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Abstract

The increasing demand for cloud computing services has significantly impacted server farms, placing substantial strain on the infrastructure. In distributed systems, dynamic computing patterns can lead to irregular load distribution across server farm resources, resulting in over- or under-loaded servers. This imbalance not only increases energy consumption but also leads to inefficient operations and resource wastage. While optimizing for energy efficiency through server consolidation is a common approach, it often exacerbates issues like uneven load distribution across physical machines (PMs), which can negatively impact system performance. This paper presents a comprehensive review of load balancing algorithms achieved through server consolidation in distributed computing environments. A meta-analysis of existing literature on this topic highlights key approaches that optimize resource utilization while improving Quality of Service (QoS) metrics. Additionally, we provide a novel classification of load balancing and server consolidation strategies based on various factors, including migration overhead, hardware constraints, network traffic, and system reliability. By exploring these factors, we aim to contribute a refined framework that enables more efficient load balancing and server consolidation in real-world applications. This work offers insights into how these algorithms can enhance server farm operations, ensuring better scalability, reduced energy consumption, and improved service quality.