A Deployment Framework for Quality-Sensitive Applications in Resource-Constrained Dynamic Environments

Traditional embedded systems and recent platforms used in emerging computing paradigms (e.g., fog computing) have resource limits and require their applications and services to be dynamically added (i.e., deployed) and removed at run-time. These applications often have non-functional (quality) requirements (e.g., end-to-end latency) which are only satisfied when sufficient resources are allocated to them. Hence, a run-time decision-maker is needed to optimize the deployments, in terms of resource budgets that are allocated to applications. Additionally, computing platforms have become heterogeneous in terms of their resources and the applications they execute. However, the existing deployment solutions are limited to specific resources and services. In this paper, we propose a run-time deployment framework that is more flexible in defining constraints and optimization goals and works with more heterogeneous resources and resource models than existing solutions. The framework is implemented on an embedded platform as a proof of concept.