AMPO: Algorithm for MapReduce Performance Optimization for enhancing big data analytics

The usage of cloud computing has lead to generation of petabytes of data just in a matter of second, which required a pivotal attention for analysis along with the storage. Although, storage issues in cloud has been solved to a large extent, but performing distributed analytical operation over the cloud is still a bigger challenge. The frequently used Hadoop MapReduce can perform distributed process modeling and inspite of its advantages, its pitfalls overshadow its potential advantageous features in terms of optimization. Hence, this paper presents a technique called as Algorithm for MapReduce Performance Optimization or AMPO for enhancing the performance of big data analytics. An analytical research methodology was adopted considering a case study of larger size traffic data to find that AMPO offers faster response time and lowered cost of resources as compared to the conventional MapReduce Programs without eliminating its major mapping and reducer operations.