With the emergence of many computation-intensive applications, mobile devices cannot meet user requirements such as delay and energy consumption due to their limited computing capabilities. Mobile Edge Computing (MEC) offloads user task computing to the MEC server through a wireless channel to significantly reduce the response delay and energy consumption of tasks. Concerning the problem of multi-user task offloading, a Multi-User Task Offloading strategy based on Stable Allocation (MUTOSA) was proposed to minimize energy consumption while ensuring the user delay requirement. Firstly, based on the comprehensive consideration of delay and energy consumption, the problem of multi-user task offloading in the independent task scenario was modeled. Then, based on the idea of delayed reception in the stable allocation of game theory, an adjustment strategy was proposed. Finally, the problem of multi-user task unloading was solved through continuous iteration. Experimental results show that, compared with the benchmark strategy and heuristic strategy, the proposed strategy can meet the delay requirements of more users, increase user satisfaction by about 10% on average, and reduce the total energy consumption of user devices by about 50%. It shows that the proposed strategy can effectively reduce energy consumption with ensuring the user delay requirement, and can effectively improve the user experience for delay-sensitive applications.

Classical regression algorithms for data set analysis of multiple models have the defects of long calculating time and low detecting accuracy of models. Therefore, a heuristic robust regression analysis method was proposed. This method mimicked the clustering principle of immune system. The B cell network was taken as classifier of data set and memory of model set. Conformity between data and model was used as the classification criteria, which improved the accuracy of the data classification. The extraction process of model set was divided into a parallel iterative trial including clustering, regressing and clustering again, by which the solution of model set was gradually approximated to. The simulation results show that the proposed algorithm needs obviously less calculating time and it has higher detecting accuracy of models than classical ones. According to the results of the eight-model data set analysis in this paper, among the classical algorithms, the best algorithm is the successive extraction algorithm based on Random Sample Consensus (RANSAC). Its mean model detecting accuracy is 90.37% and the calculating time is 53.3947s. The detecting accuracy of those classical algorithms which calculating time is below 0.5s is bellow 1%. By the contrary, the proposed algorithm needs only 0.5094s and its detecting accuracy is 98.25%.