291 research outputs found

    A Generic Checkpoint-Restart Mechanism for Virtual Machines

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    It is common today to deploy complex software inside a virtual machine (VM). Snapshots provide rapid deployment, migration between hosts, dependability (fault tolerance), and security (insulating a guest VM from the host). Yet, for each virtual machine, the code for snapshots is laboriously developed on a per-VM basis. This work demonstrates a generic checkpoint-restart mechanism for virtual machines. The mechanism is based on a plugin on top of an unmodified user-space checkpoint-restart package, DMTCP. Checkpoint-restart is demonstrated for three virtual machines: Lguest, user-space QEMU, and KVM/QEMU. The plugins for Lguest and KVM/QEMU require just 200 lines of code. The Lguest kernel driver API is augmented by 40 lines of code. DMTCP checkpoints user-space QEMU without any new code. KVM/QEMU, user-space QEMU, and DMTCP need no modification. The design benefits from other DMTCP features and plugins. Experiments demonstrate checkpoint and restart in 0.2 seconds using forked checkpointing, mmap-based fast-restart, and incremental Btrfs-based snapshots

    ๋ฐ์ดํ„ฐ ์ง‘์•ฝ์  ์‘์šฉ์˜ ํšจ์œจ์ ์ธ ์‹œ์Šคํ…œ ์ž์› ํ™œ์šฉ์„ ์œ„ํ•œ ๋ฉ”๋ชจ๋ฆฌ ์„œ๋ธŒ์‹œ์Šคํ…œ ์ตœ์ ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ์—ผํ—Œ์˜.With explosive data growth, data-intensive applications, such as relational database and key-value storage, have been increasingly popular in a variety of domains in recent years. To meet the growing performance demands of data-intensive applications, it is crucial to efficiently and fully utilize memory resources for the best possible performance. However, general-purpose operating systems (OSs) are designed to provide system resources to applications running on a system in a fair manner at system-level. A single application may find it difficult to fully exploit the systems best performance due to this system-level fairness. For performance reasons, many data-intensive applications implement their own mechanisms that OSs already provide, under the assumption that they know better about the data than OSs. They can be greedily optimized for performance but this may result in inefficient use of system resources. In this dissertation, we claim that simple OS support with minor application modifications can yield even higher application performance without sacrificing system-level resource utilization. We optimize and extend OS memory subsystem for better supporting applications while addressing three memory-related issues in data-intensive applications. First, we introduce a memory-efficient cooperative caching approach between application and kernel buffer to address double caching problem where the same data resides in multiple layers. Second, we present a memory-efficient, transparent zero-copy read I/O scheme to avoid the performance