## 4 Proposed Algorithm (NCRA-DP-ACO)
### Pheromone Management
#### 4.2.7 Algorithm
This is executed in the initial stages of our algorithm and ensures that no post hoc rebalancing is needed. This responsiveness was evident during simulation across four geographically diverse U.S. datacenters (Dallas, Richmond, San Jose, and Portland), each with distinct solar irradiance levels. When solar generation peaked in San Jose, placement density increased there. When irradiance was low across all locations, our algorithm shifted focus toward datacenters with lower carbon intensity and lower electricity and carbon tax rates. * Environmental and Economic Effectiveness Our algorithm's consolidation behavior not only reduces energy usage but also minimizes carbon emissions and carbon tax liabilities by favoring green-powered datacenters. This environmentally aware strategy translates into significant cost savings (see Figure 7). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **NCRA-DP-ACO** & **BFD** & **FFD** & **CRADP** & **UACS** \\ \hline Energy Consumption (Max) & \(-18\%\) & Baseline & \(-10\%\) & \(-6\%\) & \(-8\%\) \\ \hline Carbon Emissions (Max) & \(-15\%\) & Baseline & \(-8\%\) & \(-5\%\) & \(-6\%\) \\ \hline Total Cost (Max) & \(-17\%\) & Baseline & \(-9\%\) & \(-7\%\) & \(-9\%\) \\ \hline Live Migrations & \(-48.2\%\) vs. UACS & N/A & N/A & N/A & Baseline \\ \hline PDM (Placement Quality) & \(<\)1\% & 2–4\% & 3–5\% & 1.7\% & 1.4\% \\ \hline SLAV & Very low & Moderate & Moderate & Low & High \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Key metric comparison. Placement quality, measured by the placement degradation metric (PDM), remained below 1% across all workloads. Even at 14,000 VMs, the PDM only reached 0.54% (see Figure 10), well within acceptable thresholds, demonstrating minimal post-placement migrations and strong SLA compliance. In conclusion, NCRA-DP-ACO shows remarkable scalability and robustness. The advantages become ever more significant with an increased system load, making it a promising solution for modern cloud infrastructures where resource demand is highly dynamic and sustainability is a priority. ## 6 Conclusions and Future Work

We present NCRA-DP-ACO, a multi-objective ACO framework for VM placement across geographically distributed datacenters. By incorporating real-time renewable energy availability and dynamic PUE modeling, it achieves more energy- and cost-efficient resource allocation while reducing carbon emissions and network overhead. Simulated experiments under diverse workloads demonstrate significant gains in energy savings, cost reduction, and SLA compliance, especially at higher scales where sustainability objectives are more impactful. Future enhancements will focus on the following directions: First, we aim to incorporate adaptive pheromone strategies and reinforcement learning techniques to improve the convergence speed and responsiveness of the algorithm under rapidly changing cloud conditions. Second, we plan to introduce a dynamic weighting mechanism that adjusts the trade-off between green energy usage and operational cost based on workload intensity and renewable energy availability, enabling more flexible and context-aware optimization. Finally, we plan to integrate the proposed ACO-based VMP framework into real-world cloud orchestration platforms such as Kubernetes v1.33, and KubeEdge v1.20. This integration will enable real-time, scalable deployment across distributed edge-cloud environments. Specifically, we aim to expose our optimization module as a custom scheduler extension within Kubernetes, allowing it to dynamically manage pod-to-node assignments based on energy, carbon, and network considerations. Real-time telemetry such as the host load, temperature, and renewable energy availability will be realized through monitoring tools like Prometheus and Node Exporter, enabling data-driven decision-making. This step will help bridge simulation and deployment, allowing the algorithm to operate on live data streams and adapt to changing infrastructure conditions. Conceptualization, A.S.Z. and A.M.B. ; Methodology, A.S.Z.