Dynamic Round Robin CPU Scheduling Algorithm Based on K-Means Clustering Technique

Abstract

Minimizing time cost in time-shared operating system is the main aim of the researchers interested in CPU scheduling. CPU scheduling is the basic job within any operating system. Scheduling criteria (e.g., waiting time, turnaround time and number of context switches (NCS)) are used to compare CPU scheduling algorithms. Round robin (RR) is the most common preemptive scheduling policy used in time-shared operating systems. In this paper, a modified version of the RR algorithm is introduced to combine the advantageous of favor short process and low scheduling overhead of RR for the sake of minimizing average waiting time, turnaround time and NCS. The proposed work starts by clustering the processes into clusters where each cluster contains processes that are similar in attributes (e.g., CPU service period, weights and number of allocations to CPU). Every process in a cluster is assigned the same time slice depending on the weight of its cluster and its CPU service period. The authors performed comparative study of the proposed approach and popular scheduling algorithms on nine groups of processes vary in their attributes. The evaluation was measured in terms of waiting time, turnaround time, and NCS. The experiments showed that the proposed approach gives better results.

ATS: A Novel Time-Sharing CPU Scheduling Algorithm Based on Features Similarities

“…Like some of its predecessors, ADRR sorts the processes in ascending order [23]. Samih et al proposed DRR (Dynamic Round Robin) which uses clustering technique in grouping similar processes in a cluster, it differs from its predecessors in that it allocates time for the cluster and all processes get the same time within the same cluster [7]. Mostafa et al [24] proposed DTS (Dynamic Time Slice), DTS takes the same approach as DRR in clustering the processes using K-means clustering technique, the only difference between them is the method of calculating the time slice.…”

“…Dividing the data into useful, meaningful, or both is known as clustering [6]. The greater the difference between the clusters and the greater the similarity between the elements within the same cluster, the better the clustering [7]. Both clustering and classification are fundamental tasks in machine learning.…”

“…Clustering gathers data elements into subsets that similar elements are grouped together, while different elements belong to different groups [8,9]. The categorization process is determined by the selected algorithm [7]. Features' types determine the algorithm selected in the clustering (e.g., statistical algorithms for numeric data, conceptual algorithms for categorical data, or fuzzy clustering algorithms that allow data element to be joined to all clusters with a membership degree from 0 to 1).…”

A New Round Robin Algorithm for Task Scheduling in Real-time System

“…( 6) and (7) and mean is the summation of all processes divided by the number of all processes as in Eq. (8).…”

“…An operating system can be classified into three sorts of schedulers in this context [6]: longterm which is load a process in the memory [7], mid-term or medium-term which is reduces memory consumption [7], and short-term which is select ready process to run on CPU [7]. Round Robin(RR) is a widely used scheduling algorithm that gives each process equal priority [8]. For the execution of the process, Round Robin uses a small unit of time called Time Quantum or Time Slice (TQ) [9].…”