Resource Inference for Task Migration in Challenged Edge Networks with RITMO

Abstract

Edge computing, combined with the proliferation of IoT devices, is generating new business model opportunities and applications. Among those applications, Unmanned Aerial Vehicles (UAVs) have been deployed in several scenarios, from surveillance and monitoring to disaster response, to precision agriculture. To support such applications, however, edge network managers and application programmers need to overcome a few challenges, e.g., unstable network conditions, high loss rate, and node failures. Existing solutions designed to mitigate such inefficiencies by predicting future network conditions are often computationally intensive and hence less portable on constrained devices. In this paper, we propose RITMO, a distributed and adaptive task planning algorithm that aims at solving these challenges while running on a network of UAV devices. We model our system as a network of queues, and we exploit a simple yet effective ARIMA regressor, to dynamically predict the length of future UAV task queues. Such prediction is then used to proactively migrate the tasks in case of a failure or unbalanced loads. Our simulation results demonstrate how RITMO helps to reduce the overall latency perceived by the application and anticipates the node overloading by avoiding agents that are likely to exhaust their computational resources.