Resource Inference for Sustainable and Responsive Task Offloading in Challenged Edge Networks

In many edge computing applications, Unmanned Aerial Vehicles (UAVs) are required to be coordinated to perform several tasks. Each task is usually modeled as a process that a UAV runs, and could include hovering an area to find survivors after a natural disaster or sense and preprocess an image in cooperation with the edge cloud. Optimally and rapidly (re)assigning tasks to such IoT agents as the network conditions fluctuate and the battery of these agents quickly drains is a challenging problem. Existing solutions designed to proactively offload tasks are either energy unaware or they require solving computationally intensive task, and hence are less portable on constrained IoT devices. In this paper, we propose RITMO, a distributed and adaptive task offloading algorithm that aims to solve these challenges. RITMO exploits a simple yet effective regressor to dynamically predict the length of future UAV task queues. Such prediction is then used to anticipate the node overloading and avoid agents that are likely to exhaust their battery or their computational resources. Our results demonstrate how RITMO helps reduce the overall latency perceived by the application and the energy consumed by the nodes, outperforming recent solutions.