The main problem of modern data centers is the colossal power consumption. In order to minimize power consumption it is necessary to improve methods for resources allocation, while ensuring a high quality of service. Reallocation of resources in the cloud data center occurs through live migration of virtual machines, which additionally loads the system and interrupts monitoring of servers.
Currently, there is a large number of works devoted to individual issues of optimal allocation and resource management of cloud data centers. However, in known works there is no complete cycle of work. This paper proposes models and methods for a complete cycle of optimization and resource management of the cloud data center infocommunication system. In particular, the model for the initial placement of virtual machines in the form of a multicriteria optimization problem and the method for solving it are proposed. A two-level resource management system is presented, which includes local and global controllers. The local controller monitors the load and temperature of servers and makes a prediction for the next monitoring window. For prediction, it is suggested to use the method of group method of data handling (GMDH). To determine the size of the monitoring window two types of live migration have been studied and the method for calculating total migration time has been proposed, based on finding an analytical expression for the probability density. For SaaS and PaaS services which use horizontal scaling, a model of two-criteria optimization of the number of virtual machines in clusters of a large multi-tier application is proposed. It is suggested to solve this by a combined method of successive concessions and restrictions.