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检索条件"任意字段=23rd International Conference on Distributed Computing Systems Workshops"

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distributed Processing Applications for UAV/drones: A Survey

Distributed Processing Applications for UAV/drones: A Survey

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23rd international conference on systems Engineering

作者： Chmaj, Grzegorz Selvaraj, Henry Univ Nevada Las Vegas NV 89154 USA Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA

ISBN: (纸本)9783319084213

distributed Processing systems are the ones that include multiple devices (which could be of many types, such as PC computers, mobile devices etc.) that have computational and communication capabilities. Their computational power is jointly used for collaborative processing of variety of tasks - and this processing is realized in distributed manner. UAV - Unmanned Aerial Vehicles (also called drones) gain significant attention over recent years. They have been employed to realize multiple tasks such as surveillance or environmental monitoring. First implementations were based on single UAV, later the potential of multiple UAVs collaborating in a team was noticed. Many applications were implemented in distributed manner, using multiple collaborative UAVs and the distributed processing systems principles. In this paper we survey the applications implemented over cooperative teams of UAVs that operate as distributed processing systems. © Springer international Publishing Switzerland 2015.

关键词： applications collaborative cooperative distributed processing drones swarms UAV Unmanned Aerial Vehicle

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TFluxSCC: Exploiting Performance on Future Many-core systems through Data-Flow 23

TFluxSCC: Exploiting Performance on Future Many-core Systems...

引用

23rd Euromicro international conference on Parallel, distributed, and Network-Based Processing (PDP)

作者： Diavastos, Andreas Stylianou, Giannos Trancoso, Pedro Univ Cyprus Dept Comp Sci Nicosia Cyprus

ISBN: (纸本)9781479984909

The current trend in processor design is to increase the number of cores as to achieve a desired performance. While having a large number of cores on a chip seems to be feasible in terms of the hardware, the development of the software that is able to exploit that parallelism is one of the biggest challenges. In this paper we propose a Data-Flow based system that can be used to exploit the parallelism in large-scale many-core processors in an effective and efficient way. Our proposed system - TFluxSCC - is an extension of the TFlux Data-Driven Multithreading (DDM), which evolved to exploit the parallelism of the 48-core Intel Single-chip Cloud computing (SCC) processor. With TFluxSCC we achieve scalable performance using a global address space without the need of cache-coherency support. Our scalability study shows that application's performance can scale, with speedup results reaching up to 48x for 48 cores. The findings of this work provide insight towards what a Data-Flow implementation requires and what not from a many-core architecture in order to scale the performance.

关键词： dataflow many-cores programming model Data-Driven Multithreading

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A Monitoring System for Runtime Adaptations of Streaming Applications 23

A Monitoring System for Runtime Adaptations of Streaming App...

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23rd Euromicro international conference on Parallel, distributed, and Network-Based Processing (PDP)

作者： Selva, Manuel Morel, Lionel Marquet, Kevin Frenot, Stephane Bull Echirolles 1 Rue Prov F-38342 Echirolles France Univ Lyon Inria INSA Lyon CITI F-69621 Villeurbanne France

ISBN: (纸本)9781479984909

Streaming languages are adequate for expressing many applications quite naturally and have been proven to be a good approach for taking advantage of the intrinsic parallelism of modern CPU architectures. While numerous works focus on improving the throughput of streaming programs, we rather focus on satisfying quality-of-service requirements of streaming applications executed along-side non-streaming processes. We monitor synchronous dataflow (SDF) programs at runtime both at the application and system levels in order to identify violations of quality-of-service requirements. Our monitoring requires the programmer to provide the expected throughput of its application (e.g 25 frames per second for a video decoder), then takes full benefit from the compilation of the SDF graph to detect bottlenecks in this graph and identify causes among processor or memory overloading. It can then be used to perform dynamic adaptations of the applications in order to optimize the use of computing and memory resources.

关键词： Streaming Video Decoder Runtime Computer personnel Quality of service monitoring systems Traffic Bottlenecks Collected papers PROCESSOR CPU architecture

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Multi-Agent Deep Reinforcement Learning Based Consistency Control in Train Platoon systems 23

Multi-Agent Deep Reinforcement Learning Based Consistency Co...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Xinran Nie, Gaofeng Ni, Wanli Tian, Hui Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

ISBN: (纸本)9781665494571

The train platoon (TP) in the urban rail transit (URT) system has been regarded as a promising solution to improve the transportation capacity between metro stations. Moreover, TP has a great potential to effectively adapt to the high dynamic flow of passengers during major events or festivals. Unfortunately, the promotion of TP is challenged due to the auto-block system in the traditional URT system as well as the complicated and dynamic operation environment. This paper investigates a cooperative control method to realize the consistency of TP in the premise of anti-interference. The multi-agent system of TP based on leader-follower typology is designed to achieve the operation goals in terms of distance and velocity. A novel distributed multi-agent deep reinforcement learning algorithm is proposed to obtain the optimal control strategy, where the artificial potential field is structured to provide prior knowledge of the environment. Compared with the conventional Q-learning based methods, simulation results validate that the proposed algorithm not only benefits the achievement of the operation goal, but also prevents the unfavorable interference from the external system. © 2021 IEEE.

