Word Frequency Counting Based on Serverless MapReduce
This is an incremental improvement for organizations and programmers seeking optimized big data processing solutions.
The paper tackled optimizing word frequency counting by combining serverless computing with MapReduce, finding that adjusting the number of Map and Reduce functions reduces execution time and improves efficiency for given workloads.
With the increasing demand for high-performance and high-efficiency computing, cloud computing, especially serverless computing, has gradually become a research hotspot in recent years, attracting numerous research attention. Meanwhile, MapReduce, which is a popular big data processing model in the industry, has been widely applied in various fields. Inspired by the serverless framework of Function as a Service and the high concurrency and robustness of MapReduce programming model, this paper focus on combining them to reduce the time span and increase the efficiency when executing the word frequency counting task. In this case, the paper use a MapReduce programming model based on a serverless computing platform to figure out the most optimized number of Map functions and Reduce functions for a particular task. For the same amount of workload, extensive experiments show both execution time reduces and the overall efficiency of the program improves at different rates as the number of map functions and reduce functions increases. This paper suppose the discovery of the most optimized number of map and reduce functions can help cooperations and programmers figure out the most optimized solutions.