825 research outputs found

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

    Get PDF
    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    Modern computing: Vision and challenges

    Get PDF
    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Adaptive Microarchitectural Optimizations to Improve Performance and Security of Multi-Core Architectures

    Get PDF
    With the current technological barriers, microarchitectural optimizations are increasingly important to ensure performance scalability of computing systems. The shift to multi-core architectures increases the demands on the memory system, and amplifies the role of microarchitectural optimizations in performance improvement. In a multi-core system, microarchitectural resources are usually shared, such as the cache, to maximize utilization but sharing can also lead to contention and lower performance. This can be mitigated through partitioning of shared caches.However, microarchitectural optimizations which were assumed to be fundamentally secure for a long time, can be used in side-channel attacks to exploit secrets, as cryptographic keys. Timing-based side-channels exploit predictable timing variations due to the interaction with microarchitectural optimizations during program execution. Going forward, there is a strong need to be able to leverage microarchitectural optimizations for performance without compromising security. This thesis contributes with three adaptive microarchitectural resource management optimizations to improve security and/or\ua0performance\ua0of multi-core architectures\ua0and a systematization-of-knowledge of timing-based side-channel attacks.\ua0We observe that to achieve high-performance cache partitioning in a multi-core system\ua0three requirements need to be met: i) fine-granularity of partitions, ii) locality-aware placement and iii) frequent changes. These requirements lead to\ua0high overheads for current centralized partitioning solutions, especially as the number of cores in the\ua0system increases. To address this problem, we present an adaptive and scalable cache partitioning solution (DELTA) using a distributed and asynchronous allocation algorithm. The\ua0allocations occur through core-to-core challenges, where applications with larger performance benefit will gain cache capacity. The\ua0solution is implementable in hardware, due to low computational complexity, and can scale to large core counts.According to our analysis, better performance can be achieved by coordination of multiple optimizations for different resources, e.g., off-chip bandwidth and cache, but is challenging due to the increased number of possible allocations which need to be evaluated.\ua0Based on these observations, we present a solution (CBP) for coordinated management of the optimizations: cache partitioning, bandwidth partitioning and prefetching.\ua0Efficient allocations, considering the inter-resource interactions and trade-offs, are achieved using local resource managers to limit the solution space.The continuously growing number of\ua0side-channel attacks leveraging\ua0microarchitectural optimizations prompts us to review attacks and defenses to understand the vulnerabilities of different microarchitectural optimizations. We identify the four root causes of timing-based side-channel attacks: determinism, sharing, access violation\ua0and information flow.\ua0Our key insight is that eliminating any of the exploited root causes, in any of the attack steps, is enough to provide protection.\ua0Based on our framework, we present a systematization of the attacks and defenses on a wide range of microarchitectural optimizations, which highlights their key similarities.\ua0Shared caches are an attractive attack surface for side-channel attacks, while defenses need to be efficient since the cache is crucial for performance.\ua0To address this issue, we present an adaptive and scalable cache partitioning solution (SCALE) for protection against cache side-channel attacks. The solution leverages randomness,\ua0and provides quantifiable and information theoretic security guarantees using differential privacy. The solution closes the performance gap to a state-of-the-art non-secure allocation policy for a mix of secure and non-secure applications

    QoS-aware architectures, technologies, and middleware for the cloud continuum

    Get PDF
    The recent trend of moving Cloud Computing capabilities to the Edge of the network is reshaping how applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of diverse application domains and next-generation applications. Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, are expected to serve a wide range of applications with heterogeneous QoS requirements and call for QoS management systems to guarantee/control performance indicators, even in the presence of real-world factors such as limited bandwidth and concurrent virtual resource utilization. The present dissertation proposes a comprehensive QoS-aware architecture that addresses the challenges of integrating cloud infrastructure with edge nodes in IoT applications. The architecture provides end-to-end QoS support by incorporating several components for managing physical and virtual resources. The proposed architecture features: i) a multilevel middleware for resolving the convergence between Operational Technology (OT) and Information Technology (IT), ii) an end-to-end QoS management approach compliant with the Time-Sensitive Networking (TSN) standard, iii) new approaches for virtualized network environments, such as running TSN-based applications under Ultra-low Latency (ULL) constraints in virtual and 5G environments, and iv) an accelerated and deterministic container overlay network architecture. Additionally, the QoS-aware architecture includes two novel middlewares: i) a middleware that transparently integrates multiple acceleration technologies in heterogeneous Edge contexts and ii) a QoS-aware middleware for Serverless platforms that leverages coordination of various QoS mechanisms and virtualized Function-as-a-Service (FaaS) invocation stack to manage end-to-end QoS metrics. Finally, all architecture components were tested and evaluated by leveraging realistic testbeds, demonstrating the efficacy of the proposed solutions

