  In Round Robin CPU scheduling algorithm the main concern is with the size of
time quantum and the increased waiting and turnaround time. Decision for these
is usually based on parameters which are assumed to be precise. However, in
many cases the values of these parameters are vague and imprecise. The
performance of fuzzy logic depends upon the ability to deal with Linguistic
variables. With this intent, this paper attempts to generate an Optimal Time
Quantum dynamically based on the parameters which are treated as Linguistic
variables. This paper also includes Mamdani Fuzzy Inference System using
Trapezoidal membership function, results in LRRTQ Fuzzy Inference System. In
this paper, we present an algorithm to improve the performance of round robin
scheduling algorithm. Numerical analysis based on LRRTQ results on proposed
algorithm show the improvement in the performance of the system by reducing
unnecessary context switches and also by providing reasonable turnaround time.
