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The task of designing relational databases has always been the subject of scientific research, as it is associated with a number of interrelated steps. The result of each step is the development of models for presenting the future database with further refinement and, finally, the creation of an adequate relational database as a set of relations with the corresponding links between them. The article focuses on the normalization of databases, as one of the steps to create a datalogical model, namely, the use of the first three Normal Forms. As a result of the analysis carried out in the article, it was concluded that the definition of the Second Normal Form can be modernized and thus achieve two goals: to ensure the correct creation of potential primary keys and, thereafter, the correct external connections between relations, even before creating the data schema in a specific relational database. Moreover, to reconsider the necessity of applying the so-called “strengthened” or a higher version of the Third Normal Form, which speaks of mutual dependencies between key and non-key attributes.

Modern applications in cloud computing, internet of things and machine learning are I/O intensive. They use data storage systems as the main resource of a data center. The advent of new storage technologies makes it possible to increase the performance of I/O operations by integrating devices with different performance within the data storage system by utilizing the storage-tiering approach. To prevent data loss and service downtime, the data storage systems must ensure fault tolerance using data replication management. As modern hybrid IT infrastructures are based on hyperconverged systems, the development of new methods and models for storage management in order to ensure high performance of I/O operations, high availability and fault tolerance becomes an urgent need. The authors propose the management method based on the model of a distributed two-level data storage system. The proposed method uses the algorithms for data migration between fast and slow levels of the data storage system and the algorithms for replication of data between the nodes of distributed data storage. The simulation results indicate that the proposed management method allows increasing performance of I/O operations with files and evenly placing replicas of data blocks on the data center nodes.

Modern data centers’ providers are gradually moving away from traditional and multi-vendor IT infrastructures to open, standardized and interchangeable solutions that are based on a software defined approach to managing data center resources. The authors analyze the architectural features, requirements, limitations, hardware and software of hyperconverged infrastructures and their advantages in comparison with traditional and converged architectures deployed in data centers. The authors propose to employ two-level coordination schema to manage compute, storage, network and virtualization subsystems of hyperconverged infrastructure along with the self-management algorithms inside these subsystems.

A modern cloud data center is represented as a complex system where virtual machine consolidation and scheduling influence directly the cloud cost and performance. The virtual machine consolidation is the subject to many constraints originating from multiple domains, such as the resource requirements, user Service Level Agreement (SLA) compliance, security requirements, availability requirements, and other. Properly defined resource management methods and algorithms allow to achieve execution efficiency, SLA compliance, utilization of resources, energy saving, and the increasing profit of cloud providers. In this paper, the authors propose two versions of the Optimization using Simulated Annealing (OSA) algorithm to solve dynamic virtual machine consolidation problem. The virtual machine consolidation problem is considered as a multi-dimensional vector bin-packing problem. The authors take into account that the properties of items can be changed, new items may be requested to be deploy, and existing items may need to be reassigned to bins. Other constraints should be taken into consideration to solve virtual machine consolidation problem such as balanced load of resources of each physical machine, the limitation on maximum number of simultaneous migrations per physical machine, hardware constraints and other. The configuration of the system, the function for obtaining new configuration, the objective function for the optimization problem are determined for the proposed algorithms. The evaluation results show, that using OSA algorithms the simulated data center consumes almost the same amount of energy as while using a not optimized algorithm. On the other hand, the OSA algorithm with constraints allows to decrease overall performance degradation by virtual machines due to migrations, as a result, SLA violation is decreased. Furthermore, both OSA algorithms allow to reserve some resources of physical machine to react to increasing random resource demands in the nearest future.

A novel approach for service level management of corporate IT infrastructures is considered. Decomposition-compensation method of service level management of corporate IT infrastructures is proposed in this work. The method assumes the decomposition of tasks related to service level management and the compensation of negative impact of various factors by allocating extra resources for critical applications. The approach is based on the interaction of three integrated hierarchical processes—matching the level of services, resource planning, and service level management.

A widespread use of the cloud computing paradigm has increased the necessity and significance of improving the management efficiency of cloud infrastructures. Cloud infrastructures are characterized by a large amount of resources, different virtualization technology usage, increasing complexity, the substantial dynamics of technological changes, increasing volume of processed information. Under these conditions, special attention is paid to solving cloud resource management problems. In this paper, the authors present an architecture of Software Defined Cloud Infrastructure management system that leverages Software Defined approach in all subsystems: network, storage, and computation. Due to the intensive changes of virtual machine (VM) workloads and different conditions of resource utilization the VM placement and migration problems should be solved and optimized continuously in an online manner. To address such problems the authors present an algorithm for continuous new VM allocation and VM migration. Furthermore, the authors propose novel heuristics for VM placement and consolidation based on a physical machine (PM) workload prediction and evaluate a particular policy of the VM allocation in a data center using the adaptive genetic algorithm. The proposed Adaptive Software Defined approach to the cloud infrastructure management is implemented in the policy selector, and takes into account the existing API of SDN, Software Defined Storage, and Software Defined Computing controllers. This allows to select different algorithms or policies for resources and virtual machines management in order to adapt to the impact of disturbing influences.