ABSTRACT Many applications generate Big Data, like social networking and social inuence programs, cloud applications, public websites, scientic experiments and simulations, data warehouses, monitoring platforms, and e-government services. Data grow rapidly, since applications produce continuously increasing volumes of unstructured and structured data. e impact on data processing, transfer, and storage is the need to reevaluate the approaches and solutions to better answer user needs. In this context, scheduling models and algorithms have an important role. A large variety of solutions for specic applications and platforms exist, so a thorough and systematic analysis of existing solutions for scheduling models, methods, and algorithms used in Big Data processing and storage environments has high importance. is chapter presents the best of existing solutions and creates an overview of current and near-future trends. It will highlight, from a research perspective, the performance and limitations of existing solutions and will oer the scientists from academia and designers from industry an overview of the current situation in the area of scheduling and resource management related to Big Data processing.