LEVERAGING MACHINE LEARNING FOR RESOURCE OPTIMIZATION IN USA DATA CENTERS: A FOCUS ON INCOMPLETE DATA AND BUSINESS DEVELOPMENT

Аннотация

INTRODUCTION meets global efforts to ensure carbon emissions Background and Motivation are reduced. An area where inefficiency reins in As per Rahman et al. (2023), data centers form the include cooling systems, hardware utilization, and linchpin of modern digital infrastructure, enabling power distribution. It is on this basis that everything from e-commerce and streaming to addressing such inefficiencies becomes quite cloud computing and artificial intelligence. The important in maintaining the operational viability United States, at the forefront of technology, of data centers to support their expansion in the houses some of the world's most extensive and digital economy (Alam et al., 2023; Abdulazeez et technologically advanced data centers as a part of al. 2024). its economic and technological framework. Recent Problem Statement studies have indicated that U.S. data centers make up about 2% of the nation's total electric use, and Notwithstanding, most data centers still suffer this trend is probably continuing to increase with from ineffective use of their resources due to the growth in the number of computational factors such as imbalance in workload, inefficient resources the modern world requires. Shawon et cooling methods, and underutilization of al.(2023b), asserted that the scale and power hardware. Incomplete data is one of the most involved in such facilities outline the importance of critical barriers to optimization (Al Mukaddim et efficiently using resources to achieve al., 2023a; Feng et al., 2024). Missing sensor sustainability. To that effect, efficient resource readings, missing hardware logs, and incomplete utilization not only cuts operation costs but also environmental data pose a problem to the https://www.theamericanjournals.com/index.php/tajet 120 THE USA JOURNALS THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN – 2689-0984) VOLUME 06 ISSUE12 traditional resource management system, leading This research question aims to explore imputation, to less-than-optimal decisions. The inability to data augmentation, or robust learning algorithms handle incomplete data weakens the reliability and that can help deal with missing data for the robustness of resource optimization. The specific accuracy and reliability of machine learning value of machine learning lies in providing models. Addressing incomplete data challenges transformative opportunities regarding the will improve the performance of machine learning aforementioned challenges exacerbated by models and enhance data center resource incomplete data that plagues traditional optimization. Data centers often generate large optimization approaches so often (Buiya et al. volumes of data; however, due to several reasons 2023; Ezeigweneme et al., 2024). This study aims such as sensor failures, network outages, or human to explore how different ML techniques can be error, this data can be incomplete. used for optimizing resource utilization by data RQ3: What are the business development centers in the US, focusing on strategies to handle implications of optimizing resource usage in incomplete data and implications for business data centers? development. Machine Learning potentially The question deals with the higher-order business improves efficiency, reduces cost, and unleashes value linked to data center resource optimization. paths toward sustainable growth by applying The improvement in profitability and predictive analytics, anomaly detection, and sustainability for a data center will result from reinforcement learning. Examples of incomplete reductions in energy consumption and operational data are being treated with imputation techniques costs. Besides, optimized resource usage will or robust model design to meet the promise of ML enable data centers to scale their operations more in these most critical environments. efficiently and respond to changing business Research Questions needs. Moreover, data-driven insights gained from RQ1: How can machine learning techniques be machine learning models can help identify new applied to optimize resource usage in USA data business opportunities and improve service centers? delivery. This research question aimed at understanding Significance of the Study how machine learning algorithms can help in The significance of this research lies in its improving efficiency in data center operations. capability to resolve the pressing challenges Analyzing the trend of past data on energy confronted by U.S. data centers, which are consumption, server utilization, and several other instrumental to the digital economy yet burdened factors, machine learning models can identify by inefficiencies in resource utilization. This work patterns and thereby predict the future need for will advance not only the technical capability of resources. This information can thus be used to data centers but also their alignment with goals of optimize resource allocation, reduce energy sustainability and cost reduction