Theoretical Performance Analysis of Distributed Big Data Systems

The rapid growth of digital ecosystems has exposed the limitations of traditional computing paradigms in handling large-scale, high-velocity, and heterogeneous data workloads. This paper presents a theoretical analysis of modern distributed computing systems, focusing on performance evaluation, scalability constraints, and load balancing strategies within big data architectures. Using formal computational models and principles from complexity theory, the study examines key performance indicators such as latency, throughput, communication overhead, and fault tolerance. It further analyzes widely adopted frameworks including Apache Hadoop, Apache Spark, and Apache Kafka under theoretical constraints derived from Amdahl’s Law, Gustafson’s Law, and the CAP Theorem. In addition, the paper categorizes load balancing techniques into static, dynamic, and adaptive approaches, evaluating their theoretical efficiency in heterogeneous environments. By synthesizing existing models and identifying unresolved challenges, this work proposes a conceptual framework for assessing and designing scalable distributed systems. The findings aim to support researchers in developing next-generation high-performance big data infrastructures.