ARC: A Self-Tuning, Low Overhead Replacement Cache
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
CacheCOW: QoS for storage system caches
IWQoS'03 Proceedings of the 11th international conference on Quality of service
On multi-level exclusive caching: offline optimality and why promotions are better than demotions
FAST'08 Proceedings of the 6th USENIX Conference on File and Storage Technologies
Dynamic storage cache allocation in multi-server architectures
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
SAIL: self-adaptive file reallocation on hybrid disk arrays
HiPC'08 Proceedings of the 15th international conference on High performance computing
Adaptive multi-level cache allocation in distributed storage architectures
Proceedings of the 24th ACM International Conference on Supercomputing
Computation mapping for multi-level storage cache hierarchies
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
QoS aware storage cache management in multi-server environments
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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Managed hosting and enterprise wide resource consolidation trends are increasingly leading to sharing of storage resources across multiple classes, corresponding to different applications/customers, each with a possibly different Quality of Service (QoS) requirement. To enable a storage system to meet diverse QoS requirements, we present two algorithms for dynamically allocating cache space among multiple classes of workloads. Our algorithms dynamically adapt the cache space allocated to each class in response to the observed response time, the temporal locality of reference, and the arrival pattern for each class. Using trace driven simulations collected from large storage system installations, we experimentally demonstrate that the algorithms not only meet the QoS requirements, but also increase the throughput by achieving a higher hit rate whenever feasible.