关键词： Rails Q-learning Simulation Optimal control Transportation Bidirectional control Security

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Edge computing system with multi-LIDAR sensor network for robustness of autonomous personal-mobility 42

Edge computing system with multi-LIDAR sensor network for ro...

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42nd IEEE international conference on distributed computing systems (ICDCS)

作者： Akiyama, Kuon Shinkuma, Ryoichi Yamamoto, Chotaro Saito, Mai Ito, Toshio Shibaura Inst Technol Fac Engn Koto City Japan Shibaura Inst Technol Grad Sch Syst Engn & Sci Koto City Japan

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

One of the most important systems for smart cities is an edge computing system to facilitate decision-making by using real-time awareness and data analytics. Although existing works focused on the safety of pedestrians, the safety of personalmobility vehicles is what smart cities need to address toward the future. On-board sensors could be easily disabled because of external issues of hardware caused by dirt or snow, internal issues of hardware, and software issues such as malware infection. We propose an edge computing system that supports robust vehicle automation with a sensor network using multiple light-detection-and-ranging (LIDAR) sensors deployed like roadside cameras or indoor security cameras for surveillance for personal-mobility vehicles. We develop a prototype system of the proposed system using real LIDAR units and a mobility scooter and evaluate the system in terms of route errors to verify the feasibility of the proposed system with a scenario where the vehicle is enabled to continue self-driving even when its on-board sensor is disabled.

关键词： smart monitoring LIDAR 3D sensor network autonomous vehicle personal mobility edge computing system

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OEDASA: Optimization Enabled Error-Diluted Approximate Systolic Array Design for an Image Processing Application 37

OEDASA: Optimization Enabled Error-Diluted Approximate Systo...

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37th international conference on VLSI Design (VLSID) / 23rd international conference on Embedded systems (ES)

作者： Devi, Dantu Nandini Kumar, Gandi Ajay Gowda, Bindu G. Rao, Madhav IIIT Bangalore Bangalore Karnataka India

ISBN: (纸本)9798350384406

Systolic Array (SA) architecture is a hardware accelerator for running Artificial Intelligence (AI) workloads. Although approximate computing offers hardware and performance benefits, it often sacrifices accuracy, limiting its application to error-resilient tasks. Many inexact multipliers in SA exhibit one-sided Error Distribution (Erd), resulting in significant accumulated errors. Approximate computing proves valuable in error-resilient image processing applications, fostering research into various approximation techniques for enhanced hardware performance. This paper explores SA architecture with different configurations of approximate multipliers (AMs) featuring distinct Erds. It employs a meta-heuristic optimization algorithm, Particle Swarm Optimization (PSO), for image smoothing tasks. The study delves into trade-offs between hardware acceleration and accuracy, offering insights for advancing approximate computing in SA-based AI workloads. The PSO-evolved SA configuration showcases a 3.05% performance improvement, 10.63% silicon footprint reduction, and 10.32% power savings compared to the exact SA. The PSO-derived SA structure also offers notable hardware gains when compared with other state-of-theart (SOTA) benchmark designs. Additionally, it demonstrates a better Structural Similarity Index (SSIM) compared to SA with one-sided error-distributed AMs. The proposed spatially optimized SAs with AMs of different Erds represent a step towards reliable hardware design and establishing a nearly exact AI accelerator system. All design files and results are shared openly with the research and design community for easy adoption and further exploration.

关键词： Systolic Array Convolution Approximate computing Image Processing Particle Swarm Optimisation (PSO)

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Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics 23

Towards Understanding Optimal Load-Balancing of Heterogeneou...

