Javad Behnamian
Abstract
This research extends a two-phase algorithm for parallel job scheduling problem by considering earliness and tardiness as multi-objective functions. Here, it is also assumed that the jobs may use more than one machine at the same time, which is known as parallel job scheduling. In the first phase, jobs ...
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This research extends a two-phase algorithm for parallel job scheduling problem by considering earliness and tardiness as multi-objective functions. Here, it is also assumed that the jobs may use more than one machine at the same time, which is known as parallel job scheduling. In the first phase, jobs are grouped into job sets according to their machine requirements. For this, here, a heuristic algorithm is proposed for coloring the associated graph. In the second phase, job sets will be sequenced as a single machine scheduling problem. In this stage, for sequencing the job sets which are obtained from the first phase, a discrete algorithm is proposed, which comprises two well-known metaheuristics. In the proposed hybrid algorithm, the genetic algorithm operators are used to discretize the particle swarm optimization algorithm. An extensive numerical study shows that the algorithm is very efficient for the instances which have different structures so that the proposed algorithm could balance exploration and exploitation and improve the quality of the solutions, especially for large-sized test problems.
Maryam Shams; Ahmad Jafarzadeh Afshari; Amir Khakbaz
Abstract
Cloud computing is considered to be a new service provider technology for users and businesses. However, the cloud environment is facing a number of challenges. Resource allocation in a way that is optimum for users and cloud providers is difficult because of lack of data sharing between them. On the ...
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Cloud computing is considered to be a new service provider technology for users and businesses. However, the cloud environment is facing a number of challenges. Resource allocation in a way that is optimum for users and cloud providers is difficult because of lack of data sharing between them. On the other hand, job scheduling is a basic issue and at the same time a big challenge in reaching high efficiency in the cloud computing environment. In this paper, “the cloud resources management problem” is investigated that includes allocation and scheduling of computing resources, such that providers achieve the high efficiency of resources and users receive their needed applications in an efficient manner and with minimum cost. For this purpose, a group technology based non-linear mathematical model is presented with an aim at minimization of load difference of servers, number of transfers between servers, number of active virtual machines, maximum construction time, the cost of performing jobs and active servers energy consumption. To solve the model, a meta-heuristic multi-objective hybrid Genetic and Particle Swarm Optimization algorithm is proposed for resource allocation and scheduling. In order to demonstrate the validity and efficiency of the algorithm, a number of problems with different dimensions are randomly created and accordingly the efficiency and convergence capability of the suggested algorithm is investigated. The results indicated that the proposed hybrid method has had an acceptable performance in generating high quality, diverse and sparse solutions.