Recent Advances in Data Intensive Applications: Survey

Abstract

This survey article explores recent advancements in data transfers, coflow scheduling, and reducer placement techniques for optimizing the performance of data-intensive applications in computer clusters. These techniques address the challenges of managing data transfers, optimizing resource allocation, and minimizing Coflow Completion Time (CCT). The surveyed research papers present innovative approaches such as intelligent data transfer scheduling, network-aware algorithms, near-optimal heuristics, and leveraging inter-flow relationships. These techniques aim to improve job completion time, enhance resource utilization, and minimize interference in datacenter networks. By summarizing these advancements, this survey article provides a comprehensive overview of the latest research in the field. The findings highlight the significance of these techniques in improving the performance and efficiency of data-intensive applications in computer clusters, and also identifies open challenges and future directions, stimulating further research and development in this area.