AI-Driven Load Balancing for Energy-Efficient Data Centers

Le résumé fourni par la source

Data usage was increasing at a very fast pace, mainly due to growth in social networks and the usage of trending applications, which increased the demand for data centers that are now seen as crucial components of modern infrastructure. Nevertheless, these data centers tend to be rather energy-intensive, which automatically translates into higher operational expenses and ecological costs. Management by AI of load balancing seems to offer a possible solution to achieve high innovation without necessarily using a lot of energy. This paper aims to analyze the prospects of including Artificial Intelligence (AI) approaches in load-balancing strategies to optimize energy consumption in Data Centers. AI can work dynamically utilizing machine learning algorithms and predictive analysis to assign work, anticipate needs, and allocate resources appropriately. This paper aims to explain and explore various AI-based load balancing techniques, the integration process and its effects on the aspects of energy consumption and organizational productivity. Other important issues such as computational complexity/cost, are also considered, data protection and how data processing is done in real-time. By the experiment’s results, the team proved that load balancing with the use of AI could save up to a third of energy. At the same time, data centers’ productivity remains high, which means that the suggested technological solution could be a perspective for further usage to stabilize data centers.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.