Resource scheduling with big data becomes difficult in complex systems, such as cloud or edge computing, because big data consumes more time and more energy. The data in complex systems with a big data environment may be organised in different ways compared to traditional data. Considering other requirements, such as deadlines, system loads, and costs, the problem of scheduling in complex systems becomes more difficult in terms of time, space, and complexity. Researchers can try to make a trade-off in those metrics to schedule resources to satisfy some metrics for complex systems, however the complexity of these systems makes green computing more challenging.

Tasks have different energy consumption under different locations, whether they are on remote clouds or local. File size (code, input files, and out files) and the number of instructions both influence the energy consumption of the task. At the same time, a resource has different working states (power-computing frequency) with varying energy efficiency. Selecting the execution location and the working state of resources is a key problem in ensuring green computing in complex systems, and this should consider multiple aspects, such as the requirement of tasks, the execution location, and the working state of resources.

The aim of this Special Issue is to serve as a forum to bring together active researchers all over the world to share their recent advances in different aspects of green computing in complex systems. We welcome papers that illustrate energy-aware scheduling engineering in various complex systems, present state-of-the-art theories and novel applications in different systems, and survey the recent progress in energy management in green computing for complex systems. We also hope to attract research that gives examples in different areas and gives people the chance to further evaluate and improve those proposed methods based on big data in complex systems, and gives new methods for energy management based on complex systems. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Energy-aware scheduling based on directed acyclic graphs (DAG) for big data in complex systems
• Dynamic voltage and frequency scaling (DVFS) supporting scheduling in complex systems with a big data environment
• New theories for energy management simulation tools under big data environments
• Examples of energy-aware scheduling in some special complex systems
• Simulation tools for energy management in complex systems
• How to save energy for edge devices under big data in complex systems
• Web service composition in complex systems