Task scheduling in resource balance for virtual machine location in cloud data centers using improved genetic algorithm

One of the most important challenges in cloud data centers is the issue of scheduling tasks or allocating resources to user requests. There are several reasons why this can be seen as an NP-Complete issue. In a cloud computing environment, any user may encounter hundreds of virtual resources to perform any task. However, it is impossible for the user to assign tasks to virtual resources. Exploratory cloud methods in the form of exploratory search are a good option for these issues. One of these methods is genetic algorithm. Genetic algorithm is one of the best possible ways to solve the problem of scheduling tasks in the cloud, which has good performance for solving the problem of scheduling and dynamic load balance in parallel systems. In this research, we examine the concepts and methods presented in this field The location of virtual machines is of particular importance due to the increasing complexity of cloud systems. Due to the great importance of the scheduling process in cloud computing, various methods and algorithms have been proposed in order to achieve an acceptable scheduling in cloud computing. In all these algorithms, an attempt has been made to find an optimal solution to obtain a suitable schedule, in which each of the implemented methods has advantages and disadvantages, and a general solution. It is not recommended to improve all the parameters in the cloud