AN IMPROVED HALF LIFE VARIABLE QUANTUM TIME WITH MEAN TIME SLICE ROUND ROBIN CPU SCHEDULING (IMHLVQTRR)

Abstract

Round Robin (RR) CPU scheduling is a scheduling technique that allocate equal time slice known as quantum time (QT) to processes wanting to use the CPU. Processes are allocated the CPU in a circular manner in such a way that if QT is greater than or equal to a process’ burst time, the process will run to completion otherwise the process will be interrupted and return to the tail of the ready queue for next round of execution. The average waiting time and turnaround time in a classical RR is higher when compared with First Come First Serve and Shortest Job First CPU scheduling algorithms. The existing technique Half Life Variable Quantum Time Round Robin (HLVQTRR) further increases the average waiting time and average turnaround time for the system. Researchers proposed a dynamic QT in order to improve the classical RR which has a static QT. In dynamic RR, there are more than one QT use for allocating time slot to processes as opposed to classical RR where a fix QT is used for the allocation. This research work is a proposal that modified HLVQTRR to be ‘An Improved Half Life Variable Quantum Time with Mean Time Slice Round Robin CPU Scheduling (ImHLVQTRR)’. In the proposed technique, two quantum time (QT1 and QT2) is calculated. QT1 is the average of all the processes in the ready queue and it is constant while QT2 is the half of each process burst and it changes depending on which process is in execution. Using python programing language, the proposed approach was developed and the system was simulated and compared against the classical RR and HLVQTRR. The result shows that the proposed technique minimized average waiting time, average turnaround time and number of context switching against the existing technique (HLVQTRR) and also improved the average waiting time and average turnaround time against the classical RR.