# A Novel Cloud Energy Consumption Heuristic Based on a Network Slicing-Ring Fencing Ratio
## 3 Materials and Methods
### Iterative Heuristic Energy-Aware Non-Convex Equation
It can be seen that IHEA-NC outperforms the other heuristics, particularly in aggregated cloud models with varied system requirements. The calculation involved in the network slicing-ring fencing ratio is simple (\(E_{\text{total}_{2}}\)) and converges in just a few iterations (which amounts to milliseconds). Thus, it is favourable for energy minimisation in heterogeneous network slicing-ring fencing architectures. IHEA-NC lacks formal robustness against uncertainty in input parameters. Interesting future work could focus on modelling IHEA-NC using input probability distributions and stochastic approaches [21], or introducing adaptive control techniques [22; 23]. With varied system requirements depending on the need of the client, discretisation for the compared algorithmic adaptations resulted in limitations with disparate results. For an increased search space, the iterative heuristic energy-aware non-convex optimisation equation ensures its suitability for heterogeneous architectures with resource-heavy workloads. Network architects can optimise their aggregated network topologies consisting of interconnected switches for network operators to adapt through client needs for resource-heavy workloads. The same consistency observed with AWS through virtualisation in this paper is expected to be observed in other cloud providers like Google Cloud Platform (GCP) and Microsoft Azure due to similar platform-specific virtual machine implementations. We proposed a novel energy optimisation heuristic called IHEA-NC that iteratively refines a network slicing-ring fencing ratio for heterogeneous cloud infrastructures. Our results convincingly show that the proposed approach achieved an improvement of 91.46% compared to state-of-the-art approaches, such as the HA-NC and PSO algorithms [5; 6]. Although IHEA-NC is calculated iteratively, it has been shown to quickly converge to improved energy consumption and computational execution time within a few iterations. Its simplicity in the calculations makes it very competitive compared to alternative network slicing algorithms [3; 4]. Switches, workload orchestrators, or cloud controllers are fast enough to calculate \(E_{\text{total}_{2}}\) to meet client service agreements [18; 19; 20], especially in business-critical workloads [24]. ## 6 Conclusions

We have demonstrated how NOMA clustering based on the delay tolerance of network slices and inter-network resource communication modelled the HA-NC algorithm. The PSO algorithm offloads computational tasks between interconnected devices according to the offloading priority. Through the creation of simulation environments on each respective platform, the IHEA-NC algorithmic programme was shown to be the most efficient by satisfying an efficiency gain of 91.46% by total energy consumption in terms of energy consumption and computational execution time for the aforementioned experimental objectives. It is therefore better than the HA-NC and PSO algorithms [5; 6] which reported 312.74% (2464.300 kJ vs. 10,168.261 kJ) and 150% (10,738.07 kJ vs. 26,845.584 kJ) increases in total energy consumption, respectively. Similarly, it is more suited to aggregated workloads with lower computational execution time than the HA-NC and PSO algorithms (3555.565 s vs. 990,102.8350 s and 8888.3450 s) which results in higher throughput and overall lower per-task energy consumption in heterogeneous architectures. Due to its fastest task completion time and faster convergence than HA-NC and PSO, IHEA-NC is an advantageous heuristic regardless of the cloud platform and resource allocation. When the underlying cloudarchitecture and settings change, the IHEA-NC heuristic must be recalculated to keep the energy profile in optimal conditions. Highly dynamic network architectures may rerun IHEA-NC frequently to improve robustness or trigger a recalculation step when a certain pre-set threshold is reached. Monitoring these changes to partially recalculate IHEA-NC is indeed an interesting research direction.