Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEEII and SLE Strategy
Abstract
:1. Introduction
 A mathematical model is introduced for network slicing that takes operational constraints and security considerations into account when assigning nodes and connecting them.
 The prepared node importance rank array (NIRA) was used to allocate NSR nodes in physical and logical networks, and the SLE approach was utilized to connect the network slice request (NSR) nodes. NIRA preparation takes into account node information such as the node capacity, nearby line bandwidth, degree of the node, and the node’s proximity centrality.
 A PROMETHEEII multicriteria approach is suggested for NIRA preparation, and an SLE algorithm is provided for NSR node link creation. The SLE approach generates the shortest path array (SPA) for NSR nodes, guaranteeing that all possible connections are installed and increasing the acceptance percentage.
 The proposed PROMETHEESLE approach was evaluated on a smallworld network to confirm that the infrastructure is slicingfriendly, and the results were also compared to those obtained from a scalefree physical infrastructure.
2. Related Works
3. Proposed System Model
3.1. Physical Infrastructure Model
3.2. NSR Model
3.3. NSR Deployment Strategy
 NSR Acceptance Ratio (NAR): This metric provides the direct measurement of the adapted network slicing technique on the given physical infrastructure. It is measured by taking the ratio between successfully completed NSRs and unsuccessful NSRs for a given time ${T}_{max}$, which is expressed as$${P}_{NAR}=\frac{{\sum}_{t=0}^{{T}_{max}}NS{R}_{success}\left(t\right)}{{\sum}_{t=0}^{{T}_{max}}NS{R}_{unsuccess}\left(t\right)}$$
 Resource Efficiency (RE): This metric is measured by calculating the achieved revenue and the investment cost made for providing the physical infrastructure. The revenue can be determined from the CPU capacity of nodes and link bandwidth requested for the NSRs. The investment cost is estimated from the physical infrastructure for the case. The expression for calculating RE is given by$${P}_{RE}=\sum _{t=0}^{{T}_{max}}\frac{NS{R}_{revenue}\left(t\right)}{PS{I}_{cost}\left(t\right)}$$$$NS{R}_{revenue}\left(t\right)=CP{U}_{requested,t}+B{W}_{requested,t}$$$$PS{I}_{cost}\left(t\right)=CP{U}_{requested,t}+B{W}_{requested,t}.{L}_{t}$$
 Problem Formulation: It is understandable that effective network slicing results in the maximum utilization of physical network resources while minimizing slice provisioning costs [27]. As a result, the problem of minimizing the cost of slice provisioning is formulated using the integer linear programming model in conjunction with the necessary constraints as shown below.$$min,\sum _{{N}_{k,NSR}\in {N}_{NSR}}\sum _{{N}_{k,P}\in {N}_{P}}{b}_{i,k}(1+SP{L}_{i,P}\left(CP{U}_{i,NSR}\right)+\sum _{{E}_{kl,NSR}\in {E}_{NSR}}\sum _{{E}_{ij,P}\in {E}_{P}}{a}_{ij,kl}B{W}_{kl,NSR}$$$$Subjectto,\sum _{{N}_{i,P}}{x}_{i,k}=1,\forall {N}_{i,P}\in {N}_{P}$$$$\sum _{{N}_{j,NSR}}{x}_{i,k}\le 1,\forall {N}_{j,NSR}\in {N}_{NSR}$$$${x}_{i,k}CP{U}_{k,NSR}\le CP{U}_{i,P},\forall {N}_{i,P}\in {N}_{P},\forall {N}_{j,NSR}\in {N}_{NSR}$$$${x}_{i,k}SP{L}_{k,NSR}\le SP{L}_{i,P},\forall {N}_{i,P}\in {N}_{P},\forall {N}_{j,NSR}\in {N}_{NSR}$$$$\sum _{{N}_{ij,P}}({a}_{ij,kl}{a}_{ji,kl})={x}_{i,k}{x}_{i,l},\forall {N}_{i,P}\in {N}_{P},\forall {E}_{kl,NSR}\in {E}_{NSR}$$$$\sum _{{N}_{ij,P}}{a}_{ij,kl}BW\left({E}_{kl,NSR}\right)\le BW\left({E}_{ij,P}\right),\forall {E}_{ij,P}\in {E}_{P}$$$${N}_{min}\le {N}_{NSR}\le {N}_{max}$$$$B{W}_{min}\le B{W}_{NSR}\le B{W}_{max}$$$$CP{U}_{min}\le CP{U}_{NSR}\le CP{U}_{max}$$$$L{T}_{min}\le L{T}_{NSR}\le L{T}_{max}$$$$SP{L}_{min}\le SP{L}_{NSR}\le SP{L}_{max}$$$${T}_{max}>L{T}_{max}$$
