6 research outputs found
Інформаційна технологія управління ІТ-інфраструктурою хмарного центру оброблення даних
У дисертаційній роботі вирішено науково-практичну проблему забезпечення ефективного функціонування ІТ-інфраструктури хмарного ЦОД через створення методології управління та на її основі розроблення і застосування інформаційної технології управління з метою надання хмарних послуг із заданими показниками якості кінцевому користувачеві. Розроблено інформаційну технологію, що забезпечує ефективне функціонування ІТ-інфраструктури ЦОД провайдера хмарних послуг, яка на відміну від відомих базується на методології управління ІТ-інфраструктурою, а також враховує суттєві характеристики хмарних обчислень, гетерогенність ІТ-інфраструктури, нові архітектури організації обчислень, її багаторівневість та ієрархічність і використовує адаптацію до непередбачуваних навантажень за рахунок застосування розроблених адаптивного методу комбінованого прогнозування та методів управління для різних стратегій управління, що дозволило забезпечити виконання заданих вимог угоди про рівень обслуговування та зниження операційних і капітальних витрат.
Практична цінність інформаційної технології, а також розроблених алгоритмів, методів і підходів полягає у створенні методологічної бази розроблення і реалізації систем управління ІТ-інфраструктурою хмарних ЦОД і підвищення ефективності їх функціонування з подальшим їх застосуванням для розроблення підсистем, компонентів та інших складових систем управління ІТ-інфраструктурою провайдерів хмарних послуг.The dissertation solves the scientific and practical problem of effective management of the IT infrastructure of cloud data centers under conditions of uncertainty and variable workloads by creating a management methodology and on its basis the development and application of management information technology to provide cloud services with specified quality indicators to the end user. The proposed information technology is developed ensuring the efficient functioning of the IT infrastructure of the cloud service provider's data center by increasing the resource usage efficiency under conditions of variable workload, which unlike the known methods takes into account the developed methodology, essential characteristics of cloud computing, heterogeneity of IT infrastructure, its multilevel and hierarchy and uses adaptation to unpredictable and mixed workloads at the expense of forecasting, which allowed to ensure SLA requirements while reducing operating and capital costs.
The developed information technology is based on the concept of management of IT infrastructure of a cloud data center, which combines the following developed findings, approaches, and methods: the operator form of setting, analyzing and solving problems of IT infrastructure of the cloud data center; identifying and implementing dedicated resource and workload management strategies, as well as their implementation schemes; decomposition of cloud IT infrastructure at three levels (infrastructure, platforms, and applications); taking into account traditional, convergent and hyperconverged architectures of modern cloud data centers; application of the adaptive method of combined workload forecasting to determine the control influences on the IT infrastructure of cloud data centers; developing the Method of Integrated Resource Management for heterogeneous data centers based on SLA violations, power consumption and required power at the next management step; application of stochastic methods (beam search, simulated annealing, reinforcement learning) for implementation of particular strategies for IT infrastructure management of cloud data centers; application of a distributed two-level storage management method for hyperconverged systems; taking into account new metrics of the IT infrastructure state namely instantaneous and average viability coefficients of virtual machine, physical server imbalance indicator, the ratio of necessary resources to the average available resources, threshold of available resources) to determine the current management strategy; accounting of software-defined controllers for management of three primary data center resources namely computing, storage, network; the combination of centralized management using global data center manager and the decentralized management on the physical server level, depending on the chosen management strategy at the current management step.
To solve the problem of effective management of the IT infrastructure of the cloud data center and solving the correspondent set of tasks, the following were used: systems theory, methods of hierarchical systems theory, methods of mathematical programming, methods of operations research and decision theory, methods of mathematical and simulation modeling, methods of artificial intelligence theory, stochastic and heuristic search methods, forecasting methods, mathematical statistics methods, and cloud service models. The reliability and validity of the obtained results are conditioned by the correct use of the mathematical apparatus and are confirmed by the results of computational experiments.
The scientific novelty of the obtained results is determined by the following theoretical and practical results obtained by the author. For the first time the following has been developed: a methodology for managing the IT infrastructure of a cloud data center; a structural and functional model of a multilevel hierarchical software-defined system for managing the IT infrastructure of a cloud data center; an original information technology for managing the IT infrastructure of a cloud data center; an adaptive method of combined workload forecasting for cloud data center computing resources; a method for integrated management of IT infrastructure of cloud data center; a distributed datacenter cloud management method; a cloud datacenter power management method. The following has been further improved: the decomposition-compensation approach; the algorithms and methods of stochastic local search; an IT infrastructure management model with a coordinator.
Practical value of the information technology, as well as developed methods and approaches, is to create a methodical basis for the development and implementation of management systems for IT infrastructure of cloud data centers and increase their efficiency with their subsequent application for the development of subsystems, components and other parts of the IT infrastructure management systems for cloud service providers.
