Capacity planning with phased workloads
Proceedings of the 1st international workshop on Software and performance
ARIMA time series modeling and forecasting for adaptive I/O prefetching
ICS '01 Proceedings of the 15th international conference on Supercomputing
Minerva: An automated resource provisioning tool for large-scale storage systems
ACM Transactions on Computer Systems (TOCS)
Hippodrome: Running Circles Around Storage Administration
FAST '02 Proceedings of the Conference on File and Storage Technologies
A Modular, Analytical Throughput Model for Modern Disk Arrays
MASCOTS '01 Proceedings of the Ninth International Symposium in Modeling, Analysis and Simulation of Computer and Telecommunication Systems
Using probabilistic reasoning to automate software tuning
Using probabilistic reasoning to automate software tuning
Multi-dimensional storage virtualization
Proceedings of the joint international conference on Measurement and modeling of computer systems
Interposed proportional sharing for a storage service utility
Proceedings of the joint international conference on Measurement and modeling of computer systems
Storage device performance prediction with CART models
Proceedings of the joint international conference on Measurement and modeling of computer systems
The software architecture of a SAN storage control system
IBM Systems Journal
Façade: Virtual Storage Devices with Performance Guarantees
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Polus: Growing Storage QoS Management Beyond a "4-Year Old Kid"
FAST '04 Proceedings of the 3rd USENIX Conference on File and Storage Technologies
Simplifying network administration using policy-based management
IEEE Network: The Magazine of Global Internetworking
Research challenges of autonomic computing
Proceedings of the 27th international conference on Software engineering
Don't settle for less than the best: use optimization to make decisions
HOTOS'07 Proceedings of the 11th USENIX workshop on Hot topics in operating systems
Evolution of storage management: transforming raw data into information
IBM Journal of Research and Development
CA-NFS: a congestion-aware network file system
FAST '09 Proccedings of the 7th conference on File and storage technologies
CA-NFS: A congestion-aware network file system
ACM Transactions on Storage (TOS)
Automated control for elastic storage
Proceedings of the 7th international conference on Autonomic computing
Managing Variability in the IO Performance of Petascale Storage Systems
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Using TCP/IP traffic shaping to achieve iSCSI service predictability
LISA'10 Proceedings of the 24th international conference on Large installation system administration
Time-varying management of data storage
HotDep'05 Proceedings of the First conference on Hot topics in system dependability
YouChoose: Choosing your Storage Device as a Performance Interface to Consolidated I/O Service
ACM Transactions on Storage (TOS)
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
Modeling virtualized applications using machine learning techniques
VEE '12 Proceedings of the 8th ACM SIGPLAN/SIGOPS conference on Virtual Execution Environments
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Enterprise applications typically depend on guaranteed performance from the storage subsystem, lest they fail. However, unregulated competition is unlikely to result in a fair, predictable apportioning of resources. Given that widespread access protocols and scheduling policies are largely best-effort, the problem of providing performance guarantees on a shared system is a very difficult one. Clients typically lack accurate information on the storage system's capabilities and on the access patterns of the workloads using it, thereby compounding the problem. CHAMELEON is an adaptive arbitrator for shared storage resources; it relies on a combination of self-refining models and constrained optimization to provide performance guarantees to clients. This process depends on minimal information from clients, and is fully adaptive; decisions are based on device and workload models automatically inferred, and continuously refined, at run-time. Corrective actions taken by CHAMELEON are only as radical as warranted by the current degree of knowledge about the system's behavior. In our experiments on a real storage system CHAMELEON identified, analyzed, and corrected performance violations in 3-14 minutes--which compares very favorably with the time a human administrator would have needed. Our learning-based paradigm is a most promising way of deploying large-scale storage systems that service variable workloads on an ever-changing mix of device types.