framework for enhancing the quality of data grid services
Future Generation Computer Systems
Romano: autonomous storage management using performance prediction in multi-tenant datacenters
Proceedings of the Third ACM Symposium on Cloud Computing
Experimental evaluation of the performance-influencing factors of virtualized storage systems
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Experimental evaluation of the performance-influencing factors of virtualized storage systems
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
Performance models of storage contention in cloud environments
Software and Systems Modeling (SoSyM)
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Storage device performance prediction is a key element of self-managed storage systems. This work explores the application of a machine learning tool, CART models, to storage device modeling. Our approach predicts a deviceýs performance as a function of input workloads, requiring no knowledge of the device internals. We propose two uses of CART models: one that predicts per-request response times (and then derives aggregate values) and one that predicts aggregate values directly from workload characteristics. After being trained on the device in question, both provide accurate black-box models across a range of test traces from real environments. Experiments show that these models predict the average and 90th percentile response time with a relative error as low as 19%, when the training workloads are similar to the testing workloads, and interpolate well across different workloads.