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Real-time Task Offloading for Data and Computation Intensive Services in Vehicular Fog computing Environments

Real-time Task Offloading for Data and Computation Intensive...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chunhui Liu Kai Liu Xincao Xu Hualing Ren Feiyu Jin Songtao Guo College of Computer Science Chongqing University Chongqing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Recent advances in wireless communication, sensing, and computing technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Nevertheless, it is challenging to process data and computation intensive tasks with strict time constraints due to heterogeneous communication, storage, and computation capacities of IoV network nodes, spotty wireless connections in vehicles and infrastructures, unevenly distributed workload, and high vehicles mobility. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the fog nodes, and the terminals on processing data and computation intensive IoV tasks. Then, we formulate the real-time task offloading model, aiming at maximizing the task service ratio. Further, considering the dynamic requirements and resource constraints, we propose a real-time task offloading algorithm to adaptively categorize all tasks into four types, and then cooperatively offload them. Finally, we build the simulation model and give a comprehensive performance evaluation, which validates the performance of the proposed method.

关键词： Wireless communication Adaptation models Computational modeling Heuristic algorithms Real-time systems Sensors Task analysis

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GarbMAS: Simulation of the Application of Gamification Techniques to Increase the Amount of Recycled Waste Through a Multi-agent System 15th

GarbMAS: Simulation of the Application of Gamification Techn...

引用

15th international conference on distributed computing and Artificial Intelligence (DCAI)

作者： Gonzalez-Briones, Alfonso Valdeolmillos, Diego Casado-Vara, Roberto Chamoso, Pablo Garcia Coria, Jose A. Herrera-Viedma, Enrique Corchado, Juan M. Univ Salamanca BISITE Digital Innovat Hub Edificio Multiusos I D I Salamanca 37007 Spain Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18071 Spain Osaka Inst Technol Fac Engn Dept Elect Informat & Commun Osaka 5358585 Japan Univ Malaysia Kelantan Pusat Komputeran & Informat Karung Berkunci 36 Kota Baharu Kelantan Malaysia

ISBN: (纸本)9783319946498;9783319946481

The increase in population is increasing the growth of the number of residues. A large amount of this waste can be recycled so that it does not remain in uncontrolled landfills, pollute air, land or water. Although many campaigns and policies of recycling have been developed all-way there is not a total awareness about this problem and a large number of waste does not end up in the right place to be recycled. It is necessary to increase the amount of recycled waste and for this citizen participation is key. It is vital to involve the population in a more active way to recycle through some kind of social benefit. For this reason, GarbMAS proposes a system that generates a motivation for citizen participation by reducing the garbage tax applied by its local government. Thus, the increase in the amount of waste collected to be recycled is expected. GarbMAS employs a multi-agent system that simulates data collection and efficient waste management in cities through gamification techniques to produce the change motivation of citizens and therefore increase participation and recycled quantity. The case study in which the simulation was carried out showed an increase in citizen participation by 34.2% and an increase of 29.4% in the amount of waste collected.

关键词： Behavioral change Simulation Multi-agent system Context-awareness Social computing

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Scalable Knowledge Graph Analytics at 136 Petaflop/s 20

Scalable Knowledge Graph Analytics at 136 Petaflop/s

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Supercomputing conference

作者： Ramakrishnan Kannan Piyush Sao Hao Lu Drahomira Herrmannova Vijay Thakkar Robert Patton Richard Vuduc Thomas Potok Oak Ridge National Laboratory Oak Ridge TN USA Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship-mining problem as one of computing all-pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the Floyd-Warshall algorithm for distributed-memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (distributed Accelerated Semiring All-Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4, 096nodes (24,576GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of $136\times 10^{15}$ floating-point operations per second (136petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70% of the best possible performance given our computation (in the single-precision tropical semiring or “min-plus” algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence-driven natural language processing workflows at scale.

关键词： Knowledge engineering Databases High performance computing Supercomputers Natural language processing Acceleration Data mining

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Random Convolutional Neural Network Based on distributed computing with Decentralized Architecture 5th

Random Convolutional Neural Network Based on Distributed Com...

