Start-time fair queueing: a scheduling algorithm for integrated services packet switching networks
IEEE/ACM Transactions on Networking (TON)
Scheduling for quality of service guarantees via service curves
ICCCN '95 Proceedings of the 4th International Conference on Computer Communications and Networks
Resource overbooking and application profiling in shared hosting platforms
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Façade: Virtual Storage Devices with Performance Guarantees
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Toward QoS Analysis of Adaptive Service-Oriented Architecture
SOSE '05 Proceedings of the IEEE International Workshop
Reliable QoS monitoring based on client feedback
Proceedings of the 16th international conference on World Wide Web
pClock: an arrival curve based approach for QoS guarantees in shared storage systems
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Workload decomposition for power efficient storage systems
HotPower'08 Proceedings of the 2008 conference on Power aware computing and systems
Nested QoS: providing flexible performance in shared IO environment
WIOV'11 Proceedings of the 3rd conference on I/O virtualization
A flexible approach to efficient resource sharing in virtualized environments
Proceedings of the 8th ACM International Conference on Computing Frontiers
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The growing popularity of hosted storage services and shared storage infrastructure in data centers is driving the recent interest in performance isolation and QoS in storage systems. Due to the bursty nature of storage workloads, meeting the traditional response-time Service Level Agreements requires significant over provisioning of the server capacity. We present a graduated, distribution-based QoS specification for storage servers that provides cost benefits over traditional QoS models. Our method RTT partitions the workload to minimize the capacity required to meet response time requirements of any specified fraction of the requests.