    Digital agriculture: research, development and innovation in production chains.

    Get PDF
    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    Anpassen verteilter eingebetteter Anwendungen im laufenden Betrieb

    Get PDF
    The availability of third-party apps is among the key success factors for software ecosystems: The users benefit from more features and innovation speed, while third-party solution vendors can leverage the platform to create successful offerings. However, this requires a certain decoupling of engineering activities of the different parties not achieved for distributed control systems, yet. While late and dynamic integration of third-party components would be required, resulting control systems must provide high reliability regarding real-time requirements, which leads to integration complexity. Closing this gap would particularly contribute to the vision of software-defined manufacturing, where an ecosystem of modern IT-based control system components could lead to faster innovations due to their higher abstraction and availability of various frameworks. Therefore, this thesis addresses the research question: How we can use modern IT technologies and enable independent evolution and easy third-party integration of software components in distributed control systems, where deterministic end-to-end reactivity is required, and especially, how can we apply distributed changes to such systems consistently and reactively during operation? This thesis describes the challenges and related approaches in detail and points out that existing approaches do not fully address our research question. To tackle this gap, a formal specification of a runtime platform concept is presented in conjunction with a model-based engineering approach. The engineering approach decouples the engineering steps of component definition, integration, and deployment. The runtime platform supports this approach by isolating the components, while still offering predictable end-to-end real-time behavior. Independent evolution of software components is supported through a concept for synchronous reconfiguration during full operation, i.e., dynamic orchestration of components. Time-critical state transfer is supported, too, and can lead to bounded quality degradation, at most. The reconfiguration planning is supported by analysis concepts, including simulation of a formally specified system and reconfiguration, and analyzing potential quality degradation with the evolving dataflow graph (EDFG) method. A platform-specific realization of the concepts, the real-time container architecture, is described as a reference implementation. The model and the prototype are evaluated regarding their feasibility and applicability of the concepts by two case studies. The first case study is a minimalistic distributed control system used in different setups with different component variants and reconfiguration plans to compare the model and the prototype and to gather runtime statistics. The second case study is a smart factory showcase system with more challenging application components and interface technologies. The conclusion is that the concepts are feasible and applicable, even though the concepts and the prototype still need to be worked on in future -- for example, to reach shorter cycle times.Eine große Auswahl von Drittanbieter-Lösungen ist einer der Schlüsselfaktoren für Software Ecosystems: Nutzer profitieren vom breiten Angebot und schnellen Innovationen, während Drittanbieter über die Plattform erfolgreiche Lösungen anbieten können. Das jedoch setzt eine gewisse Entkopplung von Entwicklungsschritten der Beteiligten voraus, welche für verteilte Steuerungssysteme noch nicht erreicht wurde. Während Drittanbieter-Komponenten möglichst spät -- sogar Laufzeit -- integriert werden müssten, müssen Steuerungssysteme jedoch eine hohe Zuverlässigkeit gegenüber Echtzeitanforderungen aufweisen, was zu Integrationskomplexität führt. Dies zu lösen würde insbesondere zur Vision von Software-definierter Produktion beitragen, da ein Ecosystem für moderne IT-basierte Steuerungskomponenten wegen deren höherem Abstraktionsgrad und der Vielzahl verfügbarer Frameworks zu schnellerer Innovation führen würde. Daher behandelt diese Dissertation folgende Forschungsfrage: Wie können wir moderne IT-Technologien verwenden und unabhängige Entwicklung und einfache Integration von Software-Komponenten in verteilten Steuerungssystemen ermöglichen, wo Ende-zu-Ende-Echtzeitverhalten gefordert ist, und wie können wir insbesondere verteilte Änderungen an solchen Systemen konsistent und im Vollbetrieb vornehmen? Diese Dissertation beschreibt Herausforderungen und verwandte Ansätze im Detail und zeigt auf, dass existierende Ansätze diese Frage nicht vollständig behandeln. Um diese Lücke zu schließen, beschreiben wir eine formale Spezifikation einer Laufzeit-Plattform und einen zugehörigen Modell-basierten Engineering-Ansatz. Dieser Ansatz entkoppelt die Design-Schritte der Entwicklung, Integration und des Deployments von Komponenten. Die Laufzeit-Plattform unterstützt den Ansatz durch Isolation von Komponenten und zugleich Zeit-deterministischem Ende-zu-Ende-Verhalten. Unabhängige Entwicklung und Integration werden durch Konzepte für synchrone Rekonfiguration im Vollbetrieb unterstützt, also durch dynamische Orchestrierung. Dies beinhaltet auch Zeit-kritische Zustands-Transfers mit höchstens begrenzter Qualitätsminderung, wenn überhaupt. Rekonfigurationsplanung wird durch Analysekonzepte unterstützt, einschließlich der Simulation formal spezifizierter Systeme und Rekonfigurationen und der Analyse der etwaigen Qualitätsminderung mit dem Evolving Dataflow Graph (EDFG). Die Real-Time Container Architecture wird als Referenzimplementierung und Evaluationsplattform beschrieben. Zwei Fallstudien untersuchen Machbarkeit und Nützlichkeit der Konzepte. Die erste verwendet verschiedene Varianten und Rekonfigurationen eines minimalistischen verteilten Steuerungssystems, um Modell und Prototyp zu vergleichen sowie Laufzeitstatistiken zu erheben. Die zweite Fallstudie ist ein Smart-Factory-Demonstrator, welcher herausforderndere Applikationskomponenten und Schnittstellentechnologien verwendet. Die Konzepte sind den Studien nach machbar und nützlich, auch wenn sowohl die Konzepte als auch der Prototyp noch weitere Arbeit benötigen -- zum Beispiel, um kürzere Zyklen zu erreichen

