M.  Senthikumar, P.  Ilango, 
    Big Data Optimization for Social Networking Tweet,
    International Journal of Soft Computing,
    Volume 11,Issue 5,
    2016,
    Pages 305-311,
    ISSN 1816-9503,
    ijscomp.2016.305.311,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2016.305.311)
    Abstract: Over trillions of active users are there in the social media network worldwide, generating insurmountable data and of dynamic structure. That is why social media is indeed mountains of big data to be explored and performance is a great issue to be improved. Apache Hadoop (a cloud service) is a well-known platform for its scalability, fault-tolerance and capability of processing big data. Hadoop MapReduce gives users full control on how input datasets are processed. Hence, Hadoop is the heart of big data analytics. However, there are several issues challenging Hadoop performance. Hadoop has a large set of configuration parameters which have an impact on performance and successful completion of loads of Hadoop jobs. This study aimed at setting up and tuning Hadoop clusters on performance metrics like workload balance, throughput, network bandwidth and response time. This is achieved by the iterative process of tuning the Hadoop parameters.
    Keywords: HDFS;hadoop;well-known platform;response time;trillions