A novel blockchain enabled resource allocation and task offloading strategy in cloud computing environment

Figures & data

Figure 1. The proposed system model.

Figure 2. The block-chain enabled IoT based smart sensor network architecture.

Figure 3. The process of generating offloading decisions.

Table

Input: task size $C_i,$ $i\in \{1,2,\dots N\}$

Output: the optimal value $x_i^{\ast },{\alpha }_i^{\ast },f_i^{\mathrm{loc}\ast }$ and minimum energy consumption $e^{\ast }$

1. Begin

2. Initialize variables $f_i^{\mathrm{loc}\ast },{\alpha }_i$;

3. Randomly input N tasks with size $c_i$;

4. Obtain 2N possible offloading actions;

5. Calculate the optimal offloading action $x^{\ast }$ by (34);

6. Input ($c_i,x_i^{\ast })$ to train DNN network, and update network parameters according to (37) and (38);

7. Obtain the optimal parameterized function $F_{\omega ,b}$;

8. Input random new task $c_i$ into the well trained DNN network (i.e. the parameterized function $F_{\omega ,b}$, and achieve the optimal offloading action $x_i^{\ast }$);

9. While constraints (28) to (29) are all satisfied and $j$ is within the maximum value; do

10. Update the $f_i^{\mathrm{loc}}$ and ${\alpha }_i$ by (42) based on accumulated momentum (43) and the gradient descent function (44);

11. $j=j+1$;

12. end while

13. Obtain, $f_i^{\mathrm{loc}\ast },{\alpha }_i,x_i^{\ast }$ and the minimum total energy consumption $e^{\ast }$ according to (45).

14. end

Figure 4. Offloading of computing tasks on the edge cloud with access control.

Figure 5. Total energy consumption of the proposed method under different maximum computing capabilities of local device.