An Efficient Framework for Load Balancing using MapReduce Algorithm for Bigdata

A humanoid body of data is what is meant as “Big Data,” which refers to the enormous amount of data that exists in the world today. In order to cope with such a large range of and rapidly changing data, the techniques of Big Data Analytics must be employed. Accurately identifying and implementing current challenges is a challenge. Data is increasing at an exponential rate, according to the most current discoveries in the field of big data. Processing enormous amounts of data on computing clusters need a powerful computing paradigm like Hadoop and MapReduce. It is possible to process terabytes of data in a short period of time using the MapReduce framework. If you want to get the most out of big data, you need to plan meticulously. The Scheduling Technique can reduce starvation, increase resource utilization, and distribute jobs to available resources. The MapReduce technique, which can be implemented on a single node or across numerous, is useful when dealing with enormous amounts of data. The Hadoop MapReduce architecture has spawned a slew of scheduling algorithms, all of which differ substantially in their design, behaviour, and approach to addressing various issues. MapReduce makes it possible to organize analysis talents in order to analyze large amounts of data. System load and task tracking speed must be taken into account for this type of software. Make sure that the task is assigned to the node with the fastest task tracker in relation to the other data nodes.