Autonomic Management of Cloud Virtual Infrastructures

Introduction. The competitiveness of land use within a market economy is determined by the equation of capital expenditures, labor power that integrates land into a single engineering procedure, as well as natural and anthropogenic means with the help of which land resources are leveraged, and soil fertility is restored by rational organization of the system of adaptive-landscape specific agriculture. Therefore, the relevance of research of environmental efficiency of land resources management as the basis of sustainable rural development is beyond any reasonable doubt.Purpose. The purpose of the article is to investigate the environmental efficiency of land resources management and its impact on sustainable rural development.Results. Based on the research findings of the totality of indicators of environmental efficiency of land resources management, we will distinguish those ones that make important analysis findings at the national level for such land resources agents as micro agents; mini-agents; general agents; the state; local agents; global agents. Among all the test items of environmental efficiency of land resources management, the agent of land interests “the state” responds to 26 indicators. In addition to the existing indicators, a number of multiplicative metrics has been added for analysis. By expertise, among the presented indicators we have selected 15 under three criteria – anthropogenic impact, reproduction, harmonization. At the same time, in the author's opinion, the environmental efficiency of land resources management of the rural economic sector is to ensure the efficient land use as a constituent of the environment. With provision for expert estimation, we have offered a set of evaluative criteria for the level of environmental efficiency of land resources management of the rural economic sector.Originality. There has been proposed the map of forecast impacts of indicators of environmental efficiency of land resources management, which shows an extremely high level of influence of environmental component of sustainable development on the integral estimation of land resources management, in particular indicators of the amount of used agricultural chemicals per 1 ha of area under crops, land resources usage per a unit of gross production, proportion of sunflower and bird rape in cropping system. A computer program was used to predict the change in the value of integrated efficiency of land resources management using the proposed methodological approach. This computer program, taking into account interinfluences, makes it possible to calculate the predictive influence ranking of indicators on the assessment of the integrated efficiency of land resources management.Conclusion. The article deals with the possibilities of land resources management within the framework of sustainable development of rural territories. The criteria for environmental efficiency of land resources management include: anthropogenic impact – the main indicator – the usage of agricultural chemicals per 1 ha of area under crops; reproduction – environmental and agrochemical assessment of soils; harmonization – the relative gravity of eroded lands acreage in the structure of agricultural fields. Multiplicative indicators, the essence of which is to assess the intensity and efficiency of the parties to land relations, are taken into account for each type of efficiency of land resources management. We have implemented the matrix of forecasts of integral efficiency of land resources management in the rural economic sector in reliance on the method of criterion diagnostics, which is based on the use of a program that takes into account maps of forecast impacts of indicators of environmental efficiency of conditional, relative and absolute input indicators. The practical importance of the obtained research results is that we not only have calculated the predictive rating of the impact of the criterion indicators on the assessment of the integrated efficiency of land resources management, but also the results of the change to 1 conditional score of the input indicator. This technique permitted to reveal the most significant influence on the effectiveness of specific environmental indicators – livestock density and specific gravity of the area under crops, fertilized with organic fertilizers, which indicates the impossibility of effective land use without livestock-raising development.

Cloud computing is next generation of Internet technology and data centers. As such, Information security (IS) has become one of the most essential issues of data center security on the cloud computing, but we cannot rely solely on single solution to support all of the necessary features of IS. Therefore, we need to a new information security service model, called Security Operation Center (SOC). It can support online interactions and remote monitoring via network operations in data centers. The SOC is required to integrate related IS technologies based on security policies. In this paper, we propose the service level agreement (SLA) strategy for designing the SOC platform to meet the IS requirements of any organization in cloud computing. Therefore, the main purposes of this paper are to propose the framework of SLA strategy and the integration technology for designing an SOC platform. The SOC platform with SLA is to handle and supervise all the security service processes under different levels of cloud security enforcements according to data center scales, business properties, and existing IS functions. Consequently, the SLA strategy and integrating technology of IS can be the reference model for designing a proposed SOC platform in data centers on the cloud computing.

