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AIAA SciTech Forum

作者： Stepanyan, Vahram Ippolito, Corey Kawamura, Evan T. Lombaerts, Thomas NASA Ames Res Ctr KBR Wyle Serv Intelligent Syst Div Mail Stop 269-1 Moffett Field CA 94035 USA NASA Ames Res Ctr Intelligent Syst Div Mail Stop 269-1 Moffett Field CA 94035 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The paper presents an airborne target tracking approach with data fusion from distributed stationary and mobile sensor network transmitted through a wireless/wired communication network and using a distributed computing infrastructure. To obtain high quality measurements, sensor flight platforms' positions and orientations are optimized with respect to the available target's position and velocity estimates, and a formation control strategy is applied to derive desired trajectories for the flight platforms. As sensors move, the communication network topology switching is determined using the worst-case approach to handle uncertainties in the target's and sensors' positions, which alters communication delays for sensors' data transmission to computing centers. An optimal data migration algorithm is periodically applied to determine these communication delays for each sensor and the optimal location with minimum end-to-end latency for the target tracking algorithm execution, which is based on adaptive information fusion from multi-modal mobile and stationary sensors inside an Extended Kalman Filter framework. The approach is validated in a desktop simulation environment using synthetic sensor data generated for a simulated target's flight.

关键词： Cloud computing distributed Sensing Extended Kalman Filter Radar Measurement Data Transmission distributed computing Data Fusion Data Acquisition computing Hardware Airspace

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Optimization of the Functional Decomposition of Parallel and distributed Computations in Graph Coloring With the Use of High-Performance computing

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IEEE ACCESS 2022年 10卷 34996-35011页

作者： Skrinarova, Jarmila Dudas, Adam Matej Bel Univ Fac Nat Sci Dept Comp Sci Banska Bystrica 97401 Slovakia

This article presents methods for correct decomposition for high performance computations related to large sets of graphs. These computations contain large number of calls of sequential, recursive algorithm for NP-complete problem - proper edge coloring of graph. Decomposition of this computational problem is not trivial, since the number of recursions in various parts of the computation is different and causes a high load and time imbalance. We designed, implemented and experimentally verified a new decomposition method that significantly reduces the computational time for a large set of graphs (up to 404 million graphs). This method ensures the same duration of computational time for partial subtasks and thus eliminates the need to wait for synchronization of parallel computations.

关键词： Task analysis Parallel processing Image color analysis Resource management Program processors NP-complete problem Load management Decomposition distributed computing graph coloring parallel computing snark

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Multi-Layer Computation Offloading in distributed Heterogeneous Mobile Edge computing Networks

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2022年第2期8卷 1301-1315页

作者： Wang, Pengfei Di, Boya Song, Lingyang Jennings, Nicholas R. Peking Univ Dept Elect Natl Engn Lab Big Data Anal & Applicat Beijing 100871 Peoples R China Loughborough Univ Dept Comp Sci Loughborough LE11 3TU Leics England

In this paper, we consider distributed heterogeneous multi-layer mobile edge computing (HetMEC) networks, where resource-poor edge devices (EDs) upload computing tasks for processing to the mobile edge computing (MEC) servers and a cloud center (CC). To reduce total energy consumption, computation offloading and resource allocation are independently performed by each device and each server. However, due to the partial information available at each device and server, the offloading strategies may overwhelm the layers above. This may lead to network congestion, i.e., so many tasks are offloaded to the same node that this node is overloaded. To address this problem, we develop a smart pricing mechanism to coordinate the computation offloading of multi-layer devices, where the CC charges the MEC servers and EDs for computing services and network congestion. In particular, to satisfy the latency constraints of each task, we construct a Lagrangian framework where multi-agent reinforcement learning is utilized by each MEC server to determine its offloading strategies and resource allocation, so that the total energy consumption is reduced. Simulation results show that our algorithm achieves an energy consumption reduction of 28% and a decrease in congestion probability of between 28% and 100% compared to the state of the art.

关键词： Minimum energy control distributed computing resource management

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distributed computing approach to solve unbalanced three-phase DOPFs

Distributed computing approach to solve unbalanced three-pha...

