Modeling Deployment Decisions for Elastic Services with ABS
This work addresses the challenge for cloud service developers to optimize resource usage and scalability early in development, though it appears incremental as it builds on existing ABS methodology.
The paper tackles the problem of making efficient deployment decisions for elastic cloud services at design time to meet service-level requirements, using the ABS formal modeling approach to compare deployment decisions under peak load traffic.
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.