Grid computing refers to the infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling is the problem of mapping a set of jobs to a set of resources. It is considered one of the main steps to efficiently utilise the maximum capabilities of grid computing systems. The problem under question has been highlighted as an NP-complete problem and hence meta-heuristic methods represent good candidates to address it. In this paper, a genetic algorithm with a new mutation procedure to solve the problem of independent job scheduling in grid computing is presented. A known static benchmark for the problem is used to evaluate the proposed method in terms of minimizing the makespan by carrying out a number of experiments. The obtained results show that the proposed algorithm performs better than some known algorithms taken from the literature. Grid Computing has been defined as a type of parallel and distributed infrastructure which allows the geographically distributed autonomous and heterogeneous resources to be shared, selected and aggregated dynamically depending on their availability, capability, performance, cost, and users' quality-of-service requirements. Grid systems exploit the information technology resources by connecting loosely coupled computers and various networks together to offer the same processing capabilities provided by supercomputers to their users. This connection is achieved through computer software, called Middleware, which provides the necessary services for resource management, monitoring, security, and so forth. These computational resources are owned by different administrative organizations. Therefore, local policies are defined to specify what is shared, who is allowed to access what and when, and under what conditions. The grid architecture uses the concept of Virtual Organizations (VOs) to control resource sharing [4] . A physical organization can be part of one or more VOs by allowing all or some of its resources to be shared [6] . Nowadays, computational grid is a common tool for developing a large scale of commercial and non-commercial applications [5] . Job scheduling, which is the problem of mapping a set of jobs to a set of resources, is considered one of the main issues in grid computing systems [8] . This mapping is known to be computationally hard. The quality of resource allocation can be computed using an objective function such as minimizing the makespan, maximizing load balancing, and maximizing resources utilization [14] . The scheduler's efficiency strongly relies on the approach applied to find the mapping. Different algorithms could be used to find such mapping, which vary from simple heuristic methods to meta-heuristic methods. However, to enhance the overall performance of the grid, meta-heuristic approaches are more likely preferred [12] . One of these methods is the Genetic Algorithm (GA), a population-based meta-heuristic search method inspired by the evolution of living beings. A GA starts with a group of solutions, called the initial population, which is usually generated randomly, and then seeks to evolve the population by applying selection, crossover and mutation operators in order to find the optimal or near-optimal solutions. GAs have been widely used for solving many combinatorial optimization problems. Eleven static heuristic methods have been presented in [2] to solve the problem of job scheduling in heterogeneous environment. The results show that the GA studied outperforms the other ten methods used in the study in terms of minimizing the makespan. A population of 200 individuals, which are generated either randomly or by seeding the population with one individual generated using the Min-Min heuristic method [7] and 199 individuals generated randomly, was used.
Younis and Yang proposed an improved genetic algorithm (MGA) that adopts a new mutation procedure to solve grid independent job scheduling problem REF .
A genetic algorithm for independent job scheduling in grid computing
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Computer Science