Optiloop 5g Energy Eval

---
name: optiloop-5g-energy-eval
description: Evaluates the energy efficiency and operational performance of a 5G network orchestration framework under dynamic traffic conditions. It probes the system's ability to jointly optimize virtual network function (VNF) placement, traffic routing, and network element activation to minimize power consumption while maintaining connectivity and processing capacity. Use when the user wants to benchmark on Real-world mobile operator traffic snapshot, or asks about evaluating this task. Reports energy savings.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1907.10669
  bibtex_key: [REDACTED]
  confidence: high
---

# optiloop-5g-energy-eval

> An Optimization-enhanced MANO for Energy-efficient 5G Networks — Malandrino et al. (2019) (arXiv:1907.10669, 2019)

## What this evaluates

Evaluates the energy efficiency and operational performance of a 5G network orchestration framework under dynamic traffic conditions. It probes the system's ability to jointly optimize virtual network function (VNF) placement, traffic routing, and network element activation to minimize power consumption while maintaining connectivity and processing capacity.

## Datasets

- **Real-world mobile operator traffic snapshot** — total ?; splits: test (-1)

## Metrics

- `energy savings` **(primary)** — range: percent
  - Percentage reduction in total network power consumption compared to a baseline (e.g., All-on or Consolidation). Calculated as (baseline_energy - proposed_energy) / baseline_energy.
- `CCAT` — range: percent
  - Spare computational capabilities of the active topology. Measures the ratio of unused processing capacity across active B/F nodes relative to total capacity.
- `number of hops` — range: other
  - Average number of network links traversed by traffic requests between endpoints and serving VNFs.

## Input / output format

**Input**: Network topology graph (42 endpoints, 51 B/F nodes, 1,497 antennas), per-endpoint traffic demand (74–473 Mbit/s, static snapshot scaled by 22% annual growth), and VNF service graph (eNB, MME, HSS, P/S-GW).

**Output**: Binary activation states for switches, links, and servers; VNF placement assignments to active nodes; and traffic routing paths for each flow.

## Scoring recipe

```python
def compute_energy_savings(optiloop_energy, baseline_energy):
    return (baseline_energy - optiloop_energy) / baseline_energy

def compute_ccat(active_nodes, vnf_loads):
    total_cap = sum(node.capacity for node in active_nodes)
    used_cap = sum(vnf_loads)
    return (total_cap - used_cap) / total_cap

def compute_avg_hops(routing_paths):
    return sum(len(path) for path in routing_paths) / len(routing_paths)
```

## Common pitfalls

- Traffic demand is modeled as a static snapshot rather than a time-varying trace, ignoring temporal fluctuations.
- Energy consumption in the emulated Mininet testbed is estimated from real-world hardware measurements, not simulated via power models.
- The 'Optimal' baseline uses brute-force enumeration, which is computationally infeasible for networks larger than the testbed topology.

## Evidence (verbatim from paper)

> Figure 8: Mininet experiments with real-world topology: energy savings obtained as a function of traffic (left); spare computational capabilities of the active topology (CCAT) (center); number of hops traveled by requests (right).

## Citation

```bibtex
@misc{malandrino2019optiloop,
  title={An Optimization-enhanced MANO for Energy-efficient 5G Networks},
  author={Malandrino et al. (2019)},
  year={2019},
  note={arXiv:1907.10669}
}
```

- arXiv: 1907.10669