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Scale Drone Mapping on K8S: Auto-scale Drone Imagery processing on Kubernetes-orchestrated On-premise Cloud-computing Platform 6

Scale Drone Mapping on K8S: Auto-scale Drone Imagery Process...

引用

6th international conference on Geographical Information Systems Theory, applications and Management (GISTAM)

作者： Vithlani, Hemang Narendra Dogotari, Marcel Lam, Olee Hoi Ying Pruem, Moritz Melville, Bethany Zimmer, Frank Becker, Rolf Rhine Waal Univ Appl Sci Fac Commun & Environm D-47475 Kamp Lintfort Germany Astron Environm Serv 129 Royal St East Perth WA 6004 Australia

ISBN: (纸本)9789897584251

Aerial images acquired using drone-based imaging sensors can be processed by photogrammetry toolkits to create geometrically corrected 2D orthophoto and/or 3D models. This is a crucial step for many of the ever-evolving civil applications of drones such as precision agriculture and surveying. Nevertheless, limited computational resources become bottleneck in providing these results quickly. Cloud computing helps in such scenarios because of its value-added features, namely virtualization, elasticity, high performance and distributed computing for the web-based image processing. The containerization approach plays a vital role in cloud computing by providing operational efficiency. Container orchestration engine, Kubernetes, not only provides template-based or GUI-based service deployment but also better monitoring, log querying and autoscaling. The present work displays a scalable photogrammetry service, deployed on a Kubernetes-orchestrated on-premise cluster. This reference implementation on Kubernetes enables the parallel processing of large datasets in less time than a single computer using the free and open-source toolkit OpenDroneMap.

关键词： Drone Imagery Opendronemap Photogrammetry Open-source Kubernetes Cloud Computing

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QoS-Aware and Fault-Tolerant Replica Placement 20th

QoS-Aware and Fault-Tolerant Replica Placement

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20th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Hu, Jingkun Du, Zhihui Zhang, Sen Bader, David A. Worldmoney Blockchain Management Ltd Hong Kong Peoples R China New Jersey Inst Technol Newark NJ 07102 USA SUNY Coll Oneonta Oneonta NY 13820 USA

ISBN: (纸本)9783030602390;9783030602383

As emerging applications become more and more distributed and decentralized, it has become a more challenging problem to design and build fault-tolerant network systems with high Quality of Service (QoS) guarantee. In this paper, an optimal replica placement problem is formulated in terms of minimizing the replica placement cost subject to both QoS and fault-tolerant constraints. Based on the generalized graph model, the optimal replica placement problem is proved to be NP-hard. The essential properties of the proposed problem are investigated and two criteria, which can quantitatively measure the replica demand of each node and the contribution of one replica to other nodes in the graph, are proposed. The criteria are employed to develop efficient heuristic algorithms. Finally the proposed algorithms are evaluated with extensive network configurations and the experimental results show that the proposed heuristic algorithms can generate solutions very close to the optimal results.

关键词： Replica placement Quality of service Fault tolerance Heuristic algorithm distributed system

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UAVS3d: Fast Decoder for the 3rd Generation Audio Video Coding Standard (AVS3) 2020

UAVS3d: Fast Decoder for the 3rd Generation Audio Video Codi...

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4th international conference on Digital Signal processing, ICDSP 2020

作者： Wang, Zhenyu Han, Bingjie Wang, Ronggang Fan, Kui Gao, Wen Shenzhen Graduate School Peking University Shenzhen China Institute of Digital Media Peking University Beijing China

ISBN: (纸本)9781450376877

Compared with AVS2 and HEVC, the compression performance of the 3rd generation audio video coding standard (AVS3) has been greatly improved. For the preliminary application of this emerging coding standard, an efficient implementation of software-based decoder is necessary before the decoding chips are widely used. However, real-time decoding of UHD streams is still a big challenge for AVS3 standard. In this paper, we proposed a fast decoder design for AVS3, consisting of framework optimization, data structure and module optimizations, SIMD optimization, and parallel decoding. On the one hand, a non-redundant and cache-friendly decoding framework is proposed. In this framework, data structure and some key modules are optimized. On the other hand, SIMD instructions and frame-level parallelism are applied to fully accelerate decoding on a general-purpose processor. We also implemented an efficient AVS3 decoder (uAVS3d) in this paper. Experimental results show that, when decoding streams with bit depths of 10 and 8 on the i7 - 7800x processor, uAVS3d is 4.2 and 6.3 times faster than the AVS3 reference software with single-thread, respectively. uAVS3d has a speed-up of 6.2x when decoding with 17 threads comparing with single-thread on this processor, and can decode UHD streams in real-time. Additionally, uAVS3d has a decoding speed of 33.8 fps with single-thread for 1080p video on Huawei P10. Thus, uAVS3d can be used in most existing video applications on PC and mobile terminal device. © 2020 ACM.