interference problem caused by memory copy behavior during I/O. Third, we propose a memory-efficient fork-based checkpointing mechanism for in-memory database systems to mitigate the memory footprint problem of the existing fork-based checkpointing scheme; memory usage increases incrementally (up to 2x) during checkpointing for update-intensive workloads. To show the effectiveness of our approach, we implement and evaluate our schemes on real multi-core systems. The experimental results demonstrate that our cooperative approach can more effectively address the above issues related to data-intensive applications than existing non-cooperative approaches while delivering better performance (in terms of transaction processing speed, I/O throughput, or memory footprint).์ตœ๊ทผ ํญ๋ฐœ์ ์ธ ๋ฐ์ดํ„ฐ ์„ฑ์žฅ๊ณผ ๋”๋ถˆ์–ด ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค, ํ‚ค-๋ฐธ๋ฅ˜ ์Šคํ† ๋ฆฌ์ง€ ๋“ฑ์˜ ๋ฐ์ดํ„ฐ ์ง‘์•ฝ์ ์ธ ์‘์šฉ๋“ค์ด ๋‹ค์–‘ํ•œ ๋„๋ฉ”์ธ์—์„œ ์ธ๊ธฐ๋ฅผ ์–ป๊ณ  ์žˆ๋‹ค. ๋ฐ์ดํ„ฐ ์ง‘์•ฝ์ ์ธ ์‘์šฉ์˜ ๋†’์€ ์„ฑ๋Šฅ ์š”๊ตฌ๋ฅผ ์ถฉ์กฑํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ฃผ์–ด์ง„ ๋ฉ”๋ชจ๋ฆฌ ์ž์›์„ ํšจ์œจ์ ์ด๊ณ  ์™„๋ฒฝํ•˜๊ฒŒ ํ™œ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ๋ฒ”์šฉ ์šด์˜์ฒด์ œ(OS)๋Š” ์‹œ์Šคํ…œ์—์„œ ์ˆ˜ํ–‰ ์ค‘์ธ ๋ชจ๋“  ์‘์šฉ๋“ค์— ๋Œ€ํ•ด ์‹œ์Šคํ…œ ์ฐจ์›์—์„œ ๊ณตํ‰ํ•˜๊ฒŒ ์ž์›์„ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์„ ์šฐ์„ ํ•˜๋„๋ก ์„ค๊ณ„๋˜์–ด์žˆ๋‹ค. ์ฆ‰, ์‹œ์Šคํ…œ ์ฐจ์›์˜ ๊ณตํ‰์„ฑ ์œ ์ง€๋ฅผ ์œ„ํ•œ ์šด์˜์ฒด์ œ ์ง€์›์˜ ํ•œ๊ณ„๋กœ ์ธํ•ด ๋‹จ์ผ ์‘์šฉ์€ ์‹œ์Šคํ…œ์˜ ์ตœ๊ณ  ์„ฑ๋Šฅ์„ ์™„์ „ํžˆ ํ™œ์šฉํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ์ด๋Ÿฌํ•œ ์ด์œ ๋กœ, ๋งŽ์€ ๋ฐ์ดํ„ฐ ์ง‘์•ฝ์  ์‘์šฉ์€ ์šด์˜์ฒด์ œ์—์„œ ์ œ๊ณตํ•˜๋Š” ๊ธฐ๋Šฅ์— ์˜์ง€ํ•˜์ง€ ์•Š๊ณ  ๋น„์Šทํ•œ ๊ธฐ๋Šฅ์„ ์‘์šฉ ๋ ˆ๋ฒจ์— ๊ตฌํ˜„ํ•˜๊ณค ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ๋ฒ•์€ ํƒ์š•์ ์ธ ์ตœ์ ํ™”๊ฐ€ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์—์„œ ์„ฑ๋Šฅ ์ƒ ์ด๋“์ด ์žˆ์„ ์ˆ˜ ์žˆ์ง€๋งŒ, ์‹œ์Šคํ…œ ์ž์›์˜ ๋น„ํšจ์œจ์ ์ธ ์‚ฌ์šฉ์„ ์ดˆ๋ž˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์šด์˜์ฒด์ œ์˜ ์ง€์›๊ณผ ์•ฝ๊ฐ„์˜ ์‘์šฉ ์ˆ˜์ •๋งŒ์œผ๋กœ๋„ ๋น„ํšจ์œจ์ ์ธ ์‹œ์Šคํ…œ ์ž์› ์‚ฌ์šฉ ์—†์ด ๋ณด๋‹ค ๋†’์€ ์‘์šฉ ์„ฑ๋Šฅ์„ ๋ณด์ผ ์ˆ˜ ์žˆ์Œ์„ ์ฆ๋ช…ํ•˜๊ณ ์ž ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๊ธฐ ์œ„ํ•ด ์šด์˜์ฒด์ œ์˜ ๋ฉ”๋ชจ๋ฆฌ ์„œ๋ธŒ์‹œ์Šคํ…œ์„ ์ตœ์ ํ™” ๋ฐ ํ™•์žฅํ•˜์—ฌ ๋ฐ์ดํ„ฐ ์ง‘์•ฝ์ ์ธ ์‘์šฉ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์„ธ ๊ฐ€์ง€ ๋ฉ”๋ชจ๋ฆฌ ๊ด€๋ จ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ์ฒซ์งธ, ๋™์ผํ•œ ๋ฐ์ดํ„ฐ๊ฐ€ ์—ฌ๋Ÿฌ ๊ณ„์ธต์— ์กด์žฌํ•˜๋Š” ์ค‘๋ณต ์บ์‹ฑ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‘์šฉ๊ณผ ์ปค๋„ ๋ฒ„ํผ ๊ฐ„์— ๋ฉ”๋ชจ๋ฆฌ ํšจ์œจ์ ์ธ ํ˜‘๋ ฅ ์บ์‹ฑ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๋‘˜์งธ, ์ž…์ถœ๋ ฅ์‹œ ๋ฐœ์ƒํ•˜๋Š” ๋ฉ”๋ชจ๋ฆฌ ๋ณต์‚ฌ๋กœ ์ธํ•œ ์„ฑ๋Šฅ ๊ฐ„์„ญ ๋ฌธ์ œ๋ฅผ ํ”ผํ•˜๊ธฐ ์œ„ํ•ด ๋ฉ”๋ชจ๋ฆฌ ํšจ์œจ์ ์ธ ๋ฌด๋ณต์‚ฌ ์ฝ๊ธฐ ์ž…์ถœ๋ ฅ ๋ฐฉ์‹์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์…‹์งธ, ์ธ-๋ฉ”๋ชจ๋ฆฌ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ๋ฉ”๋ชจ๋ฆฌ ํšจ์œจ์ ์ธ fork ๊ธฐ๋ฐ˜ ์ฒดํฌํฌ์ธํŠธ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์—ฌ ๊ธฐ์กด ํฌํฌ ๊ธฐ๋ฐ˜ ์ฒดํฌํฌ์ธํŠธ ๊ธฐ๋ฒ•์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ ์ฆ๊ฐ€ ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜์˜€๋‹ค; ๊ธฐ์กด ๋ฐฉ์‹์€ ์—…๋ฐ์ดํŠธ ์ง‘์•ฝ์  ์›Œํฌ๋กœ๋“œ์— ๋Œ€ํ•ด ์ฒดํฌํฌ์ธํŒ…์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๋™์•ˆ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์ด ์ตœ๋Œ€ 2๋ฐฐ๊นŒ์ง€ ์ ์ง„์ ์œผ๋กœ ์ฆ๊ฐ€ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•๋“ค์˜ ํšจ๊ณผ๋ฅผ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์‹ค์ œ ๋ฉ€ํ‹ฐ ์ฝ”์–ด ์‹œ์Šคํ…œ์— ๊ตฌํ˜„ํ•˜๊ณ  ๊ทธ ์„ฑ๋Šฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์‹คํ—˜๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ์ œ์•ˆํ•œ ํ˜‘๋ ฅ์  ์ ‘๊ทผ๋ฐฉ์‹์ด ๊ธฐ์กด์˜ ๋น„ํ˜‘๋ ฅ์  ์ ‘๊ทผ๋ฐฉ์‹๋ณด๋‹ค ๋ฐ์ดํ„ฐ ์ง‘์•ฝ์  ์‘์šฉ์—๊ฒŒ ํšจ์œจ์ ์ธ ๋ฉ”๋ชจ๋ฆฌ ์ž์› ํ™œ์šฉ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ์œผ๋กœ์จ ๋” ๋†’์€ ์„ฑ๋Šฅ์„ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Chapter 1 Introduction 1 1.1 Motivation 1 1.1.1 Importance of Memory Resources 1 1.1.2 Problems 2 1.2 Contributions 5 1.3 Outline 6 Chapter 2 Background 7 2.1 Linux Kernel Memory Management 7 2.1.1 Page Cache 7 2.1.2 Page Reclamation 8 2.1.3 Page Table and TLB Shootdown 9 2.1.4 Copy-on-Write 10 2.2 Linux Support for Applications 11 2.2.1 fork 11 2.2.2 madvise 11 2.2.3 Direct I/O 12 2.2.4 mmap 13 Chapter 3 Memory Efficient Cooperative Caching 14 3.1 Motivation 14 3.1.1 Problems of Existing Datastore Architecture 14 3.1.2 Proposed Architecture 17 3.2 Related Work 17 3.3 Design and Implementation 19 3.3.1 Overview 19 3.3.2 Kernel Support 24 3.3.3 Migration to DBIO 25 3.4 Evaluation 27 3.4.1 System Configuration 27 3.4.2 Methodology 28 3.4.3 TPC-C Benchmarks 30 3.4.4 YCSB Benchmarks 32 3.5 Summary 37 Chapter 4 Memory Efficient Zero-copy I/O 38 4.1 Motivation 38 4.1.1 The Problems of Copy-Based I/O 38 4.2 Related Work 40 4.2.1 Zero Copy I/O 40 4.2.2 TLB Shootdown 42 4.2.3 Copy-on-Write 43 4.3 Design and Implementation 44 4.3.1 Prerequisites for z-READ 44 4.3.2 Overview of z-READ 45 4.3.3 TLB Shootdown Optimization 48 4.3.4 Copy-on-Write Optimization 52 4.3.5 Implementation 55 4.4 Evaluation 55 4.4.1 System Configurations 56 4.4.2 Effectiveness of the TLB Shootdown Optimization 57 4.4.3 Effectiveness of CoW Optimization 59 4.4.4 Analysis of the Performance Improvement 62 4.4.5 Performance Interference Intensity 63 4.4.6 Effectiveness of z-READ in Macrobenchmarks 65 4.5 Summary 67 Chapter 5 Memory Efficient Fork-based Checkpointing 69 5.1 Motivation 69 5.1.1 Fork-based Checkpointing 69 5.1.2 Approach 71 5.2 Related Work 73 5.3 Design and Implementation 74 5.3.1 Overview 74 5.3.2 OS Support 78 5.3.3 Implementation 79 5.4 Evaluation 80 5.4.1 Experimental Setup 80 5.4.2 Performance 81 5.5 Summary 86 Chapter 6 Conclusion 87 ์š”์•ฝ 100Docto

