Abstract

Cloud computing is trying to meet the needs of users with minimal resource requirements, lower costs and faster access to information. Time management is one of the key challenges in cloud computing, which can also affect other issues. Clearly, the optimal use of resources in the cloud environment can be very wide. That's why scheduling tasks have an important impact on the performance of the cloud server. In the cloud environment, we have a number of requests at the same time, that one or more actions should take place to answer each one of them and each of these require specific resources that various ways have been presented in this regard. In this project, a task scheduling algorithm is proposed for cloud environments by the combined method of two genetic and Ant Colony Algorithms (ACO) called the ACGA Optimized Algorithm which can optimize the load balancing on the server and optimize the cost. This algorithm is simulated in the CloudSim software. Also, a monitor is defined in the system as an advisor for selecting methods, balancing and introducing appropriate virtual machines. The aim of this project is to reduce the time required for completing works, appropriate distribution of loads on resources and enhancing system's productivity. The results obtained from the simulation show that the proposed method has the proper efficiency in terms of time, load balancing and cost reduction.