An Intelligent MLFQ Scheduler Prediction Using Neural Approach

Process management has a unique place among the various OS functions like file management, I/O management, networking, protection system, command interpreter system, memory management. This is because OS is primarily system software, which interacts with hardware during runtime. Different scheduling algorithms are used to manage the processes in the OS. Each algorithm has its own drawbacks and limitations. We need to keep multiple processes in memory simultaneously and exploit the multi programming ability of the CPU. In this paper, Multi-Level Feedback Queuing (MLFQ) scheduler algorithm is proposed. Our paper work is partition into three categories such as, initially speaks about the previous Central Processing Unit (CPU) scheduling algorithms, then, proposes a novel Neural based prediction scheduling scheduling approach, which results in higher CPU efficiency and finally in the performance analysis, proposed algorithm is compared with various conventional round robin approach and shows that proposed algorithm performs better in terms of CPU efficiency.