# 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 refine the optimal point with gradient descent to reduce total energy consumption with a few iterations. When cloud architecture or network resources change, a controller or threshold-based trigger (which are not covered in the present paper) could quickly recalculate IHEA-NC to maintain optimal energy consumption (minimising energy consumption and task completion time). #### 3.4.1 Simulations Using CloudSim

CloudSim is a flexible and extensible cloud simulation framework for cloud-based applications in heterogeneous real-world infrastructures. The key components of the CloudSim tool include datacenter characteristics, DatacenterBroker, NetworkTopology, virtual machines, and cloudlets. In the incorporated network cutting-ring fencing architecture, network slices, ring fences, and computational network resources are mapped to data centres, hosts within data centres, and virtual machines, respectively. The hosts assign performance metrics central to the simulation, including CPU, memory, disk, and network bandwidth. The virtual machines are the CPU, memory, storage, and network bandwidth resources allocated to each network resource inside each ring fence. Finally, cloudlets create tasks performed on \"Network Slices\" inside virtual machines. A cooling constraint central to \(E_{\text{total}_{2}}\) was indirectly simulated in the architecture to model initial energy constraints for each heuristic. #### 3.4.2 Gwa-T-12 Bitbrains Dataset

The GWA-T-12 Bitbrains Dataset (available at [[https://tinyurl.com/4](https://tinyurl.com/4) wr29k4j]([https://tinyurl.com/4](https://tinyurl.com/4) wr29k4j)) contains the network performance metrics of 1750 VMs from a distributed datacentre managed by Bitbrains. CPU, memory, disk, and network data from the fast storage network (fastStorage) and slower network attached storage (Rnd) devices are the parameters used to calculate the energy consumption. The following section describes the calculation for this specific dataset in more detail. **Understanding the data structure**: Two data set traces labelled fastStorage and Rnd that consist of the network performance metrics of 1250 VMs and 500 VMs connected to fast storage area network (SAN) devices and slower network attached storage (NAS) devices were loaded from a specified directory. With libraries like Pandas for data manipulation, the required parameters were loaded and used within the simulations. **Filtering the relevant network performance metrics**: The total computational load of resources was aggregated for each relevant trace per hour per computational resource. Since the mean usage of resources can fluctuate significantly compared to the daily usage with energy consumption, CPU core values were rounded to 0 or 1, non-numeric values were filled in the \"Timestamp\" column, and relevant time-series data were extracted with normalisation and consistent scaling. **Loading and structuring the data for CloudSim**: Through proper concatenation of the relevant dataframes, the pre-processed dataset from each network storage device is converted to a suitable format to be processed. With columns like timestamp, CPU, memory, disk, and network usage, the script saved the cleaned data set to CSV files for input to CloudSim. ## 4 Results

Three scenarios are presented to show how the novel network slicing-ring fencing ratio works. Each setup benchmarked speed in convergence with refined execution time and energy consumption. The purpose of these experimental tests was to demonstrate the efficacy of \(E_{\text{total}_{2}}\) to idealise the slicing-to-ring fencing ratio of the network. As described in the previous section, the proposed IHEA-NC algorithm optimises energy consumption by balancing network bandwidth and processing power. Latency constraints are implemented by maintaining a baseline performance level. Moreover, the proposed equation also incorporates energy-aware dynamic voltage scaling (DVS) while solving multi-user offloading in high-quality network slices.