3.4. Factors for Node Importance Calculation
 Node Capacity Factor (NCF): The availability of the number of CPUs on the node is used to determine the capacity of the node. A node with a higher number of CPUs may give service to a greater number of NSRs. As a result, a node with a large number of CPUs should be given a better ranking in the NIRA system. The value of the factor is updated immediately upon the accomplishment of each NSR as below:$${f}_{NCF,i}\left(t\right)=CP{U}_{available,i}\left(t\right)CP{U}_{NSR,i}\left(t\right)$$
 Node Topology Factor (NTF) and Node Bandwidth Factor (NBF): In both cases, the number of adjacent connections present with the physical node determines the NTF and NBF. The total number of adjacent connections of a node is represented by NTF, whereas the total number of adjacent links bandwidth is represented by NBF. Any node with a greater NTF and NBF is given a higher priority in the NIRA. Each NSR results in an update of the factors. The equations for updating the factors are shown below:$${f}_{NTF,i}\left(t\right)=\sum _{j=1}^{tNodes}{a}_{ij,available}\left(t\right)$$$${f}_{NBF,i}\left(t\right)=\sum _{j=1}^{tNodes}(B{W}_{available,ij}\left(t\right)B{W}_{NSR,ij}\left(t\right))$$
 Node Closeness Centrality Factor (NCCF): Using the factors NCF, NTF, and NBF, we may learn about the node’s local information. A node’s strength in a network should be measured by how much it can influence the shortest path. A node’s global relevance is determined by its shortest route information, which is provided by NCCF. Therefore, the closer a node is to other nodes, the greater its centrality. Listed below is the NCCF equation at time t.$${f}_{NCCF,i}\left(t\right)=\{\sum _{j=1}^{nNodes}{L}_{i,j}{\}}^{1},i\ne j$$
4. Proposed Strategies for NSR Deployment
4.1. PROMETHEEII Strategy for NIRA Preparation
Algorithm 1: Node allocation through PROMETHEEII 
Input:${G}_{P},{G}_{NSR}$ Output:$Nodeallocation$ for each node ${N}_{P}\left(i\right)$ of ${G}_{P}$ do Calculate ${f}_{NCF,i},{f}_{NTF,i},{f}_{NCCF,i},{f}_{NBF,i}$ $\xd8\left(i\right)$ for Physical infrastructure using PROMETHEEII end for for each node ${N}_{NSR}\left(j\right)$ of ${G}_{NSR}$ do Calculate ${f}_{NCF,j},{f}_{NTF,j},{f}_{NCCF,j},{f}_{NBF,j}$ Prepare $\xd8\left(j\right)$ for NSR using PROMETHEEII end for Set allocationTrack=0 for each node $\xd8\left(i\right)$ of ${G}_{P}$ do if $\xd8\left(j\right)$ is not empty then for each node $\xd8\left(j\right)$ of ${G}_{NSR}$ do if $CP{U}_{NSR,j}<CP{U}_{available,i}$ and $B{W}_{requested}\left(k\right)<B{W}_{available}\left(l\right)$ then node Allocation ${N}_{P}\left(l\right)$ to ${N}_{NSR}\left(k\right)$ Update $CP{U}_{served,l}$ and ${a}_{{l}_{j},served}\forall {N}_{P}\left(l\right)$ of ${G}_{P}$ and $B{W}_{{l}_{j},served}\forall {E}_{P}(l,j)$ Update ${f}_{NCF,l},{f}_{NTF,l},{f}_{NCCF,l},{f}_{NBF,l}$ else Move to next node end if end for else if allocationTrack=${N}_{NSR}$ then Return NSR node allocation unsuccessful else Return NSR node allocation success end if end for return $nodeAllocation$ 
4.2. SLE Strategy for SPA Preparation
Algorithm 2: SLE for SPA preparation 

