Chapter 6

The Art of Scheduling for Big Data Science

Florin Pop and Valentin Cristea

Abstract

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 ...

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