LAUSR

In recent times, fog computing becomes an emerging technology that can exhilarate the cloud services towards the network edge for increasing the speeds up of various Internet-of-Things (IoT) applications. In this context, integrating priority-aware scheduling and data offloading allow the service providers to efficiently handle a large number of real-time IoT applications and enhance the capability of the fog networks. But the energy consumption has become skyrocketing, and it gravely affects the performance of the fog networks. To address this issue, in this paper, we introduce an Energy-Efficient Task Offloading (EETO) policy combined with a hierarchical fog network for handling energy-performance trade-off by jointly scheduling and offloading the real-time IoT applications. To achieve this objective, we formulate a heuristic technique for assigning a priority on each incoming task and formulate a stochastic-aware data offloading issue with an efficient virtual queue stability approach, namely the Lyapunov optimization technique. The proposed technique utilizes the current state information for minimizing the queue waiting time and overall energy consumption while meeting drift-plus-penalty. Furthermore, a constraint restricted progressive online task offloading policy is incurred to mitigate the backlog tasks of the queues. Extensive simulation with various Quality-of-Service (QoS) parameters signifies that the proposed EETO mechanism performs better and saves about 23.79% of the energy usage as compared to the existing ones.