Energy-delay co-optimization of resource allocation for robotic services in cloudlet infrastructure

Abstract

Cloud based robotic services can be adopted for emergency management in smart factory. When multiple robots work collaboratively in such system, optimal resource allocation for executing the tasks of robotic services becomes a challenging problem due to the heterogeneity and energy consumption of resources. Since the tasks of multi-robotic services are inter-dependent, the inconvenience of data exchange between local robots and distant Cloud can significantly degrade the quality of service. Therefore, in this paper, we jointly address the energy consumption and service delay minimization problem while allocating resources in proximate Cloud (Cloudlet) based multi-robot systems for emergency management service in smart factory. A multi-objective evolutionary approach, NSGA-II algorithm is applied to solve this constrained multi-objective optimization problem. We augment the NSGA-II algorithm by defining a new chromosome structure, presorted initial population, mutation operator and selection of minimum distant solution from the non-dominated front to the origin while crossing over the chromosomes. The experimental results on the basis of synthetic data demonstrate that our approach performs significantly well compared to benchmark NSGA-II.