amount of data. The results of processing this massive data can be applied through peripheral actuators in the surrounding environment of users. On the other hand, users are more interested in running compute-intensive applications on their mobile devices in the minimum possible time and regardless of resource limitations. Due to resource limitations in ubiquitous and mobile environments, processing in a completely local manner is either impossible or very time-consuming. As a result, researchers propose running computational ubiquitous and mobile applications on clouds within a minimum possible time and without any resource limitations. This can be achieved by utilizing the elastic features of computational clouds and the offloading technique. The present research proposes a mechanism for computation offloading decision making, whose main purpose is to simultaneously reduce energy consumption and execution time in ubiquitous and mobile devices. Based on energy saving and execution time criteria and the consideration of some context-aware parameters, the proposed online mechanism performs the decision-making process for task offloading. Through simulation and the usage of real workload data, the current work evaluates the efficiency of the proposed offloading decision mechanism and the results show it to be satisfactory.

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