关键词： Decoding

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Managing Failures in Task-Based parallel Workflows in distributed Computing Environments 26th

Managing Failures in Task-Based Parallel Workflows in Distri...

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Ejarque, Jorge Bertran, Marta Cid-Fuentes, Javier Alvarez Conejero, Javier Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9783030576752;9783030576745

Current scientific workflows are large and complex. They normally perform thousands of simulations whose results combined with searching and data analytics algorithms, in order to infer new knowledge, generate a very large amount of data. To this end, workflows comprise many tasks and some of them may fail. Most of the work done about failure management in workflow managers and runtimes focuses on recovering from failures caused by resources (retrying or resubmitting the failed computation in other resources, etc.) However, some of these failures can be caused by the application itself (corrupted data, algorithms which are not converging for certain conditions, etc.), and these fault tolerance mechanisms are not sufficient to perform a successful workflow execution. In these cases, developers have to add some code in their applications to prevent and manage the possible failures. In this paper, we propose a simple interface and a set of transparent runtime mechanisms to simplify how scientists deal with application-based failures in task-based parallel workflows. We have validated our proposal with use-cases from e-science and machine learning to show the benefits of the proposed interface and mechanisms in terms of programming productivity and performance.

关键词： Failure management Scientific workflows parallel programming distributed computing

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Complexity Controlled Side Information Creation for distributed Scalable Video Coding 3

Complexity Controlled Side Information Creation for Distribu...

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3rd international conference on Recent Advances in Signal processing, Telecommunications and Computing (IEEE SigTelCom)

作者： Quang Hoang Van Le Dao Thi Hue Vien Dinh Du Vu Nguyen Hong Xiem HoangVan Hanoi Univ Ind Hanoi Vietnam VNU Univ Engn & Technol Hanoi Vietnam Radio Elect Assoc Vietnam Ho Chi Minh City Vietnam

ISBN: (纸本)9781538679630

distributed scalable video coding (DSVC) has recently been gaining many attentions due to its benefits in terms of computational complexity, error resilience and scalability, which are important for emerging video applications like wireless sensor networks and visual surveillance system (VSS). In DSVC, the side information (SI) creation plays a key role as it directly affects the DSVC compression performance and the encoder/decoder computational complexity. However, for many VSS applications, the energy of each VSS node is usually attenuating along the time, making the difficulty in transmitting surveillance video in real time. To address this problem, we propose a complexity controlled SI creation solution for the newly DSVC framework. To achieve the flexible SI creation, the complexity associated with SI creation process is modeled using a linear model in which the model parameters are estimated from a fitting process. To adjust the SI complexity, a user parameter is defined based on the availability of the VSS energy resource. Experiments conducted for a rich set of video surveillance data have revealed the benefits of the proposed complexity control solution, notably in both complexity control and compression performance.

关键词： distributed scalable video coding side information visual sensor networks

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Memory Access Optimization of High-Order CFD Stencil Computations on GPU 1

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21st international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2020

作者： Wang, Shengxiang Li, Zhuoqian Che, Yonggang Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

Stencils computations are a class of computations commonly found in scientific and engineering applications. They have relatively lower arithmetic intensity. Therefore, their performance is greatly affected by memory access. This paper studies the issue of memory access optimization for the key stencil computations of a high-order CFD program on the NVidia GPU. Two methods are used to optimize the performance. First, we use registers to cache the data used by the stencil computations in the kernel. We use the CUDA warp shuffle functions to exchange data between neighboring grid points, and adjust the thread computation granularity to increase the data reuse. Second, we use the shared memory to buffer the grid data used by the stencil computations in the kernel, and utilize loop tiling to reduce redundant accesses to the global memory. Performance evaluation is done on an NVidia Tesla K80 GPU. The results show that compared to the original implementation that only uses the global memory, the optimized implementation that utilizes the registers achieves a maximum speedup of 2.59 and 2.79 relatively for 15M and 60M grids, and the optimized implementation that utilizes the shared memory achieves a maximum speedup of 3.51 and 3.36 relatively for 15M and 60M grids. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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A Semantic Model with Self-adaptive and Autonomous Relevant Technology for Social Media applications 25th

