C4.5: programs for machine learning
C4.5: programs for machine learning
On-line extraction of SCSI disk drive parameters
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Temporally determinate disk access (extended abstract): an experimental approach
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Active disks: programming model, algorithms and evaluation
Proceedings of the eighth international conference on Architectural support for programming languages and operating systems
Virtual log based file systems for a programmable disk
OSDI '99 Proceedings of the third symposium on Operating systems design and implementation
Techniques to increase disk access locality in the Minorca multimedia file system
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Data mining on an OLTP system (nearly) for free
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
IEEE Spectrum - Linking with light
Track-Aligned Extents: Matching Access Patterns to Disk Drive Characteristics
FAST '02 Proceedings of the Conference on File and Storage Technologies
Freeblock Scheduling Outside of Disk Firmware
FAST '02 Proceedings of the Conference on File and Storage Technologies
Power Constraints: Another Dimension of Complexity in Continuous Media Playback
IDMS/PROMS 2002 Proceedings of the Joint International Workshops on Interactive Distributed Multimedia Systems and Protocols for Multimedia Systems: Protocols and Systems for Interactive Distributed Multimedia
Active Storage for Large-Scale Data Mining and Multimedia
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Exploiting Gray-Box Knowledge of Buffer-Cache Management
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Cooperative I/O: a novel I/O semantics for energy-aware applications
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Implementation and Evaluation of a Multimedia File System
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
QoS Provisioning Framework for an OSD-Based Storage System
MSST '05 Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies
Understanding The Linux Kernel
Understanding The Linux Kernel
File Classification in Self-* Storage Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
RepStore: A Self-Managing and Self-Tuning Storage Backend with Smart Bricks
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Dynamic Black-Box Performance Model Estimation for Self-Tuning Regulators
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Towards Self-Configuring Hardware for Distributed Computer Systems
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Semantically-Smart Disk Systems
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Design and Implementation of Semi-preemptible IO
FAST '03 Proceedings of the 2nd USENIX Conference on File and Storage Technologies
Diamond: A Storage Architecture for Early Discard in Interactive Search
FAST '04 Proceedings of the 3rd USENIX Conference on File and Storage Technologies
C-Miner: Mining Block Correlations in Storage Systems
FAST '04 Proceedings of the 3rd USENIX Conference on File and Storage Technologies
Apollon: file system level support for qos augmented i/o
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Exploiting idle CPU cores to improve file access performance
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
NCQ vs. I/O scheduler: Preventing unexpected misbehaviors
ACM Transactions on Storage (TOS)
Extract and infer quickly: Obtaining sector geometry of modern hard disk drives
ACM Transactions on Storage (TOS)
ACM Transactions on Storage (TOS)
Storage QoS provisioning for execution programming of data-intensive applications
Scientific Programming - Biological Knowledge Discovery and Data Mining
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In this work, we develop an intelligent storage system framework for soft real-time applications. Modern software systems consist of a collection of layers and information exchange across the layers is performed via well-defined interfaces. Due to the strictness and inflexibility of interface definition, it is not possible to pass the information specific to one layer to other layers. In practice, the exploitation of this information across the layers can greatly enhance the performance, reliability, and manageability of the system. We address the limitation of legacy interface definition via enabling intelligence in the storage system. The objective is to enable the lower-layer entity, for example, a physical or block device, to conjecture the semantic and contextual information of that application behavior which cannot be passed via the legacy interface. Based upon the knowledge obtained by the intelligence module, the system can perform a number of actions to improve the performance, reliability, security, and manageability of the system. Our intelligence storage system focuses on optimizing the I/O subsystem performance for a soft real-time application. Our intelligence framework consists of three components: the workload monitor, workload analyzer, and system optimizer. The workload monitor maintains a window of recent I/O requests and extracts feature vectors in regular intervals. The workload analyzer is trained to determine the class of the incoming workload by using the feature vector. The system optimizer performs various actions to tune the storage system for a given workload. We use confidence rate boosting to train the workload analyzer. This sophisticated learner achieves a higher than 97% accuracy of workload class prediction. We develop a prototype intelligence storage system on the legacy operating system platform. The system optimizer performs; (1) dynamic adjustment of the file-system-level read-ahead size; (2) dynamic adjustment of I/O request size; and (3) filtering of I/O requests. We examine the effect of this autonomic optimization via experimentation. We find that the storage level pro-active optimization greatly enhances the efficiency of the underlying storage system. The sophisticated intelligence module developed in this work does not restrict its usage for performance optimization. It can be effectively used as classification engine for generic autonomic computing environment, i.e. management, diagnosis, security and etc.