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

The main purpose of the study is to substantiate the need for and identify the main stages and methods of integrated assessment of the efficiency of transboundary water resources management. Studies on the aforementioned topics in world and national science have not been disseminated. The article analyzes the general approaches to the assessment of the efficiency of water resources management, substantiates the necessity and peculiarities of the assessment of the efficiency of management of transboundary water resources. As a result of the generalization of scientific approaches on these issues, along with the economic importance of recognizing the social and environmental components of the evaluation of efficiency.The generalized methodology for evaluating the effectiveness and on the basis of the social, ecological and economic constituent management objectives for assessing the effectiveness of management of transboundary water resources is recognized as the most expedient use of integrated assessment by simultaneously and coherently studying a set of parameters that reflect all or most aspects of the investigated processes.The method of estimation of the efficiency of such control is proposed by determining the degree of proximity of objects being studied, according to the relative indicators to the object acting as a reference (distance method). This indicator determines the coefficient of integrated efficiency evaluation (Ki), which is calculated by the Euclidean formula. It is recommended to evaluate the effectiveness of the criteria for institutional and organizational support for management needs, the implementation of management objectives and information provision of the management of transboundary water resources. The information field of the indicators of evaluation of the efficiency of the management process, as well as the gradation of the coefficient of complex evaluation of efficiency in determining the level of efficiency of water resources management of a specific transboundary water basin, is proposed. The possibility of estimating the degree and directions of connection between the indicators of different groups or groups of criteria was investigated and a variant of its calculation was proposed on the basis of the Spirman rank correlation coefficient.The proposed integrated assessment method allows to determine the level of efficiency of management of transboundary water resources in the pool as a whole, as well as each of its parties separately, identify shortcomings and identify and use the most effective management option.

High-speed network poses challenges on heavy traffic measurement and analysis, which becomes more serious due to the scale of modern data centers. Cloud platform is an alternative to crack the nut because it gives concurrent considerations to both high-performance computing and high-extensible data storage. Therefore, we use Hadoop to construct a trial cloud platform to tackle the challenges derived from the heavy traffic, and perform network traffic analysis and target packet dissection. Our testbed evaluates the performance against different processing strategies, and shows the statistic results. According to the experimental results, the Hadoop-based platform can fit to process massive traffic data very well, and it's appropriate to manage today's data centers with cloud computing services.

As one of the international economic organizations, the G‐7 countries are composed of the world's leading industrialized countries. In addition, the role of the G‐7 countries in global warming is another issue that is constantly debated. Concerns have been raised about these very wealthy nations' capacity to manage their resources sustainably. Examining the factors that contribute to efficient resource management in these nations has grown in significance. The goal of this research is to look at how eco‐friendly innovations, clean energy transition, economic development, and trade affect effective resource management in the G‐7 nations. The material footprint is used as a gauge for efficient resource management. The Method of Moments Quantile Regression (MMQR) approach, which is a novel method, was used in this research to examine the G‐7 nations' yearly data for the years 1990–2019. Eco‐friendly technologies lower the material footprint in the G‐7 nations, according to MMQR findings. Results are statistically significant for the majority of quantile levels (0.1–0.7). Clean energy transition also reduces the material footprint of the G‐7 countries, with statistically significant results at all quantile levels. Economic development increases the material footprints of G‐7 countries; findings are statistically significant at all quantile levels. The material footprint is consequently increased by trade, and the results are statistically significant at various quantile levels (0.5–0.9). Additionally, the research tests robustness using other panel estimators and achieves similar findings. Lastly, the paper offers policy suggestions for G‐7 nations' efficient resource management.

