Systemunterstützung für moderne Speichertechnologien

Trust and scalability are the two significant factors which impede the dissemination of clouds. The possibility of privileged access to customer data by a cloud provider limits the usage of clouds for processing security-sensitive data. Low latency cloud services rely on in-memory computations, and thus, are limited by several characteristics of Dynamic RAM (DRAM) such as capacity, density, energy consumption, for example. Two technological areas address these factors. Mainstream server platforms, such as Intel Software Guard eXtensions (SGX) und AMD Secure Encrypted Virtualisation (SEV) offer extensions for trusted execution in untrusted environments. Various technologies of Non-Volatile RAM (NV-RAM) have better capacity and density compared to DRAM and thus can be considered as DRAM alternatives in the future. However, these technologies and extensions require new programming approaches and system support since they add features to the system architecture: new system components (Intel SGX) and data persistence (NV-RAM). This thesis is devoted to the programming and architectural aspects of persistent and trusted systems. For trusted systems, an in-depth analysis of new architectural extensions was performed. A novel framework named EActors and a database engine named STANlite were developed to effectively use the capabilities of trusted~execution. For persistent systems, an in-depth analysis of prospective memory technologies, their features and the possible impact on system architecture was performed. A new persistence model, called the hypervisor-based model of persistence, was developed and evaluated by the NV-Hypervisor. This offers transparent persistence for legacy and proprietary software, and supports virtualisation of persistent memory.Vertrauenswürdigkeit und Skalierbarkeit sind die beiden maßgeblichen Faktoren, die die Verbreitung von Clouds behindern. Die Möglichkeit privilegierter Zugriffe auf Kundendaten durch einen Cloudanbieter schränkt die Nutzung von Clouds bei der Verarbeitung von sicherheitskritischen und vertraulichen Informationen ein. Clouddienste mit niedriger Latenz erfordern die Durchführungen von Berechnungen im Hauptspeicher und sind daher an Charakteristika von Dynamic RAM (DRAM) wie Kapazität, Dichte, Energieverbrauch und andere Aspekte gebunden. Zwei technologische Bereiche befassen sich mit diesen Faktoren: Etablierte Server Plattformen wie Intel Software Guard eXtensions (SGX) und AMD Secure Encrypted Virtualisation (SEV) stellen Erweiterungen für vertrauenswürdige Ausführung in nicht vertrauenswürdigen Umgebungen bereit. Verschiedene Technologien von nicht flüchtigem Speicher bieten bessere Kapazität und Speicherdichte verglichen mit DRAM, und können daher in Zukunft als Alternative zu DRAM herangezogen werden. Jedoch benötigen diese Technologien und Erweiterungen neuartige Ansätze und Systemunterstützung bei der Programmierung, da diese der Systemarchitektur neue Funktionalität hinzufügen: Systemkomponenten (Intel SGX) und Persistenz (nicht-flüchtiger Speicher). Diese Dissertation widmet sich der Programmierung und den Architekturaspekten von persistenten und vertrauenswürdigen Systemen. Für vertrauenswürdige Systeme wurde eine detaillierte Analyse der neuen Architekturerweiterungen durchgeführt. Außerdem wurden das neuartige EActors Framework und die STANlite Datenbank entwickelt, um die neuen Möglichkeiten von vertrauenswürdiger Ausführung effektiv zu nutzen. Darüber hinaus wurde für persistente Systeme eine detaillierte Analyse zukünftiger Speichertechnologien, deren Merkmale und mögliche Auswirkungen auf die Systemarchitektur durchgeführt. Ferner wurde das neue Hypervisor-basierte Persistenzmodell entwickelt und mittels NV-Hypervisor ausgewertet, welches transparente Persistenz für alte und proprietäre Software, sowie Virtualisierung von persistentem Speicher ermöglicht

