Communications of the ACM
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Rule-based versus structure-based models for explaining and generating expert behavior
Communications of the ACM
Machine Learning
A model based reasoning approach to network monitoring
CIKM '96 Proceedings of the workshop on Databases: active and real-time
Communications of the ACM
Minerva: An automated resource provisioning tool for large-scale storage systems
ACM Transactions on Computer Systems (TOCS)
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp
IEEE Software
Hippodrome: Running Circles Around Storage Administration
FAST '02 Proceedings of the Conference on File and Storage Technologies
Traveling to Rome: QoS Specifications for Automated Storage System Management
IWQoS '01 Proceedings of the 9th International Workshop on Quality of Service
IBM Storage Tank-- A heterogeneous scalable SAN file system
IBM Systems Journal
USTC'94 Proceedings of the USENIX Summer 1994 Technical Conference on USENIX Summer 1994 Technical Conference - Volume 1
ABLE: a toolkit for building multiagent autonomic systems
IBM Systems Journal
Differentiated storage services
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
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Policy-based storage management has been advertised as the silver bullet to overcome the complexity that limits the amount of storage that can be managed by system administrators. Key to this approach are: a mechanism to specify quality of service (QoS) goals; a canonical virtual model of storage devices and operations; and the mapping of the high level QoS goals to low level storage device actions. In spite of prior research and industrial standards the latter problem results in complex, manual, error-prone processes that burden system administrators and prevent the widespread acceptance of policy-based storage management. This paper proposes the Polus framework which specifically addresses this open problem. Polus removes the need for system administrators to write code that maps the QoS goals to low level system actions. Instead, it generates this mapping code by using a combination of rule-of-thumb specification mechanism, a reasoning engine and a learning engine to change the implementation paradigm of policy-based storage management. This paper also provides a quantitative analysis of the Polus framework within the context of a storage area network (SAN) file system to verify the feasibility of this new approach.