Seyedeh Foruzan Shetab Bushehri - MA.Student in Department of Computer Engineering, Arvandan Non-profit Higher Education Institute, Khoramshahr ,Iran
Khaled Mohammadnejad - Faculty member in Department of Computer Engineering , Arvandan Non -Profit Higher Education Institute , Khoramshahr,Iran

Cloud computing is a model to provide easy, distributed and comprehensive access to configurable, shared and cumulative computing resources. In other words, the workspace on the data and information in this system is transferred from the personal computer to the cloud. Therefore, users can access their data and information at anytime and anywhere in the world. One of the ways to achieve optimal productivity and reduce power consumption in the cloud infrastructure is to use load-balancing solutions. Through proper load balancing, applications will be distributed dynamically and equally across all nodes with ensuring fair and efficient assignment of each computing resource. Accordingly, in this thesis, a solution based on improved particle swarm optimization (IPSO) algorithm is developed to optimize the load balancing in the cloud infrastructure, so that it can reduce the execution time of tasks through the distribution of requests and increase users’ quality of services. The proposed technique prevents excessive or low loading in servers through optimal assignment and scheduling of tasks to physical servers. Finally, the proposed strategy is implemented in the CloudSim simulator and compared with the round-robin scheduling and ant- colony optimization solutions, its outperformance over these strategies is proved and the goals of reducing the execution time and load balancing are achieved.

cloud computing, particle swarm optimization, resource assignment, load balancing