How to jointly optimize task offloading and resource allocation to minimize the task failure rate and task payments remains an unresolved challenge in fog networks. Focusing on this problem, this research formulates a novel task offloading and resource allocation model with two offloading modes and on-demand virtual resource units (VRUs). This model is decomposed into two sub-problems to solve: a joint task offloading and resource allocation optimization problem and a matching problem with externalities and dynamics. First, for a given terminal node (TN) and fog node (FN), this research theoretically derives the optimal offloading ratio and resource allocation strategy to minimize the payment of TNs for two offloading modes, i.e., immediate and queued offloading. Second, in the multi-TNs and multi-FNs scenario, the problem of making the task offloading decision is transformed into a many-to-one matching game by considering externalities and dynamics. Finally, a Deferred acceptance-based Loss ratio and Payment Minimized task Offloading and resource Allocation optimization (DLPMOA) algorithm is proposed to derive a stable and Pareto-optimal match. The simulation results show that the proposed DLPMOA has better performance in terms of task failure rate, task average payment, fog computing resource utilization, and fairness than the state-of-the-art methods.

In the fog network composed of dense terminal devices and fog servers, how to reduce the system energy consumption during task offloading is a challenging problem. To solve this problem, this article first formulates the energy consumption minimization problem of partial task offloading under delay constraints with dynamic voltage scaling (DVS) and transmission power control (TPC) techniques. Second, this problem was decomposed into two subproblems to solve: 1) the partial task offloading problem with optimal energy consumption under known matching between the terminal device and fog server and 2) the optimal matching problem between terminal devices and fog servers. For the first subproblem, the optimal solution is obtained through theoretical derivation, and the EOPCO-S algorithm is proposed to solve it. For the second subproblem, we transform the original problem into a weighted bipartite graph matching problem and propose the Kuhn–Munkres-based EOPCO-M algorithm to solve it. Finally, numerical simulations are carried out to verify the theoretical derivation and the effectiveness of the proposed algorithms. Experimental results show that the proposed algorithm can significantly reduce the energy consumption of fog networks compared with several baseline algorithms.

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data offloading is studied where mobile date can be computed at both local devices and the edge cloud with the consideration of time-sensitive tasks for users. Since the NP-hardness of the considered optimization problem, we propose an iterative algorithm to decide the proportion of data to offload and design the resource allocation strategy in a sequence. Simulation results show that the proposed algorithm achieves better performance than the reference schemes.