A method for transparent admission control and request scheduling in e-commerce web sites
Proceedings of the 13th international conference on World Wide Web
Resource overbooking and application profiling in shared hosting platforms
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Profitable services in an uncertain world
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
How to Determine a Good Multi-Programming Level for External Scheduling
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Automatic virtual machine configuration for database workloads
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Multi-tenant databases for software as a service: schema-mapping techniques
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Modeling and exploiting query interactions in database systems
Proceedings of the 17th ACM conference on Information and knowledge management
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Tuning database configuration parameters with iTuned
Proceedings of the VLDB Endowment
Intelligent management of virtualized resources for database systems in cloud environment
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
ActiveSLA: a profit-oriented admission control framework for database-as-a-service providers
Proceedings of the 2nd ACM Symposium on Cloud Computing
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As cloud computing environments become explosively popular, dealing with unpredictable changes, uncertainties, and disturbances in both systems and environments turns out to be one of the major challenges facing the concurrent computing industry. My research goal is to dynamically manage resources and workloads for RDBMS in cloud computing environments in order to achieve ``better performance but lower cost", i.e., better service level compliance but lower consumption of virtualized computing resource(s). Nowadays, although control theory offers a principled way to deal with the challenge based on feedback mechanisms, a controller is typically designed based on the system designer's domain knowledge and intuition instead of the behavior of the system being controlled. My research approach is based on the essence of control theory but transcends state-of-the-art control-theoretic approaches by leveraging interdisciplinary areas, especially from machine learning. While machine learning is often viewed merely as a toolbox that can be deployed for many data-centric problems, my research makes efforts to incorporate machine learning as a full-fledged engineering discipline into control-theoretic approaches for realizing my research goal. My PhD thesis work implements two solid systems by leveraging machine learning techniques, namely, ActiveSLA and SmartSLA. ActiveSLA is an automatic controller featuring risk assessment admission control to obtain the most profitable service-level compliance. SmartSLA is an automatic controller featuring cost-sensitive adaptation to achieve the lowest total cost. The experimental results show that both of the two systems outperform the state-of-the-art methods.