TY - JOUR ID - 51964 TI - Integrated modeling and solving the resource allocation problem and task scheduling in the cloud computing environment JO - Journal of Industrial Engineering and Management Studies JA - JIEMS LA - en SN - 2476-308X AU - Shams, Maryam AU - Jafarzadeh Afshari, Ahmad AU - Khakbaz, Amir AD - Department of industrial engineering, Shomal University, Amol, Iran. Y1 - 2017 PY - 2017 VL - 4 IS - 1 SP - 69 EP - 89 KW - cloud computing KW - Resource Allocation KW - Task scheduling KW - non-dominated sorting genetic algorithm KW - Particle Swarm Optimization DO - 10.22116/jiems.2017.51964 N2 - 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. UR - https://jiems.icms.ac.ir/article_51964.html L1 - https://jiems.icms.ac.ir/article_51964_6ea119e40d18188bf8eb25c8d712d4af.pdf ER -