Task Scheduling Mechanism for MapReduce Framework using Inertia Weight based Grey Wolf Optimization - Chandramouli.H , Vasantha.M (International Journal of Intelligent Systems and Applications in Engineering, 2024)
MapReduce is an admirable Hadoop technique for processing enormous information in cloud computing which runs programs and instructions in equivalent employing computers or processors. It simplifies complex data processing tasks by breaking them down into two basic operations mapping and reducing. Numerous scheduling algorithms are established for the Hadoop-MR model which varies in behavior and design, managing various issues like user share impartiality, and data locality along with resource consciousness. This paper proposed an innovative optimization algorithm such as Inertia Weight-based Grey Wolf Optimization (IW-GWO) Algorithm. The inertia weight strategy is utilized for controlling the exploration and population growth capability which effectively enhances the algorithm search capability and balance the connection between local development and global exploration. The obtained result shows that the IW-GWO attains better results in terms of makespan, cost, execution time, and throughput of 2.82s, 59.75 tasks/s, 52$ and 381.62ms for 200 nodes compared with existing algorithms like MOABCQ_LJF, HWACO, and IMOMVO.