## 5 Simulation Results
### Task Offloading Evaluation  
To evaluate the offloading, we compare our proposed scheme ABSO with following task offloading strategies. * Local execution: All computational tasks are processed locally. * Full offloading: All computational tasks are executed on edge cloud by offloading. * Branch and Bound Algorithm (BBA) : The objective function is transformed into a question that is binary and linear, and it can be resolved availably by using a branch-and-bound algorithm. We observe the energy consumption of mobile devices and the completion time of tasks under various quantities of computation tasks in Figure 4. Obviously, we can see that the completion time and energy consumption will gradually increase as computational tasks increase. We can see that local execution consumes the most energy, because the CPU computation capacity of a mobile device is relatively small. Also, full offloading has higher completion time compared to others; this can mainly be attributed to the reason that full offloading needs to transmit data to the edge cloud before tasks can be processed. Moreover, it can be observed that the presented offloading strategy has better results compared to others on decreasing the expenditure of energy and time, and as the number of tasks increases, its advantages become more obvious. For the metric of the completion time, ABSO can achieve up-to 14% and 26%, and 7% performance improvement over the local execution, full offloading, and BBA, respectively. For the metric of the energy consumption, ABSO can reach up to 42% and 22%, and 9% decrement of energy compared to the local execution, full offloading, and BBA, respectively. Figure 5a depicts the influence of data size of tasks on the energy consumption. We can observe that our presented ABSO outperforms other methods. The energy consumption of local execution is relatively steady, and it is smaller than the value of full offloading as \(s_{k}\) increases. We can also see that as the data size of tasks increase, the energy consumption starts to increase. That's because with the data size of tasks rise, the communication time become larger, and the energy consumption of the edge cloud computing become larger than that of before. In the meantime, the difference of energy consumption between different strategies is subtle while the data size of tasks is small. Therefore, we infer that tasks with small amount of data are processed in the edge cloud when the other conditions of tasks are consistent. On the contrary, tasks would be supposed to processed locally. The energy consumption with the different number of CPU cycles \(w_{k}\) are shown in Figure 5b. According to the comparison, it can be seen that the energy consumption of our presented method ABSO is most optimal. It can be observed that the energy consumption of full offloading is relatively steady with little fluctuation. Also, as the increasing of required CPU cycles, the energy consumptionare getting larger. This is because that when the required CPU cycles of tasks rise, the energy consumption of local execution turns larger. From Figure 5b, we can conclude that in the case of the consistent amount data size of tasks, the needed CPU cycles is small and tasks ought to be processed locally. Conversely, the CPU cycles needed is large, tasks are transferred to the edge cloud for processing. Figure 4: Energy consumption of mobile devices and completion time of tasks under various quantities of computation tasks. Figure 5: Comparison of energy consumption for different offloading strategies. (**a**) Effect of task size on energy consumption; (**b**) Effect of required computation cycles on energy consumption; (**c**) Effect of latency deadline on energy consumption. In Figure 5c, we consider the energy consumption as the variance of latency requirements. The energy consumption of local computing and full offloading is little changed.