A Genetic Algorithm-based Approach to Dynamic Architectural Deployment

Abstract

Increasing demands for various and complex tasks on contemporary computing systems require the precise deployment of components that perform the tasks. For example, in service robot systems (Hans et al., 2002; Kim et al., 2008) that have several SBCs (single board computers), users may simultaneously request several tasks such as locomotion, speech recognition, human-following, and TTS (text-to-speech). Each task comprises a set of components that are organized by an architectural configuration. These components execute their own functionality to provide services to the user. To execute components, they must be deployed into computing units that have computing power, such as desktops, laptops, and embedded computing units. The deployment of components into computing units can influence the performance of tasks. If the system has only one computing unit, every component is deployed in the computing unit and there is no option to vary the deployment to improve the performance. On the other hand, if the system has multiple computing units, performance improvement by varying the deployment can be considered. Different instances of component deployment show different performance results because the resources of the computing units are different. Concentrated deployment into a certain computing unit may lead to resource contention and delayed execution problems. Therefore, the system requires an deployment method to improve performance when the user requests multiple tasks of a system that has multiple computing units. When determining the deployment of components that comprise the architectural configuration for the tasks, it is important to rapidly and precisely make a decision about deployment. Since there are a large number of candidate deployment instances, even for a small number of computing units and components (i.e., their combinations exponentially increase), the deployment instance selection method must efficiently search for the best deployment instance that provides the most effective performance to the user. The exhaustive search method guarantees to search the best instance; however, it requires a long time for performing search. The greedy search method rapidly finds a solution; however, it does not guarantee to search the best instance. This study proposes a genetic algorithm-based selection method that searches a set of candidate deployment instances for an optimal instance. This method repeatedly produces generations, and the solution found by the method rapidly converges to the best instance. This method more rapidly and precisely searches an optimal instance than the exhaustive search method and the greedy search method, respectively.