Task Scheduler is responsible of assigning an infrastructure resource to execute the task taking into account data locality, task constraints and the workload of each resource. The information required for the scheduling is provided by the Data Info provider, which tracks the locations of the different versions and replicas of the application data, and the Resource Manager, which provides. Scheduling in cloud computing environments, in order to minimize the cost incurred by using a set of resources and total execution time. Scheduling the big data workflow is meeting the specified deadline in such a way that the monetary cost and energy consumption are minimized. In this article many efficient scheduling techniques are analysed for the purpose of reducing the energy consumption. Analysis of these techniques has able to generate new improvements in this sector. Hence, we present a brief survey of 75 techniques. These techniques are taken from the standard publishers in the year of 2010 to 2018. Here, we are categorised techniques based on the year. Moreover, addressing of these techniques are determine the significance of their methods so that the new enhancement of task scheduling in cloud computing can be more attainable for the analysers. Finally, few of the research problems are also addressed to precede the further research on the same area. In this article the researches have made a survey about these better and early termination algorithms for the new coding standard.

Scheduling, Cloud computing, Energy consumption and Data.