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However, implementation of green analytics on big data surely results in reduced memory usage and computational cost.
The GreenHadoop framework for green analytics achieves maximized energy consumption by estimating available solar energy and scheduling MapReduce jobs accordingly.
This big data storage and processing load are efficiently handled by data centers and processors residing on the cloud which ensures green analytics.
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Green big data analytics is significant in optimizing energy consumption and re-usability of available sources to meet extensive analytics requirements of big data.
The figure depicts techniques for green big data analytics.
There is a visible advancement in today's technology towards green big data analytics.
Interested readers can refer to an extensive future perspective on green big data analytics [52, 53].
Consequently, green big data analytics requires efficiency in resource utilization, energy consumption, and infrastructure scalability.
Cloud computing technology provides the basis for green big data analytics as the optimum resource utilization with reduced energy consumption.
Figure 3 shows a green big data analytics process where storage and processing resources reside on clouds and can be requested on demand.
Analytic Green's functions are derived with the generalized ray/Cagniard-de Hoop method.
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