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

    Get PDF
    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Constraint-based simulation of virtual crowds

    Get PDF
    Central to simulating pedestrian crowds is their motion and behaviour. It is required to understand how pedestrians move to simulate and predict scenarios with crowds of people. Pedestrian behaviours enhance the range of motions people can demonstrate, resulting in greater variety, believability, and accuracy. Models with complex computations and motion have difficulty in being extended with additional behaviours. This is because the structure of these models are not designed in a way that is generally compatible with collision avoidance behaviours. To address this issue, this thesis will research a possible pedestrian model that can simulate collision response with a wide range of additional behaviours. The model will do so by using constraints, a limit on the velocity of a person's movement. The proposed model will use constraints as the core computation. By describing behaviours in terms of constraints, these behaviours can be combined with the proposed model. Pedestrian simulations strike a balance between model complexity and runtime speed. Some models focus entirely on the complexity and accuracy of people, while other models focus on creating believable yet lightweight and performant simulations. Believable crowds look realistic to human observation, but do not match up to numerical analysis under scrutiny. The larger the population, and the more complex the motion of people, the slower the simulation will run. One route for improving performance of software is by using Graphical Processing Units (GPUs). GPUs are devices with theoretical performance that far outperforms equivalent multi-core CPUs. Research literature tends to focus on either the accuracy, or the performance optimisations of pedestrian crowd simulations. This suggests that there is opportunity to create more accurate models that run relatively quickly. Real time is a useful measure of model runtime. A simulation that runs in real time can be interactive and respond live to user input. By increasing the performance of the model, larger and more complex models can be simulated. This in turn increases the range of applications the model can represent. This thesis will develop a performant pedestrian simulation that runs in real time. It will explore how suitable the model is for GPU acceleration, and what performance gains can be obtained by implementing the model on the GPU
    corecore