    CROCHET: Checkpoint and Rollback via Lightweight Heap Traversal on Stock JVMs

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    Checkpoint/rollback (CR) mechanisms create snapshots of the state of a running application, allowing it to later be restored to that checkpointed snapshot. Support for checkpoint/rollback enables many program analyses and software engineering techniques, including test generation, fault tolerance, and speculative execution. Fully automatic CR support is built into some modern operating systems. However, such systems perform checkpoints at the coarse granularity of whole pages of virtual memory, which imposes relatively high overhead to incrementally capture the changing state of a process, and makes it difficult for applications to checkpoint only some logical portions of their state. CR systems implemented at the application level and with a finer granularity typically require complex developer support to identify: (1) where checkpoints can take place, and (2) which program state needs to be copied. A popular compromise is to implement CR support in managed runtime environments, e.g. the Java Virtual Machine (JVM), but this typically requires specialized, non-standard runtime environments, limiting portability and adoption of this approach. In this paper, we present a novel approach for Checkpoint ROllbaCk via lightweight HEap Traversal (Crochet), which enables fully automatic fine-grained lightweight checkpoints within unmodified commodity JVMs (specifically Oracle\u27s HotSpot and OpenJDK). Leveraging key insights about the internal design common to modern JVMs, Crochet works entirely through bytecode rewriting and standard debug APIs, utilizing special proxy objects to perform a lazy heap traversal that starts at the root references and traverses the heap as objects are accessed, copying or restoring state as needed and removing each proxy immediately after it is used. We evaluated Crochet on the DaCapo benchmark suite, finding it to have very low runtime overhead in steady state (ranging from no overhead to 1.29x slowdown), and that it often outperforms a state-of-the-art system-level checkpoint tool when creating large checkpoints

