Software product-lines (SPLs) are software architectures that can be readily reconfigured for different project requirements. A key part of an SPL is a model that captures the rules for reconfiguring the software. SPLs commonly use feature models to capture SPL configuration rules. Each SPL configuration is represented as a selection of features from the feature model. Invalid SPL configurations can be created due to feature conflicts introduced via staged or parallel configuration or changes to the constraints in a feature model. When invalid configurations are created, a method is needed to automate the diagnosis of the errors and repair the feature selections.This paper provides two contributions to research on automated configuration of SPLs. First, it shows how configurations and feature models can be transformed into constraint satisfaction problems to automatically diagnose errors and repair invalid feature selections. Second, it presents empirical results from diagnosing configuration errors in feature models ranging in size from 100 to 5,000 features. The results of our experiments show that our CSP-based diagnostic technique can scale up to models with thousands of features.
Cloud computing can reduce power consumption by using virtualized computational resources to provision an application's computational resources on-demand. Autoscaling 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 enviornments 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.
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