About this Research Topic
Historically multi-agents have taken two distinct approaches to computing: (1) intelligent agents acting on behalf of their users and (2) agent-based modeling used in microsimulation. Intelligent agents previously mitigated client-server communication, supported e-commerce, automated remote network tasks, and coordinated grid computing, all on behalf of their users. On the other hand, agent-based modeling observes an emergent collective group behavior of reactive agents in microsimulation such as social, behavioral, and economic simulation. It has also been applied to bioinspired algorithms in optimization such as ant colonial optimization and grasshopper optimization algorithm.
In big-data computing, there is a belief that intelligent agents should be viewed as high-level big-data managers, such as cloud resource allocators, storage/back-up managers, VM load balancers, component liaisons or glues in big-data architecture, and data dispatchers/filters to data-analyzing units. The other view combines agent group behaviors with big-data computing algorithms, which practicalizes, fine- tunes, speeds up, or scales up machine learning, data mining, and/or graph mining programs and solutions.
We would like to consider any uses of multi-agents in a specific application or a case study related to big-data computing, inclusive of social studies, business computing, and scientific computing. The goal of this Research Topic is to provide the community with a broad view of the trends and opportunities where multi-agents will play a key role in big-data computing in order to construct cloud systems, to improve big-data computing algorithms, and to help specific application/system developments.
We are looking for high-quality contributions on any aspect related to multi-agent technologies in big-data computing, including but not limited to:
- Resource management, load balancing, and fault tolerance in cloud
- Big-data computing architectures
- Interface from IoT/edge to backend cloud systems.
- Interaction with machine learning models
- Agent-based models and/or simulations used in data mining
- Graph mining with agent navigation over graphs
Keywords: multi-agents, big data, cloud computing, data mining, machine learning, multiagent technology
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.