A Multi-Stage Stochastic Programming-Based Offloading Policy for Fog Enabled IoT-eHealth

To meet low latency and real-time monitoring demands of IoT-eHealth, fog computing is envisioned as a key technology to offer elastic computing resource at the edge of networks. In this context, eHealth devices can offload collected healthcare data or computational expensive tasks to a nearby fog server. However, the mobility of the eHealth devices may make the connection between them to fog servers uncertain, resulting in possible migration between fog servers. In order to evaluate the impact of this uncertainty on decision-making for offloading and resource allocation, we formulate the task offloading problem as a Multi-Stage Stochastic Programming (MSSP), with aim of minimizing the total latency of offloading to determine whether to offload or not, how much workload to offload, how much computing resource to allocate, as well as whether to migrate or not. Different from the previous MSSP based work focusing on the workload assignment only, the proposed MSSP examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions. Furthermore, to reduce the computational complexity of MSSP, we design an efficient sub-optimal offloading policy based on Sample Average Approximation, called SAA-MSSP. We conduct extensive simulation experiments to validate the effectiveness of SAA-MSSP. The results show that SAA-MSSP can converge to a near-optimal solution quickly.

Automated summary

This study proposes a Multi-Stage Stochastic Programming model with the aim of minimizing the total latency of offloading, evaluating the impact of uncertainty on decision-making for offloading and resource allocation. The proposed model examines joint decisions of offloading, resource allocation, and migration, advancing the understanding of the interactions among these decisions.