ae76089192b7789781-462720241023113952070 beie:beie director@blueeyesintelligence.orgWEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 10 30 2024 13 11 Department of CSE, DBIT, Bangalore (Karnataka), India. Usha Kirana S P https://orcid.org/0000-0002-3881-6115 Cloud Computing (CC) involves extensive data centers with numerous computing nodes that consume significant electrical energy. Researchers have identified high service-level agreement (SLA) violations and excessive energy consumption (EC) as major challenges in CC. Traditional approaches often focus on reducing EC but tend to overlook SLA violations, particularly when selecting Virtual Machines (VMs) from overloaded hosts. To address these issues, this paper introduces the Enhanced Ant Colony Optimization (EACO) algorithm, aims to reduce high EC and SLA violations by utilizing a unique approach where the best-performing ant explores movement patterns and identifies distances between movements. The algorithm comprises three key steps: tracking pheromone trails, updating pheromones and selecting the cities (VMs). The effectiveness of EACO was validated through simulations using CloudSim. Compared to existing techniques, EACO demonstrated a significant reduction in EC, achieving approximately 41-44% lower energy consumption than the traditional Ant Colony Optimization (ACO) algorithm when applied to Planet Lab data. This suggests that EACO offers a more efficient and stable solution for managing EC and SLA violations in cloud environments. 10 30 2024 22 27 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been sponsored or funded by any organization or agency. The independence of this research is a crucial factor in affirming its impartiality, as it has been conducted without any external sway. The data provided in this article is exempt from the requirement for ethical approval or participant consent. The adequate resources of this article are publicly accessible. The authorship of this article is contributed solely. 10.35940/ijitee.K9975.13111024 https://www.ijitee.org/portfolio-item/K997513111024/