4.3. Hybrid PROMETHEEII and SLE Approach for Deployment
Algorithm 3: NSR deployment through hybrid PROMETHEEII and SLE 

5. Simulation Results and Discussions
5.1. Implementation of Proposed Algorithm for NSR Resource Allocation
5.2. Implementation of the Algorithm under Different Physical Infrastructure Networks
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref. No.  Objectives  Node Provisioning for NSR Nodes  Link Provisioning for NSR Nodes  Physical Network  Resource  Security Issues 

[17]  Slice acceptance ratio and revenuetocost ratio  Deterministic and random rounding  Deterministic and random rounding  Scalefree  Bandwidth and CPU capacity  Not considered 
[19]  Acceptance ratio and resource efficiency  Acceptance ratio and resource efficiency  k shortest path Floyd algorithm  Scalefree  Bandwidth and CPU capacity  Not considered 
[20]  Acceptance ratio and resource efficiency  Simulated annealing  Simulated annealing  Scalefree  Bandwidth and CPU capacity  Not considered 
[21]  Acceptance ratio and resource efficiency  LAVA approach  LAVA approach  Scalefree  Bandwidth and CPU capacity  Not considered 
[22]  Acceptance ratio and resource efficiency  GLL approach  GLL approach  Scalefree  Bandwidth and CPU capacity  Not considered 
[23]  Slice acceptance ratio and revenuetocost ratio  Greedy node mapping  Dijkstra’s algorithm  Scalefree  Bandwidth and CPU capacity  Considered 
[24]  Resource utilization and outage probability and resource efficiency  Markov decision process  Markov decision process  Scalefree NOMA  Power and subcarrier  Considered 
[25]  Spectral efficiency and reliability  JSPA  JSPA  Scalefree OFDMA  Power and subcarrier  Not considered 
[26]  Spectral efficiency and reliability  APSO  APSO  Scalefree OFDMA  Power and subcarrier  Not considered 
[27]  Slice acceptance ratio and revenuetocost ratio  Node ranking using VIKOR  k shortest path algorithm  Scalefree  Bandwidth and CPU capacity  Considered 
[28]  Slice classification accuracy  DBN and NN, GSDHOA for weight function adjustments  DBN and NN, GSDHOA 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  Smallworld network and scalefree network  Bandwidth and CPU capacity  Considered 
${\mathit{f}}_{1}\left({\mathit{f}}_{\mathit{NCF}}\right)$  ${\mathit{f}}_{2}\left({\mathit{f}}_{\mathit{NTF}}\right)$  ${\mathit{f}}_{3}\left({\mathit{f}}_{\mathit{NBF}}\right)$  ${\mathit{f}}_{4}\left({\mathit{f}}_{\mathit{NCCF}}\right)$  

${n}_{1}$  ${f}_{1}\left({n}_{1}\right)$  ${f}_{2}\left({n}_{1}\right)$  ${f}_{3}\left({n}_{1}\right)$  ${f}_{4}\left({n}_{1}\right)$ 
${n}_{2}$  ${f}_{1}\left({n}_{2}\right)$  ${f}_{2}\left({n}_{2}\right)$  ${f}_{3}\left({n}_{2}\right)$  ${f}_{4}\left({n}_{2}\right)$ 
.  .  .  .  . 
.  .  .  .  . 
${n}_{i}$  ${f}_{1}\left({n}_{i}\right)$  ${f}_{2}\left({n}_{i}\right)$  ${f}_{3}\left({n}_{i}\right)$  ${f}_{4}\left({n}_{i}\right)$ 
Definitions  Descriptions  Range 

