Nowadays notable computing is shifted away from the cloud and performed onto the Internet of Things (IoT) devices. This necessity emerges due to the growing needs not only for real-time decision support but also for real-time data processing. When used in critical applications such as search and rescue missions or monitoring and control of critical infrastructure, the overall reliable operation of the application running on these devices becomes a major challenge, especially as system reliability is an application-and h/w-dependent measure. Moreover, performance and energy are typically constrained and vary depending on where the computation takes place, as well as, on the communication channels between the devices. Hence, the problem of task allocation under reliabilityperformance-energy constraints becomes even more complex in such cloud/hub/edge computing paradigms. In this work, we use a mathematical programming based framework to derive an optimal task allocation based on multiple operational constraints (latency and energy in both computation and communication), while taking into consideration the reliability demands of the application. We consider an architecture consisting of an edge node, an intermediate node (hub), and the cloud infrastructure, and evaluate our approach using a real-life use-case where the proposed framework minimizes the overall latency of the application while considering the reliability demands of each executed task. Configuration B Configuration C Configuration D Latency (ms) Edge Hub Cloud Edge-> Hub Hub-> Edge Hub-> Cloud Cloud-> Hub

Shifting tasks from the cloud to the edge Internet of Things (IoT) devices facilitates real-time decision support, yielding rise to the emerging edge-hub-cloud computing architecture. However, as edge devices tend to be more vulnerable to errors due to environmental/technological factors, the reliability of the application can be compromised due to this shift. We propose an optimization and design exploration framework for task allocation which maximizes reliability while respecting latency and energy constraints in computation and communication tasks, as well as memory computation constraints. It considers varying tasks' vulnerability factors, per possible executing device, and allows for different task re-execution reliability approaches based on full or selective dual or triple re-execution of tasks. We validate and showcase the effectiveness of our approach through a real-life case study for power tower/line inspection using UAVs (Unmanned Aerial Vehicles). Evaluation results show that our framework is capable of optimizing the overall reliability of the targeted application while satisfying strict latency, energy, and memory constraints, allowing the designer to explore different reliability approaches in a short period of time.

Increased demands for real-time decision support and data analytics facilitate the need of performing significant computing away from the cloud and onto the IoT devices. In this paper, we propose Metis, a mathematical programming based framework, able to deliver an optimal task allocation when targeting a specific performance metric. Metis is currently suitable for systems which consist of an edge node, an intermediate node and the cloud infrastructure, but can be expanded to multi-Edge/Hub systems. Evaluation results using a real-life use-case scenario demonstrate that Metis provides the optimal task allocation by minimizing the overall latency of the system while taking into consideration the application's specific requirements and resource constraints of each computational unit.