A Collaborative Computational Offloading Strategy for Latency-Sensitive Applications in Fog Networks

Network data traffic has expanded exponentially over the past decade, resulting in massive congestion in heterogeneous networks. Nevertheless, it is nearly impossible to run latency-intensive applications to the end-users local processing unit due to limited system resources. Recently, fog computing has come up with a solution to reduce such data congestion by offloading some or the whole part of the task to the nearby fog nodes (FNs) or the clouds. But this offloading policy becomes more complex when the FN is unable to process the task and further offload it to another neighboring FN or the cloud. In this context, in this study, we have analyzed the offloading strategy in a hierarchical fog-cloud network consisting of several heterogeneous fog devices along with a helping fog and a centralized cloud server. We have considered the most possible practical situation where the FNs are equipped with different CPU frequencies and hence, the power consumption is also different. The total system cost is formulated as a mixed-integer nonlinear problem that aims to reduce the overall delay in the proposed network. To solve the NP-hard problem, we transform it into quadratically constrained quadratic programming (QCQP) formation and further solve it by the separable semidefinite relaxation (SDR) method. Finally, by adopting several benchmark data, we conduct comprehensive simulations to test the efficiency of the proposed offloading profile. The simulation results depict that the proposed strategy outperforms in many aspects when compared to various baseline algorithms.