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  • 1.
    Danielsson, Jakob
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tsog, Nandinbaatar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kunnappilly, Ashalatha
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A systematic mapping study on real-Time cloud services2019In: Proceedings - 11th IEEE/ACM International Conference on Utility and Cloud Computing Companion, UCC Companion 2018, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 245-251, article id 8605787Conference paper (Refereed)
    Abstract [en]

    Cloud computing is relatively a new technique to host and use the services and applications from the internet. Although it offers a multitude of advantages like scalability, low operating cost, accessibility and maintainability, etc., they are often not utilized to the fullest due to the lack of timeliness property associated with the cloud. Cloud services are mainly designed to maximize throughput and utilization of resources and hence incorporating predictable execution time properties in to the cloud is arduous. Nevertheless, cloud still remains a highly attractive platform for hosting real-Time applications and services owing to features like elasticity, multi-Tenancy, ability to survive hardware failures, virtualization support and abstraction layer support which provides flexibility and portability. In order for real-Time safety-critical applications to exploit the potential of cloud computing, it is essential to ensure the predictable real-Time behavior of cloud services. In this paper, we perform a systematic mapping study on real-Time cloud services to identify the current research directions and potential research gaps. Our study focuses on analyzing the current architectures and software techniques that are available at present to incorporate real-Time property of the cloud services. We also aim at investigating the current challenges involved in realizing a predictable real-Time behavior of cloud services. 

  • 2.
    Tsog, Nandinbaatar
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Improving On-Board Data Processing using CPU-GPU Heterogeneous Architectures for Real-Time Systems2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis investigates the efficacy of heterogeneous computing architectures in real-time systems.The goals of the thesis are twofold. First, to investigate various characteristics of the Heterogeneous System Architectures (HSA) compliant reference platforms focusing on computing performance and power consumption. The investigation is focused on the new technologies that could boost on-board data processing systems in satellites and spacecraft. Second, to enhance the usage of the heterogeneous processing units by introducing a technique for static allocation of parallel segments of tasks.

    The investigation and experimental evaluation show that our method of GPU allocation for the parallel segments of tasks is more energy efficient compared to any other studied allocation. The investigation is conducted under different types of environments, such as process-level isolated environment, different software stacks, including kernels, and various task set scenarios. The evaluation results indicate that a balanced use of heterogeneous processing units (CPU and GPU) could improve schedulability of task sets up to 90% with the proposed allocation technique.

  • 3.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Becker, Matthias
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Static Allocation of Parallel Tasks to Improve Schedulability in CPU-GPU Heterogeneous Real-Time Systems2019Conference paper (Refereed)
    Abstract [en]

    Autonomous driving is one of the main challenges of modern cars. Computer visions and intelligent on-board decision making are crucial in autonomous driving and require heterogeneous processors with high computing capability under low power consumption constraints. The progress of parallel computing using heterogeneous processing units is further supported by software frameworks like OpenCL, OpenMP, CUDA, and C++AMP. These frameworks allow the allocation of parallel computation on different compute resources. This, however, creates a difficulty in allocating the right computation segments to the right processing units in such a way that the complete system meets all its timing requirements. In this paper, we consider pre-runtime static allocations of parallel tasks to perform their execution either sequentially on CPU or in parallel using a GPU. This allows for improving any unbalanced use of GPU accelerators in a heterogeneous environment. By performing several heuristic algorithms, we show that the overuse of accelerators results in a bottle-neck of the entire system execution. The experimental results show that our allocation schemes that target a balanced use of GPU improve the system schedulability up to 90%.

  • 4.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture2018In: IEEE Aerospace Conference 2018 IEEEAC2018, 2018, p. 1-8Conference paper (Refereed)
    Abstract [en]

    In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library 'MIOpen' and an open standard 'OpenVX'. OpenVX is a cross-platform computer vision library.

