# 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 has the key components of modern, scalable cloud model infrastructures. ### Baseline Performance Simulation

The following experiments were performed on CloudSim 3.0.3. A convergence threshold can help idealise the approach of iterative calculation to convergence in energy consumption for the respective algorithm. As shown in the baseline Figure 2, the HA-NC algorithm and the PSO algorithms reported energy consumptions of 10,355.299 kJ and 9909.403 kJ, respectively. The proposed IHEA-NC algorithm, as well as the other two baseline approaches, converged in three or fewer iterations. Notice that all baseline energy consumptions are within the same order of magnitude. ### Increased Search Space

After reviewing the baseline calculations, we can proceed to show the benefits of the proposed method by increasing the search space in a way similar to [16], which used a larger search space and delay-specific penalties. It can be seen in Figure 3 that under similar system requirements, the HA-NC and PSO algorithms reported a total energy consumption of 2464.3 kJ and 10,738.07 kJ, respectively. See Table 1 for energy consumption details in all tests. By comparing the baseline calculations with the improved search space, we can see a gain of 76.20% for HA-NC and a loss of 8.36% for PSO. Meanwhile, the negligible baseline

Figure 2: Baseline energy consumption for the strict guided search space strategy. ### AWS Scenario

The differences in energy consumption with convergence and resource allocation of network slices can be further highlighted with uneven multi-scale architectures. By designing dense, uneven \"aggregated\" architectures with high-quality network slices (ring fences with a larger number of hosts), network architects greatly benefit from the efficiency gain in scalability testing by seeking observations with differences in energy metrics. Clients seeking heterogeneous requirements such as virtual network function (VNF) placement [17; 18; 19; 20] can benefit greatly from energy-efficient models spread across aggregated slices. Various network slices and ring fences with their computational network resources were simulated in AWS through virtual components with VPCs, subnets, security groups, and elastic compute cloud instances. The IHEA-NC, HA-NC, and PSO algorithms were coded (code available at [[https://tinyurl.com/yeyku8d2](https://tinyurl.com/yeyku8d2)]([https://tinyurl.com/yeyku8d2](https://tinyurl.com/yeyku8d2)), (accessed on 15 July 2025)) in the AWS Lambda (serverless) part. Performance metrics were collected using AWS CloudWatch. The metrics were saved in AWS S3 buckets to examine their response performance and log files. As can be seen in Figure 5, the simulation in AWS shows encouraging results. While IHEA-NC consumed 912.958 kJ, both the HA-NC and PSO algorithms were well beyond that point (10,168.261 kJ and 26,845.584 kJ, respectively, as seen in Table 1). In fact, the PSO algorithm reports the worst performance in terms of energy consumption (with and without factoring in cooling energy). It is also interesting to note that the simulation in AWS is qualitatively different from CloudSim (Figure 3) in which both the HA-NC and the PSO energy consumption were practically the same. A lower power consumption by the IHEA-NC algorithm was also found within the same order of magnitude as compared to HA-NC and PSO. Figure 4: Time metrics for the baseline algorithmic programs with an increased search space. Figure 5: Energy metrics for aggregated architectures with increased search space. ## 5 Discussion

The results in Figure 5 and Table 1 indicate that IHEA-NC had an overall efficiency gain of 91.46% for total energy consumption (912 kJ vs. 10,680 kJ). Similarly, IHEA-NC in Figure 4 reported the fastest task completion time evaluated against the other heuristics (3555 vs. 990,102 or 8888 in s), which points to faster convergence times while maintaining accurate task completion. Tables 1 and 2 summarise the energy consumption and the number of iterations.