Algorithm 2 Algorithm to constraints matrix for Equation (21).

Input:  Number of server flows, L; Peak flow rate, $N_{\mathrm{\Lambda }}$; The upper bounds of $g\left[t\right]$, B; Number of PDU sessions, $N_L$; The probability distribution of $Ps$, ${\lambda }_{\mathit{l}}={[{\lambda }_l^0,{\lambda }_l^1,\cdots ,{\lambda }_l^{N_{\mathrm{\Lambda }}}]}^T$, $l=1,\cdots ,L$. Reliability function $F({\mathit{g}}_{\sigma },\gamma )$.  Output:  Reliability vector, $\mathit{R}$; PDU Session Aggregate Maximum Bit Rate vector $\mathit{W}$; Delay vector, ${\mathit{D}}_{\mu }$; Matrix for constraints, $\mathit{M}$. 1:${\mathit{g}}_{\sigma }\leftarrow 0$, ${\dot{\mathit{g}}}_{\sigma }\leftarrow {\mathbf{1}}_{\left|\mathcal{Z}\right|}\otimes {[0,1,\cdots ,B]}^T$, $W\leftarrow 0$;                    ▹ Generate $\mathit{R}$, $\mathit{W}$. 2:for $l=1$ to L do 3:   $\mathit{W}\leftarrow \mathit{W}\otimes {\mathbf{1}}_{\left|\mathcal{N}\right|\times \left|\mathcal{Z}\right|}$; 4:   ${\mathit{g}}_{\sigma }\leftarrow {\mathit{g}}_{\sigma }\otimes {\mathbf{1}}_{|\dot{\mathit{g}}|}+{\mathbf{1}}_{|{\mathit{g}}_{\sigma }|}\otimes {\dot{\mathit{g}}}_{\sigma }$; 5:end for 6:$\mathit{W}\leftarrow \mathit{W}\otimes {[0,1,\cdots ,B]}^T$, $\mathit{R}\leftarrow {\mathbf{0}}_{|{\mathit{g}}_{\sigma }|}\otimes {\mathbf{1}}_{|\Gamma |}$; 7:for$\left\{{\gamma }_{\upsilon \_i}^j\right\}$, $i=1$ to $2^{N_L}$ do 8:   $\mathit{R}\leftarrow \mathit{R}+F({\mathit{g}}_{\sigma },{\gamma }_{\upsilon \_i}^j)\otimes {\mathit{e}}_{|\Gamma |,i}$; 9:end for 10:${\mathit{D}}_{\mu }^{}\leftarrow 0$                        ▹Generate delay vector, ${\mathit{D}}_{\mu }$. 11:for $l=1$ to L do 12:   ${\mathit{D}}_l\leftarrow \frac{{\mu }_{L-l+1}}{{\lambda }_{l-l+1}}{[0,1,\cdots ,Z]}^T\otimes {\mathbf{1}}_{\left|\mathcal{N}\right|}$; 13:   ${\mathit{D}}_{\mu }^{}\leftarrow {\mathit{D}}_{\mu }^{}\otimes {\mathbf{1}}_{|{\mathit{D}}_l|}+{\mathbf{1}}_{|{\mathit{D}}_{\mu }^{}|}\otimes {\mathit{D}}_l$; 14:end for 15:$\mathit{D}\leftarrow \mathit{D}\otimes {\mathbf{1}}_{|\Gamma |}$; 16:$\dot{\mathit{M}}\leftarrow 1$, $\ddot{\mathit{M}}\leftarrow 1$, $\tilde{\mathit{M}}\leftarrow 1$;                         ▹ Generate delay vector, $\mathit{M}$. 17:for $l=1$ to L do 18:   for $q=1$ to Z do 19:     ${\dot{\mathit{M}}}_{l,q}\leftarrow {\mathbf{1}}_{\left|\mathcal{Z}\right|,\left|\mathcal{N}\right|}$, ${\ddot{\mathit{M}}}_{l,q}\leftarrow {\mathbf{1}}_{\left|\mathcal{Z}\right|,\left|\mathcal{N}\right|}$; 20:     for $\mathit{all}$ $g\in \mathcal{N}\left(q\right)$ do 21:        ${\mathit{M}}_{l,q,g}\leftarrow$$$\begin{gathered} \left[\begin{array}{c}{\mathbf{0}}_{q-g,N_{\mathrm{\Lambda }}+1}; \\ \mathrm{diag}\left({\mathbf{1}}_{1,N_{\mathrm{\Lambda }}+1}\right); \\ {\mathbf{0}}_{Z-N_{\mathrm{\Lambda }}+g-q,N_{\mathrm{\Lambda }}+1}\end{array}\right] \end{gathered}$$; 22:        ${\dot{\mathit{M}}}_{l,q}(:,g+1)\leftarrow {\mathit{M}}_{l,q,g}{\lambda }_{L+1-l}$; 23:        ${\ddot{\mathit{M}}}_{l,q}(:,g+1)\leftarrow {\mathit{M}}_{l,q,g}{\lambda }_{N_{\mathrm{\Lambda }}+1}$; 24:     end for 25:     ${\dot{\mathit{M}}}_l\leftarrow [{\dot{\mathit{M}}}_l,{\dot{\mathit{M}}}_{l,q}]$, ${\ddot{\mathit{M}}}_l\leftarrow [{\dot{\mathit{M}}}_l,{\ddot{\mathit{M}}}_{l,q}]$; 26:   end for 27:   $\dot{\mathit{M}}\leftarrow (\dot{\mathit{M}}\otimes {\ddot{\mathit{M}}}_l)(\ddot{\mathit{M}}\otimes {\dot{\mathit{M}}}_l)$, $\ddot{\mathit{M}}\leftarrow \ddot{\mathit{M}}\otimes {\ddot{\mathit{M}}}_l$; 28:   $\tilde{\mathit{M}}\leftarrow \tilde{\mathit{M}}\otimes (\mathrm{diag}\left({\left[{\mathbf{1}}_{\left|\mathcal{Z}\right|}\right]}^T\right)\otimes \left({\left[{\mathbf{1}}_{\left|\mathcal{N}\right|}\right]}^T\right))$ 29:end for 30:$\mathit{M}\leftarrow \dot{\mathit{M}}-\tilde{\mathit{M}}$