Table 1.
Summary of literature.
| Ref. No. | Objectives | Node Provisioning for NSR Nodes |
Link Provisioning for NSR Nodes |
Physical Network | Resource | Security Issues |
|---|---|---|---|---|---|---|
| [17] | Slice acceptance ratio and revenue-to-cost ratio | Deterministic and random rounding | Deterministic and random rounding | Scale-free | Bandwidth and CPU capacity | Not considered |
| [19] | Acceptance ratio and resource efficiency | Acceptance ratio and resource efficiency | k shortest path Floyd algorithm | Scale-free | Bandwidth and CPU capacity | Not considered |
| [20] | Acceptance ratio and resource efficiency | Simulated annealing | Simulated annealing | Scale-free | Bandwidth and CPU capacity | Not considered |
| [21] | Acceptance ratio and resource efficiency | LAVA approach | LAVA approach | Scale-free | Bandwidth and CPU capacity | Not considered |
| [22] | Acceptance ratio and resource efficiency | GLL approach | GLL approach | Scale-free | Bandwidth and CPU capacity | Not considered |
| [23] | Slice acceptance ratio and revenue-to-cost ratio | Greedy node mapping | Dijkstra’s algorithm | Scale-free | Bandwidth and CPU capacity | Considered |
| [24] | Resource utilization and outage probability and resource efficiency | Markov decision process | Markov decision process | Scale-free NOMA | Power and subcarrier | Considered |
| [25] | Spectral efficiency and reliability | JSPA | JSPA | Scale-free OFDMA | Power and subcarrier | Not considered |
| [26] | Spectral efficiency and reliability | APSO | APSO | Scale-free OFDMA | Power and subcarrier | Not considered |
| [27] | Slice acceptance ratio and revenue-to-cost ratio | Node ranking using VIKOR | k shortest path algorithm | Scale-free | Bandwidth and CPU capacity | Considered |
| [28] | Slice classification accuracy | DBN and NN, GS-DHOA for weight function adjustments | DBN and NN, GS-DHOA for weight function adjustments | - | Performance dataset consists of network features | Considered |
| [29] | Latency and training loss | GNN model with DT | GNN model with DT | - | Performance dataset consists of network features | Considered |
| Proposed | Acceptance ratio and resource efficiency | Node ranking using PROMETHEE II | SPA formation through Dijkstra’s algorithm | Small-world network and scale-free network | Bandwidth and CPU capacity | Considered |