The most practical results include: management methodology based on the operator form of the management strategy choice and scheme of its implementation; an approach to managing a multi-level hierarchical cloud infrastructure data center IT infrastructure resources; methods of forecasting the workload in cloud data center; methods of managing the resources, workload and power of the cloud data center using forecasts; methods for managing replication and cross-level data migration in a cloud datacenter storage systems.В диссертационной работе решена научно-практическая проблема обеспечения эффективного функционирования ИТ-инфраструктуры облачного ЦОД путем создания методологии управления и на ее основе разработки и применения информационной технологии управления с целью предоставления облачных услуг с заданными показателями качества конечному пользователю. Разработана информационная технология, обеспечивающая эффективное функционирование ИТ-инфраструктуры ЦОД провайдера облачных услуг, которая в отличие от известных базируется на методологии управления ИТ-инфраструктурой, а также учитывает существенные характеристики облачных вычислений, гетерогенность ИТ-инфраструктуры, новые архитектуры организации вычислений, ее многоуровневость и иерархичность и использует адаптацию к непредсказуемым нагрузкам за счет применения разработанных адаптивного метода комбинированного прогнозирования и методов управления для различных стратегий управления, что позволило обеспечить выполнение заданных требований соглашения об уровне обслуживания и снижение операционных и капитальных затрат.
Практическая ценность информационной технологии, а также разработанных алгоритмов, методов и подходов заключается в создании методологической базы разработки и реализации систем управления ИТ-инфраструктурой облачных ЦОД и повышения эффективности их функционирования с последующим их применением для разработки подсистем, компонентов и других составляющих систем управления ИТ-инфраструктурой провайдеров облачных услуг
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
End-to-End Trust Fulfillment of Big Data Workflow Provisioning over Competing Clouds
Cloud Computing has emerged as a promising and powerful paradigm for delivering data- intensive, high performance computation, applications and services over the Internet. Cloud Computing has enabled the implementation and success of Big Data, a relatively recent phenomenon consisting of the generation and analysis of abundant data from various sources. Accordingly, to satisfy the growing demands of Big Data storage, processing, and analytics, a large market has emerged for Cloud Service Providers, offering a myriad of resources, platforms, and infrastructures. The proliferation of these services often makes it difficult for consumers to select the most suitable and trustworthy provider to fulfill the requirements of building complex workflows and applications in a relatively short time.
In this thesis, we first propose a quality specification model to support dual pre- and post-cloud workflow provisioning, consisting of service provider selection and workflow quality enforcement and adaptation. This model captures key properties of the quality of work at different stages of the Big Data value chain, enabling standardized quality specification, monitoring, and adaptation.
Subsequently, we propose a two-dimensional trust-enabled framework to facilitate end-to-end Quality of Service (QoS) enforcement that: 1) automates cloud service provider selection for Big Data workflow processing, and 2) maintains the required QoS levels of Big Data workflows during runtime through dynamic orchestration using multi-model architecture-driven workflow monitoring, prediction, and adaptation.
The trust-based automatic service provider selection scheme we propose in this thesis is comprehensive and adaptive, as it relies on a dynamic trust model to evaluate the QoS of a cloud provider prior to taking any selection decisions. It is a multi-dimensional trust model for Big Data workflows over competing clouds that assesses the trustworthiness of cloud providers based on three trust levels: (1) presence of the most up-to-date cloud resource verified capabilities, (2) reputational evidence measured by neighboring users and (3) a recorded personal history of experiences with the cloud provider.
The trust-based workflow orchestration scheme we propose aims to avoid performance degradation or cloud service interruption. Our workflow orchestration approach is not only based on automatic adaptation and reconfiguration supported by monitoring, but also on predicting cloud resource shortages, thus preventing performance degradation. We formalize the cloud resource orchestration process using a state machine that efficiently captures different dynamic properties of the cloud execution environment. In addition, we use a model checker to validate our monitoring model in terms of reachability, liveness, and safety properties.
We evaluate both our automated service provider selection scheme and cloud workflow orchestration, monitoring and adaptation schemes on a workflow-enabled Big Data application. A set of scenarios were carefully chosen to evaluate the performance of the service provider selection, workflow monitoring and the adaptation schemes we have implemented. The results demonstrate that our service selection outperforms other selection strategies and ensures trustworthy service provider selection. The results of evaluating automated workflow orchestration further show that our model is self-adapting, self-configuring, reacts efficiently to changes and adapts accordingly while enforcing QoS of workflows
The service level management in the internet of things system with microcloud-based architecture
The Internet of Things (IoT) is an emergent technology that offers great opportunities to enhance economic indices and productivity of enterprises, to improve the quality of consumers’ lives, and to enable more efficient use of resources. 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. 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. It 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 the IT infrastructure management system.Интернет вещей – это новая технология, предлагающая качественно новые возможности для улучшения экономических показателей и производительности предприятий, для улучшении качества жизни потребителей и более эффективного использования информационных ресурсов. Предлагается подход к управлению уровнем услуг в системах IoT с микрооблачной инфраструктурой для обеспечения желаемого качества ИТ-услуг с рациональным использованием ИТ-ресурсов. Предложенный подход базируется на декомпозиционно-компенсационном подходе, в котором задачей оперативного управления качеством услуг является поддержание заданного уровня качества обслуживания с использованием минимального объема ИТ-ресурсов. При этом для эффективного использования ресурсов в системах интернета вещей при предоставлении ИТ-услуг в системе управления ИТ-инфраструктурой выделяются уровни координации услуг, планирования ресурсов и управления уровнем обслуживания