Edge computing in big data: challenges and benefits

Abstract

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the network edge, enabling improvements in response times and bandwidth utilization. It offers potential privacy benefits by facilitating local data processing, thereby reducing the need to transmit sensitive data to centralized cloud systems. This technology is particularly beneficial for big data applications. We analyze the transformative benefits of edge computing in big data systems, such as reduced latency, bandwidth optimization, and near-real-time decision making, alongside the potential for enhanced data control when processing occurs locally. Despite its potential, the integration of edge computing with big data analytics introduces significant technical challenges. We examine these challenges, including data consistency, fault tolerance, energy efficiency, and notably, the new security and privacy concerns arising from the distributed nature of edge devices, managing decentralized data access, and securing computation on potentially vulnerable edge infrastructure. While acknowledging the potential of current approaches, this paper identifies their limitations and proposes key future research directions and fully realize the potential of edge computing in big data analytics in the coming years. Edge-cloud computing, AI-driven orchestration, 6G networks, and quantum edge computing, as well as bio-inspired computing, represent key areas of technological advancement