COSMOS: An Orchestration Framework for Smart Computation Offloading in Edge Clouds

The evolution of Internet of Things (IoT) has sparked significant research interest in edge computing. Within this scope and given the ever-increasing number of IoT and mobile devices, computation offloading is emerging as a cutting-edge and significant research area with enormous potential and practical applications.In this respect, we present the architecture design and experimental evaluation of an orchestration framework for smart computation offloading from IoT or mobile devices to edge cloud servers. The proposed orchestration platform, namely COSMOS, includes control-plane components for workload prediction, load balancing, and admission control. COSMOS is particularly tailored to the needs of an object identification service that receives images from a multitude of Points of Interest (PoIs), performs object identification using a trained model (based on Tensorflow), calculates the prediction accuracy, and finally returns to the end-users the identification outcome and accuracy along with useful information about the identified object. COSMOS has been deployed and evaluated in a large-scale experimental facility that employs OpenStack and OpenSourceMANO (OSM) for Network Function Virtualization (NFV) orchestration. Our experimental results indicate the feasibility of computation offloading for this object identification service and further uncover useful insights in terms of performance and scalability.