${N}_{P}$  The distribution of available security level of a node in real number  (0–1) 
$CP{U}_{total}$  The distribution of CPU for each node in unit  U[30, 60] 
$B{W}_{available}$  The distribution of bandwidth of each links in unit  U[30, 60] 
L  The distribution of length of the links  U[1, 5] 
${T}_{NSR}$  The total number of NSRs arrived in the time frame  U[5, 35] 
${N}_{NSR}$  The distribution of nodes for each NSR  U[10, 30] 
$CP{U}_{NSR}$  The distribution of CPU requirement  U[5, 25] 
$B{W}_{NSR}$  The distribution of bandwidth requirement  U[5, 25] 
$SP{L}_{NSR}$  The distribution of required security level of a node in real number  (0–0.5) 
$L{T}_{NSR}$  The time duration of each NSR  T[10, 40] 
Model  100 Nodes  200 Nodes  300 Nodes  

BW Utilized  CPU Served  BW Utilized  CPU Served  BW Utilized  CPU Served  
NSRs  Min  Max  Min  Max  Min  Max  Min  Max  Min  Max  Min  Max  
SFN  10  2328  4351  2212  4133  2605  4875  2501  4680  3123  5859  2967  5566 
20  4086  7653  3882  7270  4729  8852  4540  8498  5601  10508  5321  9983  
30  7927  9908  7531  9413  9544  11,925  9162  11,448  10,626  13,275  10,095  12,611  
SWN  10  2921  5476  2775  5202  3440  6450  3302  6192  3605  6754  3425  6416 
20  5448  10,200  5176  9690  6484  12,156  6225  11,670  6720  12,600  6384  11,970  
30  11,880  14,852  11,286  14,109  13,680  17,102  13,133  16,418  14,049  17,554  13,347  16,676 
Model  100 Nodes  200 Nodes  300 Nodes  

BW Utilized  CPU Served  BW Utilized  CPU Served  BW Utilized  CPU Served  
NSRs  ${\mathit{LT}}_{\mathit{Min}}$  ${\mathit{LT}}_{\mathit{Max}}$  ${\mathit{LT}}_{\mathit{Min}}$  ${\mathit{LT}}_{\mathit{Max}}$  ${\mathit{LT}}_{\mathit{Min}}$  ${\mathit{LT}}_{\mathit{Max}}$  ${\mathit{LT}}_{\mathit{Min}}$  ${\mathit{LT}}_{\mathit{Max}}$  ${\mathit{LT}}_{\mathit{Min}}$  ${\mathit{LT}}_{\mathit{Max}}$  ${\mathit{LT}}_{\mathit{Min}}$  ${\mathit{LT}}_{\mathit{Max}}$  
SFN  10  2406  914  2286  868  2811  1045  2699  1003  3321  1957  3155  1859 
20  6243  2431  5931  2309  7368  2692  7073  2584  7862  3164  7468  3006  
30  8683  5292  8248  5027  9321  7018  8948  6737  9813  7738  9322  7351  
SWN  10  4812  1827  4571  1736  5622  2089  5397  2005  6642  3914  6310  3718 
20  10,486  8861  9862  8618  11,736  8384  11,147  8169  11,723  8328  10,937  8012  
30  13,365  10,584  12,497  10,055  15,641  11,035  14,895  10,874  17,625  12,476  16,644  10,702 
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Venkatapathy, S.; Srinivasan, T.; Jo, H.G.; Ra, I.H. Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEEII and SLE Strategy. Sensors 2023, 23, 1556. https://doi.org/10.3390/s23031556
Venkatapathy S, Srinivasan T, Jo HG, Ra IH. Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEEII and SLE Strategy. Sensors. 2023; 23(3):1556. https://doi.org/10.3390/s23031556
Chicago/Turabian StyleVenkatapathy, Sujitha, Thiruvenkadam Srinivasan, HanGue Jo, and InHo Ra. 2023. "Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEEII and SLE Strategy" Sensors 23, no. 3: 1556. https://doi.org/10.3390/s23031556