Disk and file management tasks on HP-UX
Disk and file management tasks on HP-UX
A feedback-driven proportion allocator for real-rate scheduling
OSDI '99 Proceedings of the third symposium on Operating systems design and implementation
Progress-based regulation of low-importance processes
Proceedings of the seventeenth ACM symposium on Operating systems principles
Minerva: An automated resource provisioning tool for large-scale storage systems
ACM Transactions on Computer Systems (TOCS)
Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
An Automated Profiling Subsystem for QoS-Aware Services
RTAS '00 Proceedings of the Sixth IEEE Real Time Technology and Applications Symposium (RTAS 2000)
Differentiated Caching Services; A Control-Theoretical Approach
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
A Feedback Control Approach for Guaranteeing Relative Delays in Web Servers
RTAS '01 Proceedings of the Seventh Real-Time Technology and Applications Symposium (RTAS '01)
A control-based middleware framework for quality-of-service adaptations
IEEE Journal on Selected Areas in Communications
YouChoose: Choosing your Storage Device as a Performance Interface to Consolidated I/O Service
ACM Transactions on Storage (TOS)
The Yahoo!: cloud datastore load balancer
Proceedings of the fourth international workshop on Cloud data management
ElastMan: elasticity manager for elastic key-value stores in the cloud
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
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Modern computer systems are expected to be up continuously: even planned downtime to accomplish system reconfiguration is becoming unacceptable, so more and more changes are having to be made to "live" systems that are running production workloads. One of those changes is data migration: moving data from one storage device to another for load balancing, system expansion, failure recovery, or a myriad of other reasons. Traditional methods for achieving this either require application down-time, or severely impact the performance of foreground applications - neither a good outcome when performance predictability is almost as important as raw speed. Our solution to this problem, Aqueduct, uses a control-theoretical approach to statistically guarantee a bound on the amount of impact on foreground work during a data migration, while still accomplishing the data migration in as short a time as possible. The result is better quality of service for the end users, less stress for the system administrators, and systems that can be adapted more readily to meet changing demands.