    Self-Healing Multitier Architectures Using Cascading Rescue Points

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    Software bugs and vulnerabilities cause serious problems to both home users and the Internet infrastructure, limiting the availability of Internet services, causing loss of data, and reducing system integrity. Software self-healing using rescue points (RPs) is a known mechanism for recovering from unforeseen errors. However, applying it on multitier architectures can be problematic because certain actions, like transmitting data over the network, cannot be undone. We propose cascading rescue points (CRPs) to address the state inconsistency issues that can arise when using traditional RPs to recover from errors in interconnected applications. With CRPs, when an application executing within a RP transmits data, the remote peer is notified to also perform a checkpoint, so the communicating entities checkpoint in a coordinated, but loosely coupled way. Notifications are also sent when RPs successfully complete execution, and when recovery is initiated, so that the appropriate action is performed by remote parties. We developed a tool that implements CRPs by dynamically instrumenting binaries and transparently injecting notifications in the already established TCP channels between applications. We tested our tool with various applications, including the MySQL and Apache servers, and show that it allows them to successfully recover from errors, while incurring moderate overhead between 4.54% and 71.56%

    Building Computing-As-A-Service Mobile Cloud System

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    The last five years have witnessed the proliferation of smart mobile devices, the explosion of various mobile applications and the rapid adoption of cloud computing in business, governmental and educational IT deployment. There is also a growing trends of combining mobile computing and cloud computing as a new popular computing paradigm nowadays. This thesis envisions the future of mobile computing which is primarily affected by following three trends: First, servers in cloud equipped with high speed multi-core technology have been the main stream today. Meanwhile, ARM processor powered servers is growingly became popular recently and the virtualization on ARM systems is also gaining wide ranges of attentions recently. Second, high-speed internet has been pervasive and highly available. Mobile devices are able to connect to cloud anytime and anywhere. Third, cloud computing is reshaping the way of using computing resources. The classic pay/scale-as-you-go model allows hardware resources to be optimally allocated and well-managed. These three trends lend credence to a new mobile computing model with the combination of resource-rich cloud and less powerful mobile devices. In this model, mobile devices run the core virtualization hypervisor with virtualized phone instances, allowing for pervasive access to more powerful, highly-available virtual phone clones in the cloud. The centralized cloud, powered by rich computing and memory recourses, hosts virtual phone clones and repeatedly synchronize the data changes with virtual phone instances running on mobile devices. Users can flexibly isolate different computing environments. In this dissertation, we explored the opportunity of leveraging cloud resources for mobile computing for the purpose of energy saving, performance augmentation as well as secure computing enviroment isolation. We proposed a framework that allows mo- bile users to seamlessly leverage cloud to augment the computing capability of mobile devices and also makes it simpler for application developers to run their smartphone applications in the cloud without tedious application partitioning. This framework was built with virtualization on both server side and mobile devices. It has three building blocks including agile virtual machine deployment, efficient virtual resource management, and seamless mobile augmentation. We presented the design, imple- mentation and evaluation of these three components and demonstrated the feasibility of the proposed mobile cloud model

