Co-Simulation of distributed flexibility coordination schemes

Cílem práce je implementovat a otestovat simulační prostředí, které umožní spojení simulátorů různých typů úloh. Toto prostředí je aplikováno na simulaci koordinovaného optimálního řízení spotřeby energie 20 domácností s různými požadavky na velikost spotřeby a možnostmi uložení energie. Výsledky ukazují, že koordinované řízení spotřeby energie více domácností může dosáhnout značných úspor ve srovnání s řízením spotřeby jednotlivých domácností bez ohledu na ostatní.The goal of the thesis is to implement and test co-simulation environment making it possible to connect simulators of different type. The environment is applied on simulation of coordinated optimal control of energy consumption of 20 households with different preferences on energy supply and its storage capacity. The results show that coordinated control of energy consumption may achieve considerable savings in comparison with control of individual households regardless to the others

Multi-Agent-Based Cloud Architecture of Smart Grid

AbstractPower system is a huge hierarchical controlled network. Large volumes of data are within the system and the requirement of real-time analysis and processing is high. With the smart grid construction, these requirements will be further improved. The emergence of cloud computing provides an effective way to solve these problems low-costly, high efficiently and reliably. This paper analyzes the feasibility of cloud computing for the construction of smart grid, extends cloud computing to cloud-client computing. Through “Energy Hub”, Microgrid is separated into a network of three storeys that match with the conception of cloud-client computing. This paper introduces multi-agent technology to control each node in the system. On these bases, cloud architecture of smart grid is proposed. Finally, an example is given to explain the application of cloud computing in power grid CPS structure

Improved self-management of datacenter systems applying machine learning

Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Infrastructural Security for Virtualized Grid Computing

The goal of the grid computing paradigm is to make computer power as easy to access as an electrical power grid. Unlike the power grid, the computer grid uses remote resources located at a service provider. Malicious users can abuse the provided resources, which not only affects their own systems but also those of the provider and others. Resources are utilized in an environment where sensitive programs and data from competitors are processed on shared resources, creating again the potential for misuse. This is one of the main security issues, since in a business environment competitors distrust each other, and the fear of industrial espionage is always present. Currently, human trust is the strategy used to deal with these threats. The relationship between grid users and resource providers ranges from highly trusted to highly untrusted. This wide trust relationship occurs because grid computing itself changed from a research topic with few users to a widely deployed product that included early commercial adoption. The traditional open research communities have very low security requirements, while in contrast, business customers often operate on sensitive data that represents intellectual property; thus, their security demands are very high. In traditional grid computing, most users share the same resources concurrently. Consequently, information regarding other users and their jobs can usually be acquired quite easily. This includes, for example, that a user can see which processes are running on another user´s system. For business users, this is unacceptable since even the meta-data of their jobs is classified. As a consequence, most commercial customers are not convinced that their intellectual property in the form of software and data is protected in the grid. This thesis proposes a novel infrastructural security solution that advances the concept of virtualized grid computing. The work started back in 2007 and led to the development of the XGE, a virtual grid management software. The XGE itself uses operating system virtualization to provide a virtualized landscape. Users’ jobs are no longer executed in a shared manner; they are executed within special sandboxed environments. To satisfy the requirements of a traditional grid setup, the solution can be coupled with an installed scheduler and grid middleware on the grid head node. To protect the prominent grid head node, a novel dual-laned demilitarized zone is introduced to make attacks more difficult. In a traditional grid setup, the head node and the computing nodes are installed in the same network, so a successful attack could also endanger the user´s software and data. While the zone complicates attacks, it is, as all security solutions, not a perfect solution. Therefore, a network intrusion detection system is enhanced with grid specific signatures. A novel software called Fence is introduced that supports end-to-end encryption, which means that all data remains encrypted until it reaches its final destination. It transfers data securely between the user´s computer, the head node and the nodes within the shielded, internal network. A lightweight kernel rootkit detection system assures that only trusted kernel modules can be loaded. It is no longer possible to load untrusted modules such as kernel rootkits. Furthermore, a malware scanner for virtualized grids scans for signs of malware in all running virtual machines. Using virtual machine introspection, that scanner remains invisible for most types of malware and has full access to all system calls on the monitored system. To speed up detection, the load is distributed to multiple detection engines simultaneously. To enable multi-site service-oriented grid applications, the novel concept of public virtual nodes is presented. This is a virtualized grid node with a public IP address shielded by a set of dynamic firewalls. It is possible to create a set of connected, public nodes, either present on one or more remote grid sites. A special web service allows users to modify their own rule set in both directions and in a controlled manner. The main contribution of this thesis is the presentation of solutions that convey the security of grid computing infrastructures. This includes the XGE, a software that transforms a traditional grid into a virtualized grid. Design and implementation details including experimental evaluations are given for all approaches. Nearly all parts of the software are available as open source software. A summary of the contributions and an outlook to future work conclude this thesis