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

Semantic reasoning on the edge of internet of things

Abstract. The Internet of Things (IoT) is a paradigm where physical objects are connected with each other with identifying, sensing, networking and processing capabilities over the Internet. Millions of new devices will be added into IoT network thus generating huge amount of data. How to represent, store, interconnect, search, and organize information generated by IoT devices become a challenge. Semantic technologies could play an important role by encoding meaning into data to enable a computer system to possess knowledge and reasoning. The vast amount of devices and data are also challenges. Edge Computing reduces both network latency and resource consumptions by deploying services and distributing computing tasks from the core network to the edge. We recognize four challenges from IoT systems. First the centralized server may generate long latency because of physical distances. Second concern is that the resource-constrained IoT devices have limited computing ability in processing heavy tasks. Third, the data generated by heterogeneous devices can hardly be understood and utilized by other devices or systems. Our research focuses on these challenges and provide a solution based on Edge computing and semantic technologies. We utilize Edge computing and semantic reasoning into IoT. Edge computing distributes tasks to the reasoning devices, which we call the Edge nodes. They are close to the terminal devices and provide services. The newly added resources could balance the workload of the systems and improve the computing capability. We annotate meaning into the data with Resource Description Framework thus providing an approach for heterogeneous machines to understand and utilize the data. We use semantic reasoning as a general purpose intelligent processing method. The thesis work focuses on studying semantic reasoning performance in IoT system with Edge computing paradigm. We develop an Edge based IoT system with semantic technologies. The system deploys semantic reasoning services on Edge nodes. Based on IoT system, we design five experiments to evaluate the performance of the integrated IoT system. We demonstrate how could the Edge computing paradigm facilitate IoT in terms of data transforming, semantic reasoning and service experience. We analyze how to improve the performance by properly distributing the task for Cloud and Edge nodes. The thesis work result shows that the Edge computing could improve the performance of the semantic reasoning in IoT

Scalable Storage for Digital Libraries

I propose a storage system optimised for digital libraries. Its key features are its heterogeneous scalability; its integration and exploitation of rich semantic metadata associated with digital objects; its use of a name space; and its aggressive performance optimisation in the digital library domain

Security and Privacy Issues in IoT

Internet of Things (IoT) is a global network of physical and virtual ‘things’ connected to the internet. Each object has unique ID which is used for identification. IoT is the emerging technology which will change the way we interact with devices. In future almost every electronic device will be a smart device which can compute and communicate with hand-held and other infrastructure devices. As most of the devices may be battery operated, due to less processing power the security and privacy is a major issue in IoT. Authentication, Identification and device heterogeneity are the major security and privacy concerns in IoT. Major challenges include integration, scalability, ethics communication mechanism, business models and surveillance. In this paper major issues related to security and privacy of IoT are focused

Decentralized Personal Data Marketplaces: How Participation in a DAO Can Support the Production of Citizen-Generated Data

Big Tech companies operating in a data-driven economy offer services that rely on their users’ personal data and usually store this personal information in “data silos” that prevent transparency about their use and opportunities for data sharing for public interest. In this paper, we present a solution that promotes the development of decentralized personal data marketplaces, exploiting the use of Distributed Ledger Technologies (DLTs), Decentralized File Storages (DFS) and smart contracts for storing personal data and managing access control in a decentralized way. Moreover, we focus on the issue of a lack of efficient decentralized mechanisms in DLTs and DFSs for querying a certain type of data. For this reason, we propose the use of a hypercube-structured Distributed Hash Table (DHT) on top of DLTs, organized for efficient processing of multiple keyword-based queries on the ledger data. We test our approach with the implementation of a use case regarding the creation of citizen-generated data based on direct participation and the involvement of a Decentralized Autonomous Organization (DAO). The performance evaluation demonstrates the viability of our approach for decentralized data searches, distributed authorization mechanisms and smart contract exploitation

Architectures for the Future Networks and the Next Generation Internet: A Survey

Networking research funding agencies in the USA, Europe, Japan, and other countries are encouraging research on revolutionary networking architectures that may or may not be bound by the restrictions of the current TCP/IP based Internet. We present a comprehensive survey of such research projects and activities. The topics covered include various testbeds for experimentations for new architectures, new security mechanisms, content delivery mechanisms, management and control frameworks, service architectures, and routing mechanisms. Delay/Disruption tolerant networks, which allow communications even when complete end-to-end path is not available, are also discussed