<p>To design green clouds with optimal energy and resource consumption, the host consolidation process became the most prominent cloud management policy today. In case of a consolidation, the underloaded host's VMs are shifted to the other hosts to control the energy extravagance at a data center. The consolidation process is considered an NP-hard problem, as it depends on several computational resources (i.e., CPU, RAM, Bandwidth) and migration constraints. Although many former cloud researchers were focused on designing the consolidation process, the total consolidation process is based on single computational resource (i.e., CPU) utilization only. In a single resource-based consolidation model, only the selected resource utilization value is considered in decision-making and the other resource utilization values are simply discarded. The problems identified in single resource-based consolidation process are: (i) Ignoring the other prominent resources in consolidation (ii) unnecessary consolidation of hosts (iii) aggressive migrations and (iv) energy extravagance. To overcome the limitations involved with the single resource-based consolidation process, this study proposed the “Multi Resources and Constraints-based Consolidation Model (MRCCM)". The MRCCM is designed to solve the energy extravagance issues and aggressive migrations in host consolidation process. In MRCCM, multiple resources and resource constraints are considered while host consolidation for best target host selection and efficient resource management. Cloudsim toolkit with 800 HP-ProLiant host models and EC2 VM models are configured for experimental analysis. Proposed MRCCM model with MMT host selection method recorded the total 11570 migrations, 1853 shut_downs and 111.84 Kwh of energy consumption.  Experimental results are proven that the MRCCM controlled the energy extravagance, unnecessary consolidations, and aggressive migrations at a considerable rate when compared to the other prominent consolidation models.</p><p><br></p>

Virtualisation enables multiples virtual machines (VMs) to co-locate on a same physical machine with total isolation. Hence using VMs to launch web services or applications is the common trend nowadays in enterprise information technology (IT). Data centre provides infrastructure to create, configure and manage VMs. It has seen as a utility that clients can pay for only as needed. The growing complexity of modern networked computer systems with virtualisation technology necessitates the needs efficient resource management. We have proposed an intelligent resource manager to control the resource allocation in Xen virtualised environment for dynamically allocating resources to individual VM. Our resource management architecture comprises of fuzzy logic based controller. Experimental results shows that with the proposed system data centre can efficiently allocate CPU resources to VMs that have been produced by customers. The scaling of CPU resources is automatically done in accordance with dynamically changing workload at a minimum granularity of 2 seconds. It improves the resource utilisation by 30% as compared to the ideal method while maintaining throughput as equivalent to the ideal workload allocation.

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.

We introduce EMARS, an efficient resource management system for serverless cloud computing frameworks with the goal to enhance resource (focus on memory) allocation among containers. We have built our prototype on top of an open-source serverless platform, OpenLambda. It is based upon application workloads and serverless functions' memory needs. As a background motivation we analyzed the latencies and memory requirements of functions running on AWS lambda. The memory limits also lead to variations in number of containers spawned on OpenLambda. We use memory limit settings to propose a model of predictive efficient memory management.

Recently, researchers have been working on cloud data assurance models to ensure that the data is in compliance with the policies. In such a model, the data is placed in the intended data centres without focusing on data residency issues. Thus, in order to solve such issues, we have proposed a framework known as the data resident storage (DRS) which is intended to achieve data residency protection, thus providing data residency as a service (DRaaS). The finite state machine model is developed for the purpose of verifying the methodology in terms of data location and data access. The validation is carried out in the CloudSim toolkit with the help of defined services. The developed test automation framework places the data only in data centres that are in compliance with data residency laws. Finally, this framework can be hosted by the cloud service provider to provide DRaaS to the end users.

Massively Open Online Courses (MOOC) is new trending model for delivering learning content online to any person with no limit on attendance for everyone that wishes to attend to a course. Following the MOOC trend there is an expectation that till 2015 there will be more than 6 million unique students worldwide. In order to provide appropriate quality learning user experience for the students we propose how to optimize the cloud computing resources, such as Virtual Machine (VM) distribution per contents to support the MOOC. For this purpose we have used a simulation tool, called CloudAnalyst, which supports visual modelling and simulation of large-scale applications that are deployed on cloud infrastructures. The results will provide information for the geographic distribution of traffic and location of data centres and number of resources for each data centre.

