IDENTIFY AND OVERCOME DATA PROCESSING CHALLENGES IN CLOUD USING MAP-REDUCE

Cloud is one of today's platforms, where extensive data processing has become one of the main problems, especially when calculating deadlines and costs. As the data comes from various sources like Facebook, Twitter, YouTube videos, log files, etc. But storage will require processing large amounts of data in the cloud. Therefore, we have identified the data processing problem and tried to solve the data storage problem by considering the use of the map-reduce programming model to handle parallel data processing and processing memory problems, and use the new model of the best scheduling method for Gray. Wolf Optimization Technique and Map-Reduce Framework, in MRF mainly consist of two parts 1.Calculation Part, 2. Reducing Part. In this paper, we will compare whether to use the GWO algorithm of the map-reduce framework. Make full use of the system to get better performance.