Input/output access pattern classification using hidden Markov models
Proceedings of the fifth workshop on I/O in parallel and distributed systems
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Trace-based analyses and optimizations for network storage servers
Trace-based analyses and optimizations for network storage servers
Passive NFS Tracing of Email and Research Workloads
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Disk drive level workload characterization
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Measurement and analysis of large-scale network file system workloads
ATC'08 USENIX 2008 Annual Technical Conference on Annual Technical Conference
Characterization of the E-commerce Storage Subsystem Workload
QEST '08 Proceedings of the 2008 Fifth International Conference on Quantitative Evaluation of Systems
Capture, conversion, and analysis of an intense NFS workload
FAST '09 Proccedings of the 7th conference on File and storage technologies
BORG: block-reORGanization for self-optimizing storage systems
FAST '09 Proccedings of the 7th conference on File and storage technologies
Learning kernels from indefinite similarities
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Discovery of application workloads from network file traces
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
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Storage infrastructure in large-scale cloud data center environments must support applications with diverse, time-varying data access patterns while observing the quality of service. Deeper storage hierarchies induced by solid state and rotating media are enabling new storage management tradeoffs that do not apply uniformly to all application phases at all times. To meet service level requirements in such heterogeneous application phases, storage management needs to be phase-aware and adaptive, i.e., to identify specific storage access patterns of applications as they occur and customize their handling accordingly. This paper presents LoadIQ, a novel, versatile, adaptive, application phase detector for networked (file and block) storage systems. In a live deployment, LoadIQ analyzes traces and emits phase labels learnt on the fly by using Support Vector Machines(SVM), a state of the art classifier. Such labels could be used to generate alerts or to trigger phase-specific system tuning. Our results show that LoadIQ is able to identify workload phases (such as in TPC-DS) with accuracy 93%.