Challenges in managing dependable data systems
ACM SIGMETRICS Performance Evaluation Review - Design, implementation, and performance of storage systems
Towards self-predicting systems: What if you could ask ‘what-if’?
The Knowledge Engineering Review
Storage workload estimation for database management systems
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A new approach to dynamic self-tuning of database buffers
ACM Transactions on Storage (TOS)
Automatic virtual machine configuration for database workloads
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Automatic virtual machine configuration for database workloads
ACM Transactions on Database Systems (TODS)
Online monitoring and visualisation of database structural deterioration
International Journal of Autonomic Computing
A bayesian approach to online performance modeling for database appliances using gaussian models
Proceedings of the 8th ACM international conference on Autonomic computing
Using computer simulation to predict the performance of multithreaded programs
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
A workload-driven approach to database query processing in the cloud
The Journal of Supercomputing
A study of unpredictability in fault-tolerant middleware
Computer Networks: The International Journal of Computer and Telecommunications Networking
Workload management: a technology perspective with respect to self-* characteristics
Artificial Intelligence Review
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Administration tasks increasingly dominate the total cost of ownership of database management systems. A key task, and a very difficult one for an administrator, is to justify upgrades of CPU, memory and storage resources with quantitative predictions of the expected improvement in workload performance. Current database systems are not designed with such prediction in mind and hence offer only limited help to the administrator. This paper proposes changes to database system design that enable a Resource Advisor to answer "what-if" questions about resource upgrades. A prototype Resource Advisor built to work with a commercial DBMS shows the efficacy of our approach in predicting the effect of upgrading a key resource - buffer pool size - on OLTP workloads in a highly concurrent system.