Abstract: Edge computing, extending computing resources and enhancing storage capacity for resource-constrained Internet of Things (IoT) devices, can improve the performance of IoT applications. In an IoT environment, most applications will be deployed at multi-sites in a distributed architecture, and these sites will need to collaborate to finish a service. In order to solve the cost optimization problem of multi-site collaborative computation in the IoT environment, a genetic-based multi-site collaborative computation offloading algorithm (GAMCCO) is proposed. The algorithm models the application into a task relation graph and analyzes the dependencies among tasks. Afterwards, the multi-site collaborative offloading problem is formulated in terms of execution cost, and genetic algorithm is used to find the best offloading scheme. Experimental and evaluation results show that the proposed GAMCCO algorithm can effectively reduce the delay of IoT applications and the energy consumption of terminal devices.

Key words: Internet of Things, edge computing, computation offloading, genetic algorithm, multi-site