Resource-Efficient Distributed and Parallel Computing for Big Data

Distributed and parallel computing are fundamental to contemporary data science, enabling the storage, processing, and analysis of increasingly large and complex datasets. Recent advances in distributed computing frameworks, parallel algorithms, cloud-edge architectures, and distributed machine learning have substantially improved scalability and reduced execution times. However, the rapid expansion of data-intensive workloads continues to drive up computational, communication, memory, and energy demands. Existing studies frequently examine frameworks, algorithms, resource management, and deployment environments in isolation, obscuring their combined effects on system performance, scalability, and efficiency. Further investigation is therefore needed to develop integrated, reproducible solutions that support the efficient execution of data-intensive tasks across heterogeneous and geographically distributed infrastructures.

This Research Topic aims to highlight recent advances in distributed and parallel computing that contribute to efficient, scalable, and reliable data science. It provides a dedicated platform for high-quality research examining how distributed systems, algorithms, and resource-management strategies can be designed and evaluated for large-scale data processing. Particular emphasis is placed on measurable improvements in computational efficiency, communication overhead, energy consumption, resource utilisation, latency, throughput, scalability, and operational cost. Contributions should demonstrate clear relevance to real-world data science workloads, such as scientific data analysis, streaming and IoT analytics, healthcare, or industrial platforms. The Research Topic also encourages research bridging algorithmic and systems-level developments to clarify the trade-offs involved in deploying distributed and parallel solutions.

To gather further insights into resource-efficient distributed and parallel computing for big data analytics and data-intensive applications, we welcome Original Research, Methods, Review, and Brief Research Report articles addressing, but not limited to, the following themes:

• Resource-efficient distributed computing frameworks for large-scale data processing
• Parallel and distributed algorithms for scalable big data analytics
• Communication-efficient, computation-efficient, and privacy-preserving distributed machine learning
• Cloud-edge architectures for resource-aware data processing and analytics
• Workload scheduling, task placement, and adaptive resource management
• Energy-aware and cost-efficient execution of distributed data workloads
• Distributed memory, storage, and data-movement optimisation
• Performance modelling, scalability analysis, and reproducible benchmarking of efficiency trade-offs
Integrated approaches combining distributed frameworks, algorithms, and deployment environments, including case studies from real-world data-intensive applications

Submissions should make a clear contribution to distributed or parallel computing for data-intensive workloads and report measurable outcomes related to efficiency, scalability, performance, resource utilisation, communication, energy, latency, or cost. Manuscripts focused solely on a generic machine-learning application, or in which the distributed or parallel computing contribution is incidental, fall outside the scope of this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Scalable Machine Learning, Big Data Analytics, Cloud-Edge Computing, Distributed Computing, Parallel Algorithms, Resource Efficiency, Energy-Aware Computing, Performance Benchmarking

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.