A Semantic Model with Self-adaptive and Autonomous Relevant ...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Samani, Zahra Najafabadi Lercher, Alexander Saurabh, Nishant Prodan, Radu Univ Klagenfurt Inst Informat Technol Klagenfurt Austria

ISBN: (纸本)9783030483401;9783030483395

With the rapidly increasing popularity of social media applications, decentralized control and ownership is taking more attention to preserve user's privacy. However, the lack of central control in the decentralized social network poses new issues of collaborative decision making and trust to this permission-less environment. To tackle these problems and fulfill the requirements of social media services, there is a need for intelligent mechanisms integrated to the decentralized social media that consider trust in various aspects according to the requirement of services. In this paper, we describe an adaptive microservice-based design capable of finding relevant communities and accurate decision making by extracting semantic information and applying role-stage model while preserving anonymity. We apply this information along with exploiting Pareto solutions to estimate the trust in accordance with the quality of service and various conflicting parameters, such as accuracy, timeliness, and latency.

关键词： Semantic information Community detection Pareto-trust Decentralized social media Role-stage model

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parallel Discovery of Trajectory Companions from Heterogeneous Streaming Data 43

Parallel Discovery of Trajectory Companions from Heterogeneo...

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43rd IEEE-Computer-Society Annual international Computers, Software and applications conference (COMPSAC)

作者： Xian, Yongyi Liu, Yan Xu, Chuanfei Elnikety, Sameh Concordia Univ Fac Engn & Comp Sci Montreal PQ Canada Microsoft Res Redmond WA USA

ISBN: (纸本)9781728126074

Trajectorystreams consist of large volumes of time-stamped spatial data that are constantly generated from diverse and geographically distributed sources. Discovery of traveling patterns on trajectory streams such as gathering and companies needs to process each record when it arrives and correlates across multiple records near real-time. Thus techniques for handling high-speed trajectory streams should scale on distributed cluster computing. The main issues encapsulate three aspects, namely a data model to represent the continuous trajectory data, the parallelism of a discovery algorithm, and end-to-end performance improvement. In this paper, we propose a parallel discovery method that consists of 1) a model of partitioning trajectories sampled on different time intervals;2) definition on distance measurements of trajectories;and 3) a parallel discovery algorithm. We develop this method in a stream processing workflow. From parallelization point of view, we investigate system performance, scalability, stability. Our method discovers trajectory gathering patterns with low latency and scales as the size of trajectory data grows.

关键词： Data stream processing parallel computing distributed computing Big data

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Optimal Placement of Stream processing Operators in the Fog 3

Optimal Placement of Stream Processing Operators in the Fog

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3rd IEEE international conference on Fog and Edge Computing (ICFEC)

作者： Hiessl, Thomas Karagiannis, Vasileios Hochreiner, Christoph Schulte, Stefan Nardelli, Matteo TU Wien Distributed Syst Grp Vienna Austria Univ Roma Tor Vergata Dept Civil Engn & Comp Sci Engn Rome Italy

ISBN: (纸本)9781728123653

Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoiding this delay, we leverage on the fog computing paradigm which extends the cloud to the edge of the network. In order to design a stream processing solution for the fog, we first formulate an optimization problem for the placement of stream processing operators, which is tailored to fog computing environments. Then, we build a plugin (for stream processing frameworks) which solves the optimization problem periodically in order to support the dynamic resources of the fog. We evaluate this approach by performing experiments on an OpenStack testbed. The results show that our plugin reduces the response time and the cost by 31.5% and 8.8% respectively, compared to optimizing the placement of operators only upon initialization.

关键词： Edge Computing Stream processing Internet of Things

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Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs 26th

Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Wang, Qinglin Li, Dongsheng Huang, Xiandong Shen, Siqi Mei, Songzhu Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

ISBN: (纸本)9783030576752;9783030576745

Convolutional Neural Networks (CNNs) are widely applied in various machine learning applications and very time-consuming. Most of CNNs' execution time is consumed by convolutional layers. A common approach to implementing convolutions is the FFT-based one, which can reduce the arithmetic complexity of convolutions without losing too much precision. As the performance of ARMv8 multi-core CPUs improves, they can also be utilized to perform CNNs like Intel X86 CPUs. In this paper, we present a new parallel FFT-based convolution implementation on ARMv8 multi-core CPUs. The implementation makes efficient use of ARMv8 multi-core CPUs through a series of computation and memory optimizations. The experiment results on two ARMv8 multicore CPUs demonstrate that our new implementation gives much better performance than two existing approaches in most cases.

关键词： CNNs Convolution FFT ARMv8 parallel algorithm

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