PA-Offload: Performability-Aware Adaptive Fog Offloading for Drone Image Processing

Abstract

Smart drone systems have built-in computing resources for processing real-world images captured by cameras to recognize their surroundings. Due to limited resources and battery constraints, resource-intensive image processing tasks cannot always run on drones. Thus, offloading computation tasks to any available node in a fog computing infrastructure can be considered as a promising solution. An important challenge when applying fog offloading is deciding when to start or stop offloading, taking into account performance and availability impacts under varying workloads and communication link states. In this paper, we present a performability-aware adaptive offloading scheme called PA-Offload that controls the offloading of image processing tasks from a drone to a fog node. To incorporate uncertainty factors, we introduce Stochastic Reward Nets (SRNs) to model the entire system behavior and compute a performability metric that is a composite measure of service throughput and system availability. The estimated performability value is then used to determine when to start or stop the offloading in order to make a better trade-off between performance and availability. Our numerical experiments show the effectiveness of PA-offload in terms of performability compared to non-adaptive fog offloading schemes.