Algorithm 1 Resource Predictive Load Balancing SFC Migration Algorithm (RP-LBM)
1	Input: Prediction result $c_{j,u}^{nf}\left(t+n\right), m_{j,u}^{nf}\left(t+n\right)\left(n=1,2,\dots \right);$ Physical network diagram $G=\left(V^s,E^s\right)$; SFC network diagram $G_i^{nf}=\left(V_i^{nf}, E_i^{nf}\right)$
2	Output: SFC mapping strategy $\pi$;
3	Calculate the resource utilization ${\eta }_R$ of each physical node according to the prediction result;
4	if ${\eta }_R \ge th{r}_{over}$ then
5	Select an SFC to migrate on that node;
6	Initialize $({\theta }_c, {\theta }_a),K_{max}$, M, ($({\epsilon }_c, {\epsilon }_a)$);
7	for episode = 1, …, M do
8	Select mapping actions $a\left(t\right)$ from the strategies $\pi \left(s_n\left(t\right)|a_n\left(t\right),{\theta }_a^n\right)$;
9	if constraints are satisfied then
10	Execute the action $a\left(t\right)$, get the instantaneous reward
11	r$\left(t\right)$ and transfer it to the state s$\left(t+1\right)$;
12	Obtain the advantage function $A\left(s_n\left(t\right),a_n\left(t\right)\right)$;
13	else
14	Set instantaneous reward r(t) = $-1/\epsilon$, and re-select the action $a\left(t\right)$ from the policy network;
15	end if
16	Compute the clipped surrogate objective for PPO
17	Update the policy parameters ${\theta }_a$ using gradient ascent
18	Update the value function parameters ${\theta }_c$ using gradient descent
19	end for
20	end if