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IEEE Canada Electric Power Conference, EPEC

作者： Abolfazl Mosaddegh Claudio A. Cañizares Kankar Bhattacharya Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

Distribution systems have been gradually improved with new technologies. They have been upgraded from the traditional system with low-level control to a smart-grid system with high-level control. In the present work, a mathematical model of an unbalanced three-phase distribution system, including ZIP loads and other components of distribution systems is used, and a Genetic Algorithm (GA) -based Distribution Optimal Power Flow (DOPF) model is applied to find the optimal integer solutions for discrete system control elements such as Load Tap Changers (LTCs) and Switched Capacitors (SCs) in a practical feeder. In order to reduce the computational burden and consequently the run-time, a communication Middleware System for smart grids is used to solve the GA-based DOPF problem on a decentralized computer system using a parallel computing approach. This system is responsible for running the model, managing all communication between the nodes, and transferring the results between various parts of the parallel system. Comparing with heuristic methods with faster sub-optimal solutions in a centralized computer system, the present work is expected to yield better optimal solution within acceptable practical run-times.

关键词： Computational modeling Load modeling Capacitors Mathematical model Switches Real-time systems distributed computing

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Advances in distributed Load Orchestration for Vision computing in 5G-MEC Environments 37

Advances in Distributed Load Orchestration for Vision Comput...

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37th International Conference on Information Networking (ICOIN)

作者： Boing, Ricardo do Nascimento Sampaio, Hugo Vaz Koch, Fernando Bezerra, Wesley dos Reis Santa Cruz, Rene Nolio Westphall, Carlos Becker Univ Fed Santa Catarina Dept Informat & Stat Florianopolis SC Brazil IBM Corp Austin TX USA

ISBN: (纸本)9781665462686

Multi-access Edge computing (MEC) is a type of network architecture that provides cloud computing capabilities at the edge of the network. We consider the use case of video surveillance for an university campus running on a 5-GMEC environment. A key issue is the eventual overloading of computing resources on the MEC nodes during peak demand. We propose a new strategy for distributed orchestration in MEC environments based on how load balancing strategies organize processing queue. Then, we elaborated a strategy for deadline-aware queueing prioritization that organizes requests based on pre-established thresholds. We introduce a simulation-based experimentation environment and conduct a number of tests demonstrating the benefit of our approach by reducing the number of referrals and improving the effectiveness in meeting deadlines.

关键词： 5G-MEC Multi-access Edge computing Load Orchestration distributed computing Video Surveillance

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distributed computing system for solving applied tasks

Distributed computing system for solving applied tasks

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Polina V. Mishchenko Novosibirsk State Technical University Novosibirsk Russia

High performance computing is now one of the most strategic felds of research for many countries. However the developments in this feld in Russia face a number of problems. The high cost of high-performance equipment is among them. Many universities and enterprises can't purchase and maintain expensive computer systems. One way of overcoming this problem is to develop own high performance computing systems. The paper describes proposed approach of creating high-performance computing facilities. It is based on a combination of personal computers in the distributed computing system with a programmable structure. Network software system was developed to facilitate creation of system (or required number of subsystems) and realization date exchanges between personal computers during solution of task. At present the group of researchers of Novosibirsk State Technical University has developed a fragment of the experimental distributed computing system. The system prototype is used for further researches. Also it is planned to use the system in the educational purposes.

关键词： Computers Parallel algorithms Organizations Random access memory distributed computing Mathematical model

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3PEIC: computing Power Evaluation Framework for distributed Heterogeneous Intelligent computing Systems 9

3PEIC: Computing Power Evaluation Framework for Distributed ...

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9th IEEE Smart World Congress, SWC 2023

作者： Han, Chengxu Li, Zhuo Li, Zecheng Beijing Information Science and Technology University Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing China Beijing Information Science and Technology University School of Computer Science Beijing China

ISBN: (纸本)9798350319804

computing Power Network (CPN) plays an important role as an infrastructure in various application fields. The evaluation of computing power in distributed heterogeneous intelligent computing systems serves as the basis of CPN and is especially crucial in the on-demand allocation of computing power resources and tasks scheduling. In this paper, we propose a computing power evaluation metric and computing Power Evaluation framework for distributed heterogeneous Intelligent computing systems (PEIC). The computing power evaluation metric integrates software and hardware resource information of the devices into five primary metrics, as data processing capacity, data storage capacity, data transfer capacity, intelligent computing service capacity, and device computing resource utilization capacity. In the PEIC framework, we combine static resource information with dynamic intelligent computing service capacities. Entropy Weight Method (EWM) is used to solve the problem of collaborative influence between devices, and Analytic Hierarchy Process (AHP) method is used to give a comprehensive computation power evaluation for distributed heterogeneous intelligent computing systems. We also implemented the PEIC framework in a real environment and run five typical application scenarios such as image classification and natural language processing, and comprehensively evaluated the computation power of the system. © 2023 IEEE.

