Performance assertion checking
SOSP '93 Proceedings of the fourteenth ACM symposium on Operating systems principles
Machine Learning
Performance debugging for distributed systems of black boxes
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Storage Device Performance Prediction with CART Models
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Stardust: tracking activity in a distributed storage system
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Emergent (mis)behavior vs. complex software systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Ursa minor: versatile cluster-based storage
FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Pip: detecting the unexpected in distributed systems
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
Informed data distribution selection in a self-predicting storage system
ICAC '06 Proceedings of the 2006 IEEE International Conference on Autonomic Computing
An autonomic framework for enhancing the quality of data grid services
Future Generation Computer Systems
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To be effective for automation, in practice, system models used for performance prediction and behavior checking must be robust. They must be able to cope with component upgrades, misconfigurations, and workload-system interactions that were not anticipated. This paper promotes making models self-evolving, such that they continuously evaluate their accuracy and adjust their predictions accordingly. Such self-evaluation also enables confidence values to be provided with predictions, including identification of situations where no trustworthy prediction can be produced. With a combination of expectation-based and observation-based techniques, we believe that such self-evolving models can be achieved and used as a robust foundation for tuning, problem diagnosis, capacity planning, and administration tasks.