# ARMONIA: A Unified Access and Metro Network Architecture

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## Abstract

**:**

## 1. Introduction

## 2. ARMONIA Architecture

#### 2.1. Description of ARMONIA Network

#### 2.2. ARMONIA Metropolitan Network

#### 2.3. ARMONIA Access Networks

## 3. ARMONIA Algorithmic Mechanisms

#### 3.1. QoT Estimation for Metro Networks

#### 3.2. Network Slice Requirements Prediction

#### 3.3. Resource Allocation

_{e}. Specific nodes are equipped with processing capacity c

_{v}and a number of optical transceivers M

_{v}. We assume that the metro network is organized into layers, with different processing and networking capabilities and thus different processing ξ

_{v}cost for the nodes of different layers.

_{t}, BR

_{t}, TP

_{t}, OV

_{t}}. The modulation format MF

_{t}(bits/symbol) describes the number of bits encoded in a symbol, and the baud rate BR

_{t}(symbols/sec) describes the number of transmitted symbols per sec. Thus, the total transmission rate of a given tuple t equals $M{F}_{t}\xb7B{R}_{t}$. Finally, the FEC overhead OV

_{t}is taken under consideration to calculate the net transmission rate. We assume Nyquist WDM transmission, and thus a tuple t requires $\lceil}\frac{\left(1+y\right)\xb7B{R}_{t}}{z}{\displaystyle \rceil$ spectrum slots, assuming a bandwidth overhead factor y and spectrum slots of width equal to z.

_{r}and processing capacity ε

_{r}(measured in Floating-point Operations per Second -FLOPS). The slices’ demanded network capacity is routed through the network. Processing power is allocated at the traversed nodes, with the objective to serve all the slice requests, minimizing the utilized bandwidth and the processing cost. In doing so, we leverage the QoT estimator (Section 3.1) and the traffic prediction algorithm (Section 3.2). The QoT estimator allows to estimate beforehand the QoT of the lightpaths to be considered by the resource allocation algorithm. The estimator provides accurate QoT metrics that help the allocation algorithm to assign the most suitable combination of modulation format (MF

_{t}) and baud rate (BR

_{t}) in order to efficiently serve the demands. The predicted traffic is used by the allocation algorithm in order to efficiently plan the network’s resources, avoiding resource overprovisioning.

_{r}. Then, for each one of these demands and for each transmission configuration (tuple) t it calculates the regeneration points (if needed) based on the QoT estimator and the set of nodes where the slice’s processing requirements can be served. Since the solution space for each demand can be vast, slowing the execution, our algorithm has an additional phase where it prunes the dominated candidate solutions. These are the configurations with more spectrum requirements and paths with available processing capacity of higher cost than other candidate solutions. For each slice demand the additional cost of each solution is considered, taking thus into account the slice demands served up to that point (the current state of the network). The objective function is a weighted combination of (i) the incremental spectrum utilized, (ii) the number of transponders, and (iii) the cost of processing nodes.

Algorithm 1 Pseudocode of the resource allocation heuristic algorithm. |

1: Inputs: Network Topology, |

2: Transponder configurations T |

3: Objective weights: w_{1},w_{2} |

4: New slice demand = (s, d, bandwidth, processing) |

5: |

6: calculate k paths |

7: For each path with av_processing > slice required_processing |

8: Find spectrum voids |

9: For each transmission configuration combination T |

10: Use the QoT estimator to calculate the QoT |

11: Calculate number of connections, spectrum slots needed |

12: endfor |

13: endfor |

14: |

15: For each non-dominated path-tuple-processing cost configuration |

16: For each connection |

17: if size(void) > required_spectrum |

18: Select best fit void |

19: endif |

20: Select best fit processing |

21: endfor |

22: ${\mathrm{w}}_{2}\xb7\mathrm{spectrum}\text{}+\left(1-{\mathrm{w}}_{1}-{\mathrm{w}}_{2}\right)\xb7\mathrm{pr}\_\mathrm{cost}$ |

23: if cost < min cost |

24: update best solution |

25: endif |

26: endfor |

27: if size(best_solution) > 1 |

28: establish the slice demand |

29: elseif |

30: block slice demand |

31: endif |

#### 3.4. ARMONIA Deployment Consideration

## 4. Simulation Results

#### 4.1. QoT Estimation

#### 4.2. Traffic Prediction

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 6.**Metro network topology organized in two layers with nine metro-edge and six metro core nodes.

**Figure 7.**Signal-to-Noise Ratio (SNR) accuracy of the QoT estimator for various number of lightpaths (80, 120, 160).

**Figure 8.**(

**a**) The number of utilized transponders and (

**b**) the bandwidth utilization in the actual, machine learning assisted and static provisioning scenarios for various traffic loads.

**Figure 9.**Average utilization of the processing edge and core nodes in the actual and ML assisted provisioning scenarios for different traffic loads.

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## Share and Cite

**MDPI and ACS Style**

Kretsis, A.; Sartzetakis, I.; Soumplis, P.; Mitropoulou, K.; Kokkinos, P.; Nicopolitidis, P.; Papadimitriou, G.; Varvarigos, E.
ARMONIA: A Unified Access and Metro Network Architecture. *Appl. Sci.* **2020**, *10*, 8318.
https://doi.org/10.3390/app10238318

**AMA Style**

Kretsis A, Sartzetakis I, Soumplis P, Mitropoulou K, Kokkinos P, Nicopolitidis P, Papadimitriou G, Varvarigos E.
ARMONIA: A Unified Access and Metro Network Architecture. *Applied Sciences*. 2020; 10(23):8318.
https://doi.org/10.3390/app10238318

**Chicago/Turabian Style**

Kretsis, Aristotelis, Ippokratis Sartzetakis, Polyzois Soumplis, Katerina Mitropoulou, Panagiotis Kokkinos, Petros Nicopolitidis, Georgios Papadimitriou, and Emmanouel Varvarigos.
2020. "ARMONIA: A Unified Access and Metro Network Architecture" *Applied Sciences* 10, no. 23: 8318.
https://doi.org/10.3390/app10238318