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5th international conference on Human Centered computing (HCC)

作者： Xu, Yige Lu, Huijuan Ye, Minchao Yan, Ke Gao, Zhigang Jin, Qun China Jiliang Univ Coll Informat Engn 258 Xueyuan St Hangzhou Peoples R China Hangzhou Dianzi Univ Coll Comp Sci & Technol 1158 Baiyang St Hangzhou Peoples R China Waseda Univ Fac Human Sci Tokorozawa Saitama 3591192 Japan

ISBN: (纸本)9783030374297;9783030374280

In recent years, deep learning has made great progress in image classification and detection. Popular deep learning algorithms rely on deep networks and multiple rounds of back-propagations. In this paper, we propose two approaches to accelerate deep networks. One is expanding the width of every layer. We reference to the Extreme Learning Machine, setting big number of convolution kernels to extract features in parallel. It can obtain multiscale features and improve network efficiency. The other is freezing part of layers. It can reduce back-propagations and speed up the training procedure. From the above, it is a random convolution architecture that network is proposed for image classification. In our architecture, every combination of random convolutions extracts distinct features. Apparently, we need a lot of experiments to choose the best combination. However, centralized computing may limit the number of combinations. Therefore, a decentralized architecture is used to enable the use of multiple combinations.

关键词： distributed computing Random convolution Decentralized architecture

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Performance Efficient Multiresilience Using Checkpoint Recovery in Iterative Algorithms 24th

Performance Efficient Multiresilience Using Checkpoint Recov...

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international European conference on parallel and distributed computing (Euro-Par)

作者： Ashraf, Rizwan A. Engelmann, Christian Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA

ISBN: (纸本)9783030105495;9783030105488

In this paper, we address the design challenge of building multiresilient iterative high-performance computing (HPC) applications. Multiresilience in HPC applications is the ability to tolerate and maintain forward progress in the presence of both soft errors and process failures. We address the challenge by proposing performance models which are useful to design performance efficient and resilient iterative applications. The models consider the interaction between soft error and process failure resilience solutions. We experimented with a linear solver application with two distinct kinds of soft error detectors: one detector has high overhead and high accuracy, whereas the second has low overhead and low accuracy. We show how both can be leveraged for verifying the integrity of checkpointed state used to recover from both soft errors and process failures. Our results show the performance efficiency and resiliency benefit of employing the low overhead detector with high frequency within the checkpoint interval, so that timely soft error recovery can take place, resulting in less re-computed work.

关键词： High-performance computing Resilience Soft errors Process failures Fault injection Checkpoint restart Design patterns Iterative algorithms Linear solver Performance Analytical models

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Global Task Data-Dependencies in PGAS Applications 34th

Global Task Data-Dependencies in PGAS Applications

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34th international conference on High Performance computing (ISC High Performance)

作者： Schuchart, Joseph Gracia, Jose High Performance Comp Ctr Stuttgart HLRS Nobelstr 19 D-70569 Stuttgart Germany

ISBN: (纸本)9783030206567;9783030206550

Recent years have seen the emergence of two independent programming models challenging the traditional two-tier combination of message passing and thread-level work-sharing: partitioned global address space (PGAS) and task-based concurrency. In the PGAS programming model, synchronization and communication between processes are decoupled, providing significant potential for reducing communication overhead. At the same time, task-based programming allows to exploit a large degree of shared-memory concurrency. The inherent lack of fine-grained synchronization in PGAS can be addressed through fine-grained task synchronization across process boundaries. In this work, we propose the use of task data dependencies describing the data-flow in the global address space to synchronize the execution of tasks created in parallel onmultiple processes. We present a description of the global data dependencies, describe the necessary interactions between the distributed scheduler instances required to handle them, and discuss our implementation in the context of the DASHC++ PGASframework. We evaluate our approach using the Blocked Cholesky Factorization and the LULESH proxy app, demonstrating the feasibility and scalability of our approach.

