Optimization of Sorting Algorithms for Big Data and Cloud Computing Environments

The effectiveness of sorting algorithms has become essential to modern computing in the age of digital transformation, where data is generated at enormous speeds and scales. Large-scale analytics, cloud storage, and distributed machine learning are all supported by sorting operations; however, the scale, heterogeneity, and distributed nature of contemporary systems pose challenges for conventional algorithms like Quicksort, Mergesort, and Heapsort. The evolution from traditional in-memory methods to distributed, adaptive, and hardware-accelerated approaches is highlighted in this review of recent developments in sorting algorithm optimization for big data and cloud environments. The value of algorithmic and architectural co-design has been demonstrated by the up to 5.31× speedup, 6× lower shuffle overhead, and 73% shorter execution times achieved by modern techniques that incorporate learned-model-based partitioning, SSD-internal computation, and framework-level innovations. Future directions focus on AI-driven adaptivity, skew-resilient partitioning, and energy-efficient cloud-native frameworks for scalable, intelligent, and sustainable sorting in big data systems, while persistent issues like I/O bottlenecks, data skew, and hardware integration complexity still exist.