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23rd Annual IEEE international conference on High Performance computing, Data, and Analytics (HiPCW)

作者： Hirschmann, Steffen Pflueger, Dirk Glass, Colin W. Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Univ Stuttgart High Performance Comp Ctr Stuttgart Stuttgart Germany

ISBN: (纸本)9781509057733

For heterogeneous dynamic short-range molecular dynamics simulations it is critical to employ suitable loadbalancing methods to minimize the time to solution. However, designing, selecting and parametrizing the optimal loadbalancing method is a complex task which depends on detailed properties of the simulation scenario. The main challenge in balancing the load of molecular dynamics simulations is the extreme difference in load for scenarios with a heterogeneous particle density, which can easily reach 4-6 orders of magnitude. Therefore, heterogeneity is deemed to be a relevant property. In this paper, we formulate a suitable metric to reliably quantify heterogeneity. We apply this metric, which is based on the binning of particles and the evaluation of statistical moments, to example scenarios, and we correlate the results to the performance of five load balancing methods. Furthermore, we identify the load dynamics as a second relevant property. It quantifies how rapidly the load varies over time, and we introduce corresponding metrics. We show that the load dynamics can be used to determine how long the benefits of a specific partitioning are expected to last. The results indicate that these metrics are useful to differentiate between scenarios, and that they facilitate reasoning over the complex relationship between particle simulation scenarios and optimal load balancing methods. This work is a first step towards understanding this relationship, and it introduces key concepts that we regard as crucial in this process.

关键词： short-range molecular dynamics parallelization spatial decomposition distributed memory linked-cell load-balancing heterogeneity load dynamics

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DDoS Defense Scheme Based on Machine Learning in Software-Defined Networking 23

DDoS Defense Scheme Based on Machine Learning in Software-De...

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作者： Zhong, Hong Yu, Chao Cui, Jie Sun, Xiuwen Gu, Chengjie Liu, Lu School of Computer Science and Technology Anhui University Hefei China Security Research Institute New H3C Group Hefei China

ISBN: (纸本)9781665494571

Since software-defined networking (SDN) has the ad-vantages of central control programmability and global view, it is suitable for industrial network. However, large-scale security risks such as distributed denial of service (DDoS) attacks endanger the security and reliability of services in the SDN-based system. Therefore, a comprehensive and efficient solution to this issue is crucial. This paper proposes a scheme for mitigating DDoS attack traffic deployed on an SDN application plane. The framework periodically obtains the traffic statistics of the switch ports and flow tables. After obtaining the traffic characteristics, a machine learning model is used for training. The trained model can identify benign and malicious traffic. After the attack is found, the rules are sent to the switch to control the flow and resist DDoS attacks. We use the CICDDoS2019 dataset to perform experiments. The experimental results show that compared to other schemes, the proposed scheme achieves more accurate classification. © 2021 IEEE.

关键词： Training Support vector machines Switches Machine learning Denial-of-service attack Feature extraction Control systems

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A Real-time Demand Response Strategy of Home Energy Management by Using distributed Deep Reinforcement Learning 23

A Real-time Demand Response Strategy of Home Energy Manageme...

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作者： Liu, Weirong Wang, Yujie Jiang, Fu Cheng, Yijun Rong, Jieqi Wang, Chenglong Peng, Jun School of Computer Science and Engineering Central South University Changsha China School of Automation Central South University Changsha China

ISBN: (纸本)9781665494571

Home energy management system (HEMS) autonomously schedules the energy usage of home electricity consuming devices to reduce the electricity cost based on real-time electricity price. However, it is very challenging to design an optimal energy management strategy under the uncertainty of the resident's behavior, outdoor temperature and renewable generation. To solve this problem, a real-time demand response (DR) strategy based on deep reinforcement learning (DRL) is proposed. Firstly, the real-time DR management problem is modeled as a Markov game, and then a home energy management algorithm is proposed based on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3). It is worth mentioning that the proposed algorithm builds the thermal dynamics model and takes energy storage system (ESS) into account, which improves the utilization of renewable power generation effectively. The simulation result based on real-world data demonstrates the effectiveness of the proposed algorithm. © 2021 IEEE.

关键词： Renewable energy sources Schedules Costs Uncertainty Heuristic algorithms Simulation Reinforcement learning

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A metamodel for distributed ensembles of virtual appliances

A metamodel for distributed ensembles of virtual appliances

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SEKE 2011 - Proceedings of the 23rd international conference on Software Engineering and Knowledge Engineering

作者： Collazo-Mojica, Xabriel J. Sadjadi, S. Masoud School of Computing and Information Sciences Florida International University Miami FL 33199 United States

ISBN: (纸本)1891706292

We present our work on modeling distributed ensembles of virtual appliances (DEVAs) on Infrastructure as a Service (IaaS) clouds. Designing solutions on IaaS providers require a good under standing of the underlying details such as the software installation or the network configuration. We propose the use of DEVAs, a modeling approach built on top of the notion of virtual appliances, that allows easy-to-compose and ready-to-use cloud application architectures that are IaaS-agnostic, and that abstract away unnecessary details for web application developers. In this paper, we extend the definition of a DEVA from previous work by presenting an underlying metamodel and how that metamodel can be transformed to an actual deployment. We also present a case study where we model a web application architecture and we discuss how we can instantiate it in an IaaS cloud. We argue that the DEVA modeling approach is suitable for typical cloud use cases.

关键词： Cloud computing architecture

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