From online social network analysis to a user-centric private sharing system

Online social networks (OSNs) have become a massive repository of data constructed from individuals’ inputs: posts, photos, feedbacks, locations, etc. By analyzing such data, meaningful knowledge is generated that can affect individuals’ beliefs, desires, happiness and choices—a data circulation started from individuals and ended in individuals! The OSN owners, as the one authority having full control over the stored data, make the data available for research, advertisement and other purposes. However, the individuals are missed in this circle while they generate the data and shape the OSN structure. In this thesis, we started by introducing approximation algorithms for finding the most influential individuals in a social graph and modeling the spread of information. To do so, we considered the communities of individuals that are shaped in a social graph. The social graph is extracted from the data stored and controlled centrally, which can cause privacy breaches and lead to individuals’ concerns. Therefore, we introduced UPSS: the user-centric private sharing system, in which the individuals are considered as the real data owners and provides secure and private data sharing on untrusted servers. The UPSS’s public API allows the application developers to implement applications as diverse as OSNs, document redaction systems with integrity properties, censorship-resistant systems, health care auditing systems, distributed version control systems with flexible access controls and a filesystem in userspace. Accessing users’ data is possible only with explicit user consent. We implemented the two later cases to show the applicability of UPSS. Supporting different storage models by UPSS enables us to have a local, remote and global filesystem in userspace with one unique core filesystem implementation and having it mounted with different block stores. By designing and implementing UPSS, we show that security and privacy can be addressed at the same time in the systems that need selective, secure and collaborative information sharing without requiring complete trust

Global Data Plane: A Widely Distributed Storage and Communication Infrastructure

With the advancement of technology, richer computation devices are making their way into everyday life. However, such smarter devices merely act as a source and sink of information; the storage of information is highly centralized in data-centers in today’s world. Even though such data-centers allow for amortization of cost per bit of information, the density and distribution of such data-centers is not necessarily representative of human population density. This disparity of where the information is produced and consumed vs where it is stored only slightly affects the applications of today, but it will be the limiting factor for applications of tomorrow.The computation resources at the edge are more powerful than ever, and present an opportunity to address this disparity. We envision that a seamless combination of these edge-resources with the data-center resources is the way forward. However, the resulting issues of trust and data-security are not easy to solve in a world full of complexity. Toward this vision of a federated infrastructure composed of resources at the edge as well as those in data-centers, we describe the architecture and design of a widely distributed system for data storage and communication that attempts to alleviate some of these data security challenges; we call this system the Global Data Plane (GDP).The key abstraction in the GDP is a secure cohesive container of information called a DataCapsule, which provides a layer of uniformity on top of a heterogeneous infrastructure. A DataCapsule represents a secure history of transactions in a persistent form that can be used for building other applications on top. Existing applications can be refactored to use DataCapsules as the ground truth of persistent state; such a refactoring enables cleaner application design that allows for better security analysis of information flows. Not only cleaner design, the GDP also enables locality of access for performance and data privacy—an ever growing concern in the information age.The DataCapsules are enabled by an underlying routing fabric, called the GDP network, which provides secure routing for datagrams in a flat namespace. The GDP network is a core component of the GDP that enables various GDP components to interact with each other. In addition to the DataCapsules, this underlying network is available to applications for native communication as well. Flat namespace networks are known to provide a number of desirable properties, such as location independence, built-in multicast, etc. However, existing architectures for such networks suffer from routing security issues, typically because malicious entities can claim to possess arbitrary names and thus, receive traffic intended for arbitrary destinations. GDP network takes a different approach by defining an ownership of the name and the associated mechanisms for participants to delegate routing for such names to others. By directly integrating with GDP network, applications can enjoy the benefits of flat namespace networks without compromising routing security.The Global Data Plane and DataCapsules together represent our vision for secure ubiquitous storage. As opposed to the current approach of perimeter security for infrastructure, i.e. drawing a perimeter around parts of infrastructure and trusting everything inside it, our vision is to use cryptographic tools to enable intrinsic security for the information itself regardless of the context in which such information lives. In this dissertation, we show how to make this vision a reality, and how to adapt real world applications to reap the benefits of secure ubiquitous storage