Virtualization of network I/O on modern operating systems

Network I/O of modern operating systems is incomplete. In this networkage, users and their applications are still unable to control theirown traffic, even on their local host. Network I/O is a sharedresource of a host machine, and traditionally, to address problemswith a shared resource, system research has virtualized the resource.Therefore, it is reasonable to ask if the virtualization can providesolutions to problems in network I/O of modern operating systems, inthe same way as the other components of computer systems, such asmemory and CPU. With the aim of establishing the virtualization ofnetwork I/O as a design principle of operating systems, thisdissertation first presents a virtualization model, hierarchicalvirtualization of network interface. Systematic evaluation illustratesthat the virtualization model possesses desirable properties forvirtualization of network I/O, namely flexible control granularity,resource protection, partitioning of resource consumption, properaccess control and generality as a control model. The implementedprototype exhibits practical performance with expected functionality,and allowed flexible and dynamic network control by users andapplications, unlike existing systems designed solely for systemadministrators. However, because the implementation was hardcoded inkernel source code, the prototype was not perfect in its functionalcoverage and flexibility. Accordingly, this dissertation investigatedhow to decouple OS kernels and packet processing code throughvirtualization, and studied three degrees of code virtualization,namely, limited virtualization, partial virtualization, and completevirtualization. In this process, a novel programming model waspresented, based on embedded Java technology, and the prototypeimplementation exhibited the following characteristics, which aredesirable for network code virtualization. First, users program inJava to carry out safe and simple programming for packetprocessing. Second, anyone, even untrusted applications, can performinjection of packet processing code in the kernel, due to isolation ofcode execution. Third, the prototype implementation empirically provedthat such a virtualization does not jeopardize system performance.These cases illustrate advantages of virtualization, and suggest thatthe hierarchical virtualization of network interfaces can be aneffective solution to problems in network I/O of modern operatingsystems, both in the control model and in implementation

Real-Time QoS Monitoring and Anomaly Detection on Microservice-based Applications in Cloud-Edge Infrastructure

Ph. D. Thesis.Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. A microservicebased cloud application architecture advocates decomposition of monolithic application components into independent software components called \microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. The deployment environment for microservices in multi-cloud environments is very complex as there are numerous components running in heterogeneous environments (VM/container) and communicating frequently with each other using REST-based/REST-less APIs. In some cases, multiple components can also be executed inside a VM/container making any failure or anomaly detection very complicated. It is necessary to monitor the performance variation of all the service components to detect any reason for failure. Microservice and container architecture allows to design loose-coupled services and run them in a lightweight runtime environment for more e cient scaling. Thus, containerbased microservice deployment is now the standard model for hosting cloud applications across industries. Despite the strongest scalability characteristic of this model which opens the doors for further optimizations in both application structure and performance, such characteristic adds an additional level of complexity to monitoring application performance. Performance monitoring system can lead to severe application outages if it is not able to successfully and quickly detecting failures and localizing their causes. Machine learning-based techniques have been applied to detect anomalies in microservice-based cloud-based applications. The existing research works used di erent tracking algorithms to search the root cause if anomaly observed behaviour. However, linking the observed failures of an application with their root causes by the use of these techniques is still an open research problem. Osmotic computing is a new IoT application programming paradigm that's driven by the signi cant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with cloud-based services. Much of the di culty in Quality of Service (QoS) and performance monitoring of IoT applications in an osmotic computing environment is due to the massive scale and heterogeneity (IoT + edge + cloud) of computing environments. To handle monitoring and anomaly detection of microservices in cloud and edge datacenters, this thesis presents multilateral research towards monitoring and anomaly detection on microservice-based applications performance in cloud-edge infrastructure. The key contributions of this thesis are as following: • It introduces a novel system, Multi-microservices Multi-virtualization Multicloud monitoring (M3 ) that provides a holistic approach to monitor the performance of microservice-based application stacks deployed across multiple cloud data centers. • A framework forMonitoring, Anomaly Detection and Localization System (MADLS) which utilizes a simpli ed approach that depends on commonly available metrics o ering a simpli ed deployment environment for the developer. • Developing a uni ed monitoring model for cloud-edge that provides an IoT application administrator with detailed QoS information related to microservices deployed across cloud and edge datacenters.Royal Embassy of Saudi Arabia Cultural Bureau in London, government of Saudi Arabi

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Polymorphic computing abstraction for heterogeneous architectures

Integration of multiple computing paradigms onto system on chip (SoC) has pushed the boundaries of design space exploration for hardware architectures and computing system software stack. The heterogeneity of computing styles in SoC has created a new class of architectures referred to as Heterogeneous Architectures. Novel applications developed to exploit the different computing styles are user centric for embedded SoC. Software and hardware designers are faced with several challenges to harness the full potential of heterogeneous architectures. Applications have to execute on more than one compute style to increase overall SoC resource utilization. The implication of such an abstraction is that application threads need to be polymorphic. Operating system layer is thus faced with the problem of scheduling polymorphic threads. Resource allocation is also an important problem to be dealt by the OS. Morphism evolution of application threads is constrained by the availability of heterogeneous computing resources. Traditional design optimization goals such as computational power and lower energy per computation are inadequate to satisfy user centric application resource needs. Resource allocation decisions at application layer need to permeate to the architectural layer to avoid conflicting demands which may affect energy-delay characteristics of application threads. We propose Polymorphic computing abstraction as a unified computing model for heterogeneous architectures to address the above issues. Simulation environment for polymorphic applications is developed and evaluated under various scheduling strategies to determine the effectiveness of polymorphism abstraction on resource allocation. User satisfaction model is also developed to complement polymorphism and used for optimization of resource utilization at application and network layer of embedded systems