## 4 Proposed Algorithm (NCRA-DP-ACO)
### Pheromone Management
#### 4.2.7 Algorithm
When scaled further, its efficiency continues to improve, dropping to 0.062 kWh/VM at 14,000 VMs. We note, however, that real-world outcomes may vary due to workload diversity, hardware heterogeneity, and cooling-system differences not captured by synthetic-trace simulations. ### Discussion of Results

The experimental results demonstrate that NCRA-DP-ACO excels across multiple objectives--energy efficiency, SLA compliance, carbon emission reduction, and operational cost savings. The ACO algorithm dynamically adapts to the system load, renewable energy availability, and resource heterogeneity among datacenters. These adaptive features are particularly effective as the system scales from medium to large workloads (e.g., 5000+ VMs). The simulated infrastructure comprises four geographically distributed datacenters, each with 126 heterogeneous PMs (504 total) connected through a backbone network. A centralized broker and CIS coordinate placement decisions using up-to-date site states. Under light workloads (500-1000 VMs), NCRA-DP-ACO may incur slightly higher carbon emissions and costs than simple heuristics because it prioritizes renewable avail \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Original VM** \\ **Count** \\ \end{tabular} & \
\begin{tabular}{c} **Energy/VM** \\ **(kWh)** \\ \end{tabular} &
\begin{tabular}{c} **Total Energy at** \\ **5000 VM (kWh)** \\ \end{tabular} & \multirow{2}{*}{**Trace Type**} & \multirow{2}{*}{**Source**} \\ \hline NCRA-DP-ACO & 5000 & 0.169 & 845.10 & Metacentrum & This study \\ \hline NSGA-III & 200 & 2.13 & 10,650 & Synthetic & [19] \\ \hline ETA-ACO & 500 & 0.490 & 2450 & Synthetic & [25] \\ \hline Standard ACO & 500 & 0.606 & 3030 & Synthetic & [25] \\ \hline Standard PSO & 500 & 0.533 & 3190 & Synthetic & [25] \\ \hline ABGWO (GWO Hybrid) & 2000 & 0.210 & 1050 & Azure (real) & [46] \\ \hline GA + Threshold & 300 & 0.533 & 2666 & Synthetic & [48] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Energy consumption per VM (kWh) at 5000 VMs for NCRA-DP-ACO and published baselines. ability (often requiring more active servers at a low scale). In contrast, heuristic methods typically aim to minimize the number of active servers, yielding marginally better results at small scales. However, as the workload intensity grows, the advantages of NCRA-DP-ACO become clearly dominant due to better overall consolidation and green energy prioritization. Traditional approaches begin to incur remarkably high SLA violations and energy consumption due to the absence of adaptive placement mechanisms. On the other hand, with such a density of VMs, ACO maintains SLA violation rates very satisfactorily below 0.006. This trade-off reflects the proposed algorithm's bias toward long-term environmental sustainability over short-term server consolidation efficiency, and the advantages are clearer at scale. The advantages are as follows:

* Scalability to Workload Intensity The framework was tested on a wide range of VM loads ranging from 500 to 14,000 VMs, demonstrating stable behavior and low overheads across scales. Key findings include the following:
* Energy savings reached up to 18% compared to traditional heuristics (see Figure 5). * Carbon emissions were reduced by more than 15% in large-scale deployments due to intelligent VM consolidation and datacenter selection (see Figure 6). * Live migrations remained low (under 2.3% of total VMs), showing that the solution is stable and does not rely on excessive re-optimization (see Figure 7). These outcomes (see Table 10) confirm that NCRA-DP-ACO scales efficiently and maintains optimization quality across a broad spectrum of cloud loads. * Sensitivity to Solar Energy Availability Our proposed algorithm incorporates solar energy availability into every placement decision in real time. For each VM request, the framework evaluates the current solar output across all datacenters and prioritizes placement to sites with the highest available green energy.