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There is a necessity to schedule the system and user processes by the operating system to getting maximum utilization of the CPU and obtaining high throughput. This paper presents a dynamic scheduling algorithm that make use of genetic algorithm operations for scheduling multi non-preemptive task on uniprocessor system to get minimum average waiting time and average turnaround time. The algorithm...
The studied problem is to optimize a production system where these systems have two dedicated processors. The assignment of tasks to these processors is fixed. For this problem, we have three types of tasks. Some tasks must be processed only by the first processor, a few others by the second processor and the remaining tasks need simultaneously both processors. This NP-hard problem requires the use...
Metaheuristics algorithms show very good performance in solving various job scheduling problems in computational grid systems. However, due to the complexity and heterogeneous nature of resources in grid computing, stand-alone algorithm is not capable to find a good quality solution in reasonable time. This study proposes a hybrid algorithm, specifically ant colony system and genetic algorithm to...
The problem of scheduling independent users' jobs to resources in Grid Computing systems is of paramount importance. This problem is known to be NP-hard, and many techniques have been proposed to solve it, such as heuristics, genetic algorithms (GA), and, more recently, particle swarm optimization (PSO). This article aims to use PSO to solve grid scheduling problems, and compare it with other techniques...
Task Scheduling is a critical problem in cloud computing platforms. Traditional algorithms mainly focus on shortening the makespan, but seldom mention the energy consumption. This paper proposes a duplication based method to reach multiple targets, including reducing the executing time and energy cost. The main algorithms used are genetic algorithm and ant colony algorithm, with a new dynamic fusion...
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