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The Internet of Things (IoT) is an emerging technology that offers great opportunities that is designed to improve the quality of consumers' lives, and also to improve economic indicators and productivity of enterprises, and more efficient use of resources. IoT system refers to the use of interconnected devices and distributed subsystems to leverage data gathered by sensors and actuators in some sort of environment and to take a proper decision on a high level. In this chapter, the authors propose an approach to Microcloud-based IoT infrastructure management to provide the desired quality of IT services with rational use of IT resources. Efficiency of IT infrastructure management can be estimated by the quality of services and the management costs. The task of operational service quality management is to maintain a given level of service quality with the use of minimum IT resources amount in IoT environment. Then, the maximum efficiency can be achieved by selecting such control when actual level of service corresponds to the coordinated with business unit and can be achieved by minimal costs. The proposed approach allows the efficient use of resources for IT services provision in IoT ecosystem through the implementation of service level coordination, resource planning and service level management processes in an integrated IT infrastructure management system based on hyperconvergence and software-defined principles. The main goals of this chapter are to investigate the state of art of the IoT applications resource demands in the context of datacenter architecture deployment and to propose Microcloud-based IoT infrastructure resource control method.

Cloud data centers are most dynamic systems in a modern digital world. To deliver the high-performance and fault-tolerant IT services to end users effectively it is necessary to develop new methods for data center resource management while adapting to the emergence of new requirements. In this paper, the authors refine and evaluate the previously proposed method for cloud data center resource management based on the reinforcement learning approach. The proposed method takes into account the power consumption and the number of SLA violations in the management policy. The power consumption is managed by switching physical servers to active or sleep state depending on current utilization of three resources: CPU, memory, and network bandwidth. The proposed reinforcement learning agent allows to determine the optimal policy for managing the physical servers without creating an environment model and preliminary information about the workload. The evaluation results show that the proposed method allows to decrease the SLA violation time, to serve more VM schedule requests when the number of VMs is changing frequently, and to decrease the utilization of data center network due to decreased number of migrations.

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.

The complexity of cloud data centers requires advanced resource management solutions that are capable to adapt to the changing customer's demands while providing continuous service and performance guarantees. The proposed Integrated Resource Management approach for energy-aware resource management provides the necessary elasticity on the physical machine level by considering a data center power consumption and a service-level agreement violation. The Integrated Resource Management approach is based on the data center state-space dynamic model, power consumption model, prediction model, SLA violation model, and capacity planning model method. The modeling process takes into consideration heterogenous environment, virtualized resource granularity, virtual machine live migration power consumption, virtual machines on/off state, and virtual machine deployment delay. The analysis and simulation, using Google cluster-usage traces, show that using Integrated Resource Management approach cloud service providers can achieve energy savings while minimizing SLA violations in terms of the number of overloaded physical machines and VM deployment delay.

Software-defined IT infrastructure does not have a clear definition in modern science world. There are many different interpretations among the corporate definitions of a software-defined IT-infrastructure, each of which is surely possible. General representation of software-defined IT-infrastructure was proposed based on those corporate interpretations. To solve the management problem in software-defined IT infrastructure the base management model was introduced. The base management model consists of three management loops namely outer, inner and operational loops. An analysis of several existing methods for service level management was conducted. Among the methods, which were considered in the paper, there are the use of management system with coordinator, injection of neural network into management subsystem, the use of a genetic algorithm, etc. The management system model and method for service level management, which is used in proposed management system, were developed basing on conducted overview of management methods and defined term of software-defined IT infrastructure. The method for management of software-defined IT infrastructure uses algorithm, which consists of three steps that can be executed separately or consequently. Those steps are reorganization of tasks, coordination of tasks according to IT infrastructure characteristics and adaptive planning, which is conducted based on actual data about IT infrastructure functioning. In order to study the efficiency of the proposed method the modelling with CloudSim was performed. Results of work were used in the management system of IT-infrastructure, which is named Smartbase ITS Control.

The Internet of Things (IoT) is an emerging technology that is designed to improve the quality of consumer's life and also to improve economic indicators and productivity of enterprises, for more efficient use of their resources. In this paper the authors propose an approach to Microcloud-based IoT infrastructure management which provides the required quality of IT services with rational use of IT resources. The proposed approach is based on decomposition-compensation method in which the task of operational service quality management is to maintain a given level of service quality with the use of minimum IT resources amount in IoT environment. Thus the maximum efficiency can be achieved by selecting such control when actual level of service corresponds to the agreed one with the business unit and can be achieved by minimal costs. The proposed approach allows the efficient use of resources for IT services provision in IoT ecosystem through the implementation of service level coordination, resource planning and service level management processes in an integrated IT infrastructure management system. The main goal of this paper is to propose Microcloud-based IoT infrastructure resource management approach using decomposition-compensation method.

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.