METO: Matching-Theory-Based Efficient Task Offloading in IoT-Fog Interconnection Networks

Typical cloud systems are often prone to inherent wide area network (WAN) latency. To address this issue fog computing is proposed that enables resource-constrained Internet-of-Things (IoT) devices, to execute deadline-sensitive tasks at the edge of the network. These devices can extend their battery lifespan by intelligently offloading computations as tasks to fog nodes (FNs) in their vicinity. However, finding an optimal offloading plan in a densely connected IoT-fog network is proven to be \mathcal {NP} -Hard. Hence, in this article, we propose a matching theory-based efficient task offloading strategy called METO that aims to reduce the total system energy and number of outages (number of tasks exceeding the deadline) in an IoT-fog interconnection network. As resource allocation involves multiple criteria, their weights are derived using criteria importance though inter criteria correlation (CRITIC). Furthermore, to rank the alternatives we use the technique for order of preference by similarity to ideal solution (TOPSIS). Based on this ranking, we formulate the overall offloading problem as a one-to-many matching game and utilize the deferred acceptance algorithm (DAA) to produce a stable assignment. Simulation is performed in two different settings comprising offloading of homogeneous and heterogeneous tasks. Extensive simulations across both environments confirm that the proposed algorithm outperforms the existing schemes with respect to improved energy consumption, completion time, and execution time. Moreover, METO also shows the reduced number of outages across baselines used for comparison.