Big data is a field of managing large-size datasets in a distributed computing environment. These datasets require robust network algorithms to transport huge block files efficiently. The traditional processing datasets approach involves a basic data placement technique that delivers resultant data blocks and exchanges block replicas in the cluster. Thus, the cluster processes datasets with homogeneous network attributes, default configurations, and protocols to process and exchange the dataset. Therefore, it limits big data operations and creates complexity when multiple networks handshake together to process large-scale datasets. Also, it produces several types of latency issues, such as I/O latency, interoperability issues, node-to-node latency, and network-to-network latency.

To overcome these problems, we use a multi-homing networking approach that involves several networks that interact with a single network operation concurrently and exchange dataset efficiency for reducing uncertainty. However, multi-homing still lacks development and research contributions such as involving machine learning and deep learning techniques assist data block placement and identify the faults along with several other network latencies.

This Special Issue is seeking conceptual, empirical, or technological papers that will offer new insights into the field. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Novel big data networking frameworks
• Context-aware big data management strategies
• 5G-enabled big data frameworks
• Machine learning techniques for multi-homing big data networks
• IoT-enabled big data processing through the multi-homing network
• Deep learning models for big data management
• Cloud-based big data processing through multi-homing networks
• Optimization algorithms for big data networks
• Communication protocols for multi-homing big data networks
• Social data processing in multi-homing big data networks
• Heterogeneous storage management in big data networks
• Fault-tolerance techniques in big data networks
• Security issues in big data networks
• Industrial data management in big data networks
• Intelligent systems and frameworks for big data networks
• Multi-agent frameworks for big data networks