关键词： Benchmark Deep Learning distributed computing

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Split distributed computing in Wireless Sensor Networks

Split Distributed Computing in Wireless Sensor Networks

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作者： Kenyeres, M. Brno University of Technology

We designed a novel method intended to improve the performance of distributed computing in wireless sensor networks. Our proposed method is designed to rapidly increase the speed of distributed computing and decrease the number of the messages required for a network to achieve the desired result. In our analysis, we chose Average consensus algorithm. In this case, the desired result is that every node achieves the average value calculated from all the initial values in the reduced number of iterations. Our method is based on the idea that a fragmentation of a network into small geographical structures which execute distributed calculations in parallel significantly affects the performance.

关键词： Wireless sensor networks distributed computing improvement of distributed computing

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distributed computing and data storage in proteomics: Many hands make light work, and a stronger memory

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PROTEOMICS 2014年第4-5期14卷 367-377页

作者： Verheggen, Kenneth Barsnes, Harald Martens, Lennart VIB Dept Med Prot Res Ghent Belgium Univ Ghent Dept Biochem Fac Med & Hlth Sci B-9000 Ghent Belgium Univ Bergen Dept Biomed Prote Unit N-5020 Bergen Norway

Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, distributed architectures have gained substantial popularity in the recent years. In this review, we provide an overview of the current techniques for distributed computing, along with examples of how the techniques are currently being employed in the field of proteomics. We thus underline the benefits of distributed computing in proteomics, while also pointing out the potential issues and pitfalls involved.

关键词： Bioinformatics Cloud computing Crowdsourcing distributed computing Parallelized computing

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A novel hierarchical distributed vehicular edge computing framework for supporting intelligent driving

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AD HOC NETWORKS 2024年 153卷

作者： Yang, Kun Sun, Peng Yang, Dingkang Lin, Jieyu Boukerche, Azzedine Song, Liang Fudan Univ Shanghai Peoples R China Duke Kunshan Univ Suzhou Peoples R China Univ Toronto Toronto ON Canada Univ Ottawa Ottawa ON Canada Gulf Univ Sci & Technol Ctr Appl Math & Bioinformat CAMB Hawally Kuwait

Recently, various infrastructure-assisted or onboard driving assistant applications have been proposed as a component of intelligent transportation systems (ITS) to improve the transportation system's efficiency and release public concern about road safety. However, such AI-assisted intelligent applications are mainly data-driven and put great demands on the computing power of the ITS systems. Therefore, in the highly dynamic Internet-of-Vehicles environment in ITS, how to effectively coordinate the limited computing power of the various components of the system and realize reliable support for such resource-consuming applications through efficient resource allocation methods is the focus of our research. Accordingly, a novel joint computing and communication resource scheduling method is proposed to fulfill those ITS applications' inherent heterogeneous quality of service (QoS) requirements. By fully exploiting the computing resources provided by the onboard computing device, the edge computing device located in the vehicle's proximity and remote data center, we designed a hierarchical three-layer Vehicular Edge computing (VEC) framework. Briefly, an onboard joint computation offloading and transmission scheduling policy is designed to assign corresponding offloading decisions to the locally generated computing tasks by considering the vehicle's computing resources and real-time network link status. Additionally, a new distributed resource allocation policy is developed for the edge devices, in which we derive a server selection policy and allocate communication time based on our proposed metric. To evaluate the performance and validate the effectiveness of our proposed method, we conduct extensive simulation tests and ablation experiments, respectively. The results show that our approach can achieve stable performance in various experimental settings. Also, compared to the state-of-the-art algorithms, our joint resource allocation policy significantly reduces the

关键词： Vehicular edge computing distributed computing Scheduling computing task offloading Mobility-aware

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