Effect of Cross Over Operator in Genetic Algorithms on Anticipatory Scheduling

Abstract

Disk schedulers in current operating systems schedule a request as soon as the previous request has been completed. In many common applications disk read requests are issued synchronously with short period between them In this case, the scheduler switches to a request from another process with the assumption that the last process has no more requests. This condition is called deceptive idleness. It can be overcome by anticipatory disk scheduling framework, which introduces a short, controlled delay period, during which the disk scheduler waits for additional requests to arrive from the process that issued the last serviced request. Genetic algorithms, powerful and broadly applicable optimization techniques and the most widely known types of evolutionary computation methods can be used for optimizing the anticipatory scheduling. In this paper, we propose to use the cross over operators while tuning the anticipatory scheduling with Genetic algorithms. In general, a cross over operator is regarded as a main genetic operator and the performance of genetic algorithms depends to a great extent on the performance of the cross over operator used. We make the study on Linux Operating Systems kernel 2.6.18. We use various cross over operators and make a comparison on the performance of anticipatory scheduling.

Hardware design of a new genetic based disk scheduling method

“…Also, Turton and Arsalan (1995) uses order-based crossover that require more execution time than single point crossover and it is not concerned with real time traffic. In another work, the effect of different order-based crossovers on the performance of an anticipatory disk scheduling algorithm that is tuned by a genetic algorithm has been studied in Selvi and Rajaram (2007). In these genetic based methods, no solution has been considered to reduce the number of requests that miss deadlines.…”

A genetic based disk scheduling method to decrease makespan and missed tasks

“…However, there are some genetic disk scheduling methods in literature; for example, Turton and Arsalan [31] presented an algorithm that uses the concept of parallel genetic algorithms by employing order-based crossovers. Also, the effect of different order-based crossovers on the performance of an anticipatory disk scheduling algorithm that is tuned by a genetic algorithm has been studied in [32]. In another algorithm presented by Ö kdem and Karaboga [33], ant colony (ACO) approach has been used to schedule disk requests.…”

Face Retrieval Using Image Moments and Genetic Algorithm

“…Selection process keeps and eliminates chromosomes in the population on the basis of their corresponding fitness or quality [13]. There are several possible selection strategies.…”