Big Data Analytics (BDA) applications are new software application for processing large amount of data to collect the hidden value. Big data is defined as a datasets whose size is beyond the capability of usual relational databases to collect, direct and handle the data with lesser latency. Most of the recent research works aimed to reduce the traffic and workload for convalescing the quality of services in big data. In recent times, many research works are carried out for getting better the performance of regression and classification process during the data access from the big data. However, the job completion time and memory space complexity remained challenging issue. Our main objective is to reduce the space complexity and time complexity during the data accessing from big data. In order to reduce the job completion time and memory space consumption, many existing techniques are reviewed. The key objective of the research is to increase performance of traffic aware job scheduling techniques with minimal space and time complexity. In this paper, review of various existing job scheduling techniques is carried out. The study and analysis about the performance of three existing techniques in terms of their space and time complexity is measured as the number of user requests increases and a comparison of the results between these techniques is carried out. Limitations of existing techniques are also discussed.

Big Data Analytics, Relational databases, Quality of services, Regression, Classification

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