An analytical model for multi-tier internet services and its applications
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Dynamic Provisioning of Multi-tier Internet Applications
ICAC '05 Proceedings of the Second International Conference on Automatic Computing
Capturing, indexing, clustering, and retrieving system history
Proceedings of the twentieth ACM symposium on Operating systems principles
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Exploiting nonstationarity for performance prediction
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Adaptive control of virtualized resources in utility computing environments
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Burstiness in multi-tier applications: symptoms, causes, and new models
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Automated control of multiple virtualized resources
Proceedings of the 4th ACM European conference on Computer systems
Injecting realistic burstiness to a traditional client-server benchmark
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
A cost-sensitive adaptation engine for server consolidation of multitier applications
Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware
Experimental evaluation of N-tier systems: Observation and analysis of multi-bottlenecks
IISWC '09 Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC)
CloudXplor: a tool for configuration planning in clouds based on empirical data
Proceedings of the 2010 ACM Symposium on Applied Computing
Proceedings of the 2010 ACM Symposium on Applied Computing
Automated control for elastic storage
Proceedings of the 7th international conference on Autonomic computing
Intelligent management of virtualized resources for database systems in cloud environment
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Automatic elasticity in OpenStack
Proceedings of the Workshop on Secure and Dependable Middleware for Cloud Monitoring and Management
On estimating actuation delays in elastic computing systems
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
ElastMan: elasticity manager for elastic key-value stores in the cloud
Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference
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Elastic n-tier applications have non-stationary workloads that require adaptive control of resources allocated to them. This presents not only an opportunity in pay-as-you-use clouds, but also a challenge to dynamically allocate virtual machines appropriately. Previous approaches based on control theory, queuing networks, and machine learning work well for some situations, but each model has its own limitations due to inaccuracies in performance prediction. In this paper we propose a multi-model controller, which integrates adaptation decisions from several models, choosing the best. The focus of our work is an empirical model, based on detailed measurement data from previous application runs. The main advantage of the empirical model is that it returns high quality performance predictions based on measured data. For new application scenarios, we use other models or heuristics as a starting point, and all performance data are continuously incorporated into the empirical model's knowledge base. Using a prototype implementation of the multi-model controller, a cloud testbed, and an n-tier benchmark (RUBBoS), we evaluated and validated the advantages of the empirical model. For example, measured data show that it is more effective to add two nodes as a group, one for each tier, when two tiers approach saturation simultaneously.