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Availability Modeling for Drone Image Processing Systems with Adaptive Offloading

Availability of a computing process running on a flying drone is an essential quality aspect for mission-critical drone systems. Computing tasks such as image processing tasks can be lost when the process encounters a failure. Since the failure probability of the process depends on workload intensities, reducing drone workloads by computation offloading or load-balancing must have impacts on the system availability. While many existing studies discuss the performance-cost tradeoff associated with computation offloading, potential impacts on the system availability have not been deeply investigated yet. In this paper, we propose stochastic models for estimating the availability, the performance, and the energy consumption of a drone system with image processing tasks that can be either offloaded to a fog node or distributed to a collaborative drone. Our comprehensive numerical analysis with the proposed model clarifies the trade-offs among the availability, the throughputs, and the energy consumption under different computation modes. Furthermore, we propose an adaptive offloading scheme that can change the computation modes dynamically according to workload intensities and network conditions. A simulation study with a phased mission scenario shows that the proposed adaptive scheme can achieve high availability with 26% of energy reduction and less than 4% of throughput losses.