Paper Details

To examine large volume of data and to extract hidden data, applications of big data analytics isused. To schedule the job and data, cuckoo search scheduling algorithm is used in this proposed study. The cuckoosearch algorithm permits providers and consumers of resources to perform the decisions for scheduling on their own. Sodepending on their requirement, providers and consumers achieve enough amounts of data. The objective of this paper isto minimize the overall turnaround timing and execution cost and to maximize the utilization of the resources. To achievethis objective, Cuckoo Search Algorithm (CSA)is designed depending on Confidence Time Gap (CTG). Hadoop is asoftware framework that stores huge volume of data in a cluster and allows to process data from all nodes. Map Reduceis a application framework used to process huge volume of data in clusters. The efficiency of Big Data Analytics isimproved by implementing job scheduling using Cuckoo Search Algorithm. This algorithm is more efficient andconvenient than the available resource brokers implementing various data-based job scheduling algorithms.

D. S. Dayana, D. Godwin Immanuel, “Job Scheduling In Big Data Using Cuckoo Optimization Technique”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 2, pp. 462-465, February 2018.