Automated Diagnosis of Product-Line Configuration Errors in Feature Models
SPLC '08 Proceedings of the 2008 12th International Software Product Line Conference
Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology
SnowFlock: rapid virtual machine cloning for cloud computing
Proceedings of the 4th ACM European conference on Computer systems
GreenCloud: a new architecture for green data center
ICAC-INDST '09 Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session
Bioinformatics
The Eucalyptus Open-Source Cloud-Computing System
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Selecting highly optimal architectural feature sets with Filtered Cartesian Flattening
Journal of Systems and Software
Automated reasoning for multi-step feature model configuration problems
Proceedings of the 13th International Software Product Line Conference
Energy Efficient Allocation of Virtual Machines in Cloud Data Centers
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Energy-Efficient Cloud Computing
The Computer Journal
Automated reasoning on feature models
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
Editorial: Special section: Green computing
Future Generation Computer Systems
A model-driven approach for virtual machine image provisioning in cloud computing
ESOCC'12 Proceedings of the First European conference on Service-Oriented and Cloud Computing
Towards multi-cloud configurations using feature models and ontologies
Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds
A model view controller based Self-Adjusting Clustering Framework
Journal of Systems and Software
Hi-index | 0.00 |
Cloud computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on demand. Auto-scaling is an important cloud computing technique that dynamically allocates computational resources to applications to match their current loads precisely, thereby removing resources that would otherwise remain idle and waste power. This paper presents a model-driven engineering approach to optimizing the configuration, energy consumption, and operating cost of cloud auto-scaling infrastructure to create greener computing environments that reduce emissions resulting from superfluous idle resources. The paper provides four contributions to the study of model-driven configuration of cloud auto-scaling infrastructure by (1) explaining how virtual machine configurations can be captured in feature models, (2) describing how these models can be transformed into constraint satisfaction problems (CSPs) for configuration and energy consumption optimization, (3) showing how optimal auto-scaling configurations can be derived from these CSPs with a constraint solver, and (4) presenting a case study showing the energy consumption/cost reduction produced by this model-driven approach.