Cloud computing is an emerging technology, which provides services over internet such as software, hardware, network and storage. The key role for cloud computing is virtualization which reduces the total cost and gives reliable, flexible and secured services. However compute service are chosen between the providers located in multiple data centres. One of the major security concerns related to the virtualization and the Storage where the outside attackers can use the files in the storage and the data owners are not capable of knowing attacks. In this paper we proposed a high level authentication for the cloud user and remote monitor controlled of your cloud storage. Here our model provides the dual authentication for the cloud and to get the runtime record of the logs and the secured application controls, the logs are remotely accessed and controlled by the owner of the data.

Current networks are providing plenty of services that users can access and use. These services are more and more pervasive and deployed in different networks distributed across an environment. This raises the problem of managing such environments in order to grant access to services from anywhere and to adapt the environment’s networks to dynamic changes. Also, there is a need of an autonomous behavior to reduce human intervention and assure environment’s consistency. This autonomous and distributed behavior leads to the definition and integration of existing and new technologies to enable autonomous distributed management. This is fulfilled by providing paradigms that bring awareness about the surroundings and enabled tools to manage and adapt the environment’s resources. The main problem is to dynamically provide auto‐configuration of networks to deal with the frequent changes which results from users’ roaming, changing services constraints, and changing services themselves, and adding, upgrading or removing policies. The outcome of these issues is a dynamic system with complex management. Hence, this paper proposes the integration of different techniques to provide an autonomous, distributed, and secure management including auto‐configuration, adaptation, and auto‐protection of pervasive environments. Then, policies control the behavior of the environment, devices configuration and the enforcement of security mechanisms to protect sensitive data. Also, mobile agents are employed to distribute management tasks across the distributed environment. In order to provide auto‐protection capabilities, the autonomous behavior of the environment have to be secured. Actually, this security issue is addressed by defining an agent‐based Public Key Infrastructure (PKI) with X.509 certificates. The agent ensures then that the security functions are applied across all the distributed networks, where specific agents are responsible for conveying necessary information and certificates to local environments. Finally, the paper proposes a semantic‐based privacy management approach using ontologies to decide how privacy information is handled in the environment.

An analysis of the peculiarities of human resources management process implementation at domestic enterprises under conditions of the russian-Ukrainian war is presented in the article. A study of the main factors influencing on the enterprises’ personnel functioning and development is carried out. It is determined that the most negative one is the military and political situation in the country. A significant number of challenges and problems, affecting the efficiency of human resources management is identified, in particular: the necessity to ensure the personnel's physical safety, mobilization of qualified workers for the purpose of country defense, the gradual release and optimization of human resources, an insufficient number of active qualified labor force, a significant deterioration of social and psychological climate within collectives, deformation of communication and reduction in the efficiency of the management system, low (absent) level of readiness for staff inclusion, etc. In addition, it is determined that despite the current situation in the country, it is important and necessary to adjust the personnel management approach to the social norms of Western countries in the process of European integration processes. To increase the efficiency of human resources management and improve the psychological climate at the enterprise, some measures are suggested, aimed at providing psychological support and rehabilitation of employees: introducing flexible work schedules, programs for professional development and increasing the personnel qualifications, strengthening the motivation system through new approaches, using modern information technologies to optimize personnel management, active involvement of external experts to provide advisory assistance on issues of personnel management in wartime, etc. Emphasis is put on the necessity to develop and implement existing professional programs for veterans of war readaptation and resocialization, including the ones at workplaces. It is noted that only a comprehensive approach to the implementation of the above-mentioned measures makes it possible to ensure an efficiency increase of the enterprises’ human resources management systems, which as a result, will have a positive impact on the economic situation in the country.