The Art of Scheduling for Big Data Science

Many applications generate big data, like social networking and social influence programs, Cloud applications, public websites, scientific 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. The 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 specific 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. This 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 offer 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.