    Standart-konformes Snapshotting fรผr SystemC Virtuelle Plattformen

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    The steady increase in complexity of high-end embedded systems goes along with an increasingly complex design process. We are currently still in a transition phase from Hardware-Description Language (HDL) based design towards virtual-platform-based design of embedded systems. As design complexity rises faster than developer productivity a gap forms. Restoring productivity while at the same time managing increased design complexity can also be achieved through focussing on the development of new tools and design methodologies. In most application areas, high-level modelling languages such as SystemC are used in early design phases. In modern software development Continuous Integration (CI) is used to automatically test if a submitted piece of code breaks functionality. Application of the CI concept to embedded system design and testing requires fast build and test execution times from the virtual platform framework. For this use case the ability to save a specific state of a virtual platform becomes necessary. The saving and restoring of specific states of a simulation requires the ability to serialize all data structures within the simulation models. Improving the frameworks and establishing better methods will only help to narrow the design gap, if these changes are introduced with the needs of the engineers and developers in mind. Ultimately, it is their productivity that shall be improved. The ability to save the state of a virtual platform enables developers to run longer test campaigns that can even contain randomized test stimuli. If the saved states are modifiable the developers can inject faulty states into the simulation models. This work contributes an extension to the SoCRocket virtual platform framework to enable snapshotting. The snapshotting extension can be considered a reference implementation as the utilization of current SystemC/TLM standards makes it compatible to other frameworkds. Furthermore, integrating the UVM SystemC library into the framework enables test driven development and fast validation of SystemC/TLM models using snapshots. These extensions narrow the design gap by supporting designers, testers and developers to work more efficiently.Die stetige Steigerung der Komplexitรคt eingebetteter Systeme geht einher mit einer ebenso steigenden Komplexitรคt des Entwurfsprozesses. Wir befinden uns momentan in der รœbergangsphase vom Entwurf von eingebetteten Systemen basierend auf Hardware-Beschreibungssprachen hin zum Entwurf ebendieser basierend auf virtuellen Plattformen. Da die Entwurfskomplexitรคt rasanter steigt als die Produktivitรคt der Entwickler, entsteht eine Kluft. Die Produktivitรคt wiederherzustellen und gleichzeitig die gesteigerte Entwurfskomplexitรคt zu bewรคltigen, kann auch erreicht werden, indem der Fokus auf die Entwicklung neuer Werkzeuge und Entwurfsmethoden gelegt wird. In den meisten Anwendungsgebieten werden Modellierungssprachen auf hoher Ebene, wie zum Beispiel SystemC, in den frรผhen Entwurfsphasen benutzt. In der modernen Software-Entwicklung wird Continuous Integration (CI) benutzt um automatisiert zu รผberprรผfen, ob eine eingespielte ร„nderung am Quelltext bestehende Funktionalitรคten beeintrรคchtigt. Die Anwendung des CI-Konzepts auf den Entwurf und das Testen von eingebetteten Systemen fordert schnelle Bau- und Test-Ausfรผhrungszeiten von dem genutzten Framework fรผr virtuelle Plattformen. Fรผr diesen Anwendungsfall wird auch die Fรคhigkeit, einen bestimmten Zustand der virtuellen Plattform zu speichern, erforderlich. Das Speichern und Wiederherstellen der Zustรคnde einer Simulation erfordert die Serialisierung aller Datenstrukturen, die sich in den Simulationsmodellen befinden. Das Verbessern von Frameworks und Etablieren besserer Methodiken hilft nur die Entwurfs-Kluft zu verringern, wenn diese ร„nderungen mit Berรผcksichtigung der Bedรผrfnisse der Entwickler und Ingenieure eingefรผhrt werden. Letztendlich ist es ihre Produktivitรคt, die gesteigert werden soll. Die Fรคhigkeit den Zustand einer virtuellen Plattform zu speichern, ermรถglicht es den Entwicklern, lรคngere Testkampagnen laufen zu lassen, die auch zufรคllig erzeugte Teststimuli beinhalten kรถnnen oder, falls die gespeicherten Zustรคnde modifizierbar sind, fehlerbehaftete Zustรคnde in die Simulationsmodelle zu injizieren. Mein mit dieser Arbeit geleisteter Beitrag beinhaltet die Erweiterung des SoCRocket Frameworks um Checkpointing Funktionalitรคt im Sinne einer Referenzimplementierung. Weiterhin ermรถglicht die Integration der UVM SystemC Bibliothek in das Framework die Umsetzung der testgetriebenen Entwicklung und schnelle Validierung von SystemC/TLM Modellen mit Hilfe von Snapshots
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