Modeling Deployment Decisions for Elastic Services with ABS
The use of cloud technology can offer significant savings for the deployment of services, provided that the service is able to make efficient use of the available virtual resources to meet service-level requirements. To avoid software designs that scale poorly, it is important to make deployment decisions for the service at design time, early in the development of the service itself. ABS offers a formal, model-based approach which integrates the design of services with the modeling of deployment decisions. In this paper, we illustrate the main concepts of this approach by modeling a scalable pool of workers with an auto-scaling strategy and by using the model to compare deployment decisions with respect to client traffic with peak loads.
Analysis of SLA Compliance in the Cloud: An Automated, Model-based Approach
To develop tools for SLA compliance checking, we believe it is essential to work at the level of models: it is important to describe and analyze SLAs in a way which is independent of the concrete technology offered by the cloud service provider. Shifting to the modeling level increases the level of abstraction, reduces complexity, and removes dependency on a specific runtime environment.
The importance of models applies to SLAs as well as to software: a model-centric approach allows us to create a formal representation of the essential aspects of an SLA. At the same time, software deployed on the cloud can be represented as an executable model annotated with parametric expressions for the use of resources. Combining these models, we may now employ techniques with a formal basis, such as state-of-art static software analysis, to provide proven guarantees on service performance, thereby vastly raising the degree of automation.