Network-, Cost-, and Renewable-Aware Ant Colony Optimization for Energy-Efficient Virtual Machine Placement in Cloud Datacenters  
[PERSON]  
1 Department of Mathematics & Computer Science, Beirut Arab University, Beirut 1107, Lebanon; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Mathematics & Computer Science, Alexandria University, Alexandria 21526, Egypt 22 Department of Mathematics & Computer Science, Alexand  
40% of their energy budget is spent on cooling them down [4]. Datacenters around the world waste about 3% of all the electricity generated globally. This percentage corresponds to an annual consumption exceeding 416 terawatt-hours. For example, the U.S. alone consumed more than 73 billion kWh in 2020 [5], illustrating the significance of addressing energy consumption. Power management for datacenters can either take hardware or software forms. One important software method is the optimization of VM allocation to PMs, known as VM placement. VMs can run concurrently on a virtualized platform, competing among themselves for physical resources that are scheduled by a hypervisor [6]. Currently, there is active research being conducted on designing efficient scheduling algorithms for VM placement. Cloud providers must carefully design VM-to-PM mapping strategies to ensure energy efficiency while meeting user requirements, for which the specification is mostly found through Service Level Agreements (SLAs). VM placement techniques have evolved, but existing approaches usually employ a single-objective optimization strategy and consequently neglect others, resulting in suboptimal trades. For example, energy-aware algorithms may forget network performance, while network-aware strategies may increase energy consumption. Green VM placement aims to align workload execution with periods or locations of abundant renewable energy and lower carbon intensity. The integration of renewable energy sources into VM placement has not been adequately examined in the literature. In this paper, we propose NCRA-DP-ACO (Network-, Cost-, and Renewable-Aware Dynamic-PUE ACO), a multi-objective framework that jointly balances energy, carbon, and network performance and cost efficiency. Traditional exact optimization methods (e.g., Integer Linear Programming) may guarantee optimality, but they become computationally infeasible as the scale of the VM placement problems grows, especially in large and dynamic datacenter environments. Because VM placement is NP-hard, exact real-time solutions are impractical. Therefore, heuristic and metaheuristic techniques are favored due to their ability to efficiently explore large solution spaces and produce near-optimal solutions within acceptable time limits. Ant Colony Optimization (ACO) is a flexible, decentralized metaheuristic inspired by ant foraging behavior. Multiple candidate solutions are constructed in parallel, and pheromone-guided probabilistic selection helps avoid premature convergence to suboptimal solutions. These traits make ACO well suited for multi-objective VM placement (VMP). In Section 4, we describe the algorithm framework, the pheromone-update process, and the multi-objective heuristic development in detail. Our primary contribution is a network-, cost-, and renewable-aware VMP framework that leverages ACO to dynamically optimize VM allocation across multiple datacenters. Unlike traditional placement strategies, the proposed NCRA-DP-ACO algorithm integrates real-world solar energy availability, carbon pricing, the network transmission cost, and the dynamic PUE per datacenter into a unified multi-objective model. We evaluate placement decisions using four real-world U.S. datacenters with a heterogeneous infrastructure and variable solar availability. Using authentic Metacentrum workload traces [7], we show that NCRA-DP-ACO reduces energy consumption by 13.7%, carbon emissions by 6.9%, and live VM migrations by 48.2% versus a baseline ACO approach while preserving SLA compliance. The remainder of this paper is organized as follows. Section 2 reviews related work.