## 4 Proposed Algorithm (NCRA-DP-ACO)
### Pheromone Management
#### 4.2.7 Algorithm
The algorithm time complexity is defined in Equation (28). \[O\big{(}D\cdot m\cdot ACO\big{(}m,n^{\prime}\big{)}\big{)} \tag{28}\]

where \(D\) is the number of datacenters, \(m\) is the number of PMs per datacenter, and \(n^{\prime}\) is the number of VMs identified for live migration. ## 5 Experimental Setup and Results

### Experimental Setup

All experiments were implemented using CloudSimPlus 5.4.2 [38], a cloud simulation toolkit in Java 1.8. To support energy-aware scheduling and sustainability analysis, we extended CloudSimPlus to incorporate green energy modeling, solar datasets, and carbon tax metrics. #### 5.1.1 Datacenter Configuration

We consider four U.S. datacenters (Dallas, Richmond, San Jose, Portland) in different time zones, similar to [3]. According to the evaluated workload, each datacenter has 126 heterogeneous _PM_s of six configurations based on four parameters: number of cores, core speed (GHz), memory (GB), and storage (GB). Detailed characteristics are shown in Table 3.

The simulation environment includes a heterogeneous set of PMs to reflect the diversity commonly found in real-world datacenters. These server types differ in terms of processing power, memory capacity, and energy efficiency, providing a realistic basis for evaluating VM placement decisions. Incorporating multiple server configurations allows for a more robust assessment of the algorithm's performance in balancing resource utiliza \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site Characteristics** & **Dallas** & **Richmond** & **San Jose** & **Portland** \\ \hline Server Power Model & \multicolumn{4}{c}{Calculated using (5) based on SpecPower benchmark [39]} \\ \hline PUE Model & \multicolumn{4}{c}{\(PUE(U_{t},H_{t})=1+\frac{0.2+0.01:U_{t}+0.01:U_{t}+H_{t}}{U_{t}}\)} \\ \hline Carbon Intensity (ton CO\({}_{2}\) /MWh) & 0.335 & 0.268 & 0.199 & 0.287 \\ \hline Carbon Tax (USD/ton CO\({}_{2}\)) & 24 & 17.6 & 38.59 & 25.75 \\ \hline Energy Price (cents/kWh) & 6.38 & 8.62 & 19.8 & 7.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Datacenter characteristics. tion and energy consumption. The detailed specifications of the PMs used in the simulation are provided in Table 4.

#### 5.1.2 VM Instances

To mimic a realistic cloud environment, different VM configurations were created, reflecting the demands different users place on _CPU_, memory, and storage. These _VM_ types represent typical requests for services encountered on Infrastructure-as-a-Service (IaaS) platforms. Details of the types of VMs used in the simulation are given in Table 5.

#### 5.1.3 Solar Energy

Solar energy data for the four datacenters were procured from an NREL project [40]. Global Horizontal Irradiance (GHI) data for south-facing flat-plate collectors fixed on tilt were considered, measured in W/m\({}^{2}\). We simulated a five-day interval (26-30 May 2014). The total area of the solar panels is 2684 m\({}^{2}\)[3]. The hourly solar energy output in kWh was calculated using these data, as shown in Figure 3. \begin{table}
\begin{tabular}{c c c c} \hline
**Server Type** & **CPU Cores** & **Memory (GB)** & **Storage (GB)** \\ \hline Type 1 & 2 & 16 & 2000 \\ \hline Type 2 & 4 & 32 & 6000 \\ \hline Type 3 & 8 & 32 & 7000 \\ \hline Type 4 & 8 & 64 & 7000 \\ \hline Type 5 & 16 & 128 & 9000 \\ \hline Type 6 & 32 & 128 & 12,000 \\ \hline \end{tabular}
\end{table} 
Table 4: Server types. Figure 3: Hourly solar energy (kWh) profiles over five days at four datacenter sites. \begin{table}
\begin{tabular}{c c c c} \hline
**VM Type** & **Number of PEs (CPU Cores)** & **Memory (GB)** & **Storage (GB)** \\ \hline Type 1 A1\_Medium & 1 & 1 & 100 \\ \hline Type2 m5.large & 2 & 2 & 200 \\ \hline Type 3 m5.xlarge & 4 & 4 & 500 \\ \hline Type 4 m5.2 xlarge & 8 & 8 & 1000 \\ \hline Type 5 m5.4 xlarge & 16 & 64 & 2000 \\ \hline \end{tabular}
\end{table} 
Table 5: VM types. #### 5.1.4 Temperature

Hourly temperatures for each datacenter location (26-30 May 2014) were retrieved from Weatherbase [41].