  • 5.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Behnam, Moris
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Intelligent Data Processing using In-Orbit Advanced Algorithms on Heterogeneous System Architecture2018In: 2018 IEEE AEROSPACE CONFERENCE, IEEE , 2018Conference paper (Refereed)
    Abstract [en]

    In recent years, commercial exploitation of small satellites and CubeSats has rapidly increased. Time to market of processed customer data products is becoming an important differentiator between solution providers and satellite constellation operators. Timely and accurate data dissemination is the key to success in the commercial usage of small satellite constellations which is ultimately dependent on a high degree of autonomous fleet management and automated decision support. The traditional way for disseminating data is limited by on the communication capability of the satellite and the ground terminal availability. Even though cloud computing solutions on the ground offer high analytical performance, getting the data from the space infrastructure to the ground servers poses a bottleneck of data analysis and distribution. On the other hand, adopting advanced and intelligent algorithms onboard offers the ability of autonomy, tasking of operations, and fast customer generation of low latency conclusions, or even real-time communication with assets on the ground or other sensors in a multi-sensor configuration. In this paper, the advantages of intelligent onboard processing using advanced algorithms for Heterogeneous System Architecture (HSA) compliant onboard data processing systems are explored. The onboard data processing architecture is designed to handle a large amount of high-speed streaming data and provides hardware redundancy to be qualified for the space mission application domain. We conduct an experimental study to evaluate the performance analysis by using image recognition algorithms based on an open source intelligent machine library "MIOpen" and an open standard "OpenVX". OpenVX is a cross-platform computer vision library.

  • 6.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Nolin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Using Docker in Process Level Isolation for Heterogeneous Computing on GPU Accelerated On-Board Data Processing Systems2019Conference paper (Refereed)
    Abstract [en]

    The technological advancements make the intelligent on-board data processing possible on a small scale of satellites and deep-space exploration spacecraft such as CubeSats. However, the operation of satellites may fall into critical conditions when the on-board data processing interferes strongly to the basic operation functionalities of satellites. In order to avoid these issues, there exist techniques such as isolation, partitioning, and virtualization. In this paper, we present an experimental study of isolation of on-board payload data processing from the basic operations of satellites using Docker. Docker is a leading technology in process level isolation as well as continuous integration and continuous deployment (CI/CD) method. This study continues with the prior study on heterogeneous computing method, which improves the schedulability of the entire system up to 90%. Based on this heterogeneous computing method, the comparison study has been conducted between the non-isolated and isolated environments.

  • 7.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Unibap AB, Uppsala, Sweden.
    A Trade-Off between Computing Power and Energy Consumption of On-Board Data Processing in GPU Accelerated Real-Time Systems2019Conference paper (Refereed)
    Abstract [en]

    On-board data processing is one of the prior on-orbit activities that it improves the performance capability of in-orbit space systems such as deep-space exploration, earth and atmospheric observation satellites, and CubeSat constellations. However, on-board data processing encounters with higher energy consumption compared to traditional space systems. Because traditional space systems employ simple processing units such as micro-controllers or a single-core processor as the systems require no heavy data processing on orbit. Moreover, solving the radiation hardness problem is crucial in space and adopting a new processing unit is challenging.

    In this paper, we consider a GPU accelerated real-time system for on-board data processing. According to prior works, there exist radiation-tolerant GPU, and the computing capability of systems is improved by using heterogeneous computing method. We conduct experimental observations of power consumption and computing potential using this heterogeneous computing method in our GPU accelerated real-time system.The results show that the proper use of GPU increases computing potential with 10-140 times and consumes between 8-130 times less energy. Furthermore, the entire task system consumes 10-65% of less energy compared to the traditional use of processing units.

  • 8.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Advancing On-Board Big Data Processing Using Heterogeneous System Architecture2018In: ESA/CNES 4S Symposium 4S 2018, 2018Conference paper (Refereed)
  • 9.
    Tsog, Nandinbaatar
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sjödin, Mikael
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. ES (Embedded Systems).
    Bruhn, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Unibap AB, Sweden.
    Using Heterogeneous Computing on GPU Accelerated Systems to Advance On-Board Data Processing2019In: European Workshop on On-Board Data Processing 2019 OBDP2019, 2019Conference paper (Refereed)
1 - 9 of 9
CiteExportLink to result list
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  • modern-language-association-8th-edition
  • vancouver
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  • en-US
  • fi-FI
  • nn-NO
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  • Other locale
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