关键词： parallel programming PGAS Task parallelism RMA

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Adaptive Load Balancing based on Machine Learning for Iterative parallel Applications

Adaptive Load Balancing based on Machine Learning for Iterat...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： C. R. Anna Victoria Oikawa Vinicius Freitas Márcio Castro Laércio L. Pilla Federal University of Santa Catarina (UFSC) Florianopolis Brazil LRI Univ. Paris-Saclay – CNRS Orsay France

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

The performance of irregular scientific applications can be easily affected by an uneven distribution of work among the computing resources. In this context, Load Balancing (LB) stands as one of the most important solutions to improve resource utilization. However, choosing the best-performing load balancing algorithm for a given application is not a trivial task. For instance, manually and statically choosing an LB algorithm does not work in situations where applications have a dynamic or unknown behavior. In this context, we propose a Machine Learning-based Adaptive Load Balancer (ADAPTIVELB) to automate the load balancing algorithm decision at run time. This approach monitors and collects information about the application dynamically, and according to the analyzed data, it makes a decision of invoking the most suitable LB algorithm. Our experiments show that ADAPTIVELB can select a good load balancing algorithm in most of the cases, leading to performance improvements over statically chosen LB algorithms and over the absence of a load balancer.

关键词： Task analysis Heuristic algorithms Load management Machine learning algorithms Adaptation models Prediction algorithms Machine learning

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Forgery Detection For High-Resolution Digital Images Using FCM And PBFOAAlgorithm

Forgery Detection For High-Resolution Digital Images Using F...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Satinder Jeet Kaur Nidhi Bhatla Sachdeva Engineering College for Girls Gharuan(Mohali) India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

Image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the exist

关键词： Forgery Feature extraction Digital images Databases Image recognition Handheld computers Grid computing

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Role of Mathematics in Image Processing

Role of Mathematics in Image Processing

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2019 international conference on Machine Learning, Big Data, Cloud and parallel computing, COMITCon 2019

作者： Vashisht, Manisha Bhatia, Madhulika Manav Racha International Institute of Research and Studies Faridabad India

ISBN: (纸本)9781728102115

The various Image Processing technologies have revolutionized the way in which a particular image is captured, stored, or retrieved from a certain source. Most commonly used Image Processing tasks include;Image Restoration, Image Segmentation, Image Enhancement, De-Blurring, De-noising, etc. Such Images are well utilized in various ways by several of the Imaging techniques, like;Angiography, MRI (Magnetic Resonance Imaging), Arterial Spin Labeling (ASL), Computer Assisted Tomography (CT), Deep Brain Stimulation (DBS), Electroencephalography (EEG), etc. But, it is the, Mathematics that has played a crucial role in various Image Processing tasks mentioned above. Despite the innovation and rapid advances in the various Imaging Technology, one thing that has remained important throughout, is, the use of *** has been observed that image processing has got strong connection with Mathematics. Many of the image processing methods rely on the basic Mathematical Techniques of Histogram Equalization, Probability and Statistics, Discrete Cosine Transforms, Fourier Transforms, Differential Equations, Integration, Matrix and Algebra. For computational purposes, Matlab is one of the most commonly used tool by the researchers in the area of Image Processing. Other Tools which are commonly used include SciLab, GNU Octave, SageMath, *** paper critically reviews the role of Mathematics in various Image Processing Applications. This paper would also present the basic Mathematical tools and techniques, used in Image processing, along with the literature review of the same. © 2019 IEEE.

关键词： Electrophysiology

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Autonomic and Latency-Aware Degree of parallelism Management in SPar 24th

Autonomic and Latency-Aware Degree of Parallelism Management...

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international European conference on parallel and distributed computing (Euro-Par)

作者： Vogel, Adriano Griebler, Dalvan De Sensi, Daniele Danelutto, Marco Fernandes, Luiz Gustavo Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre RS Brazil Univ Pisa Dept Comp Sci Pisa Italy Tres De Maio Fac Lab Adv Res Cloud Comp Tres De Maio Brazil

ISBN: (纸本)9783030105495;9783030105488

Stream processing applications became a representative workload in current computing systems. A significant part of these applications demands parallelism to increase performance. However, programmers are often facing a trade-off between coding productivity and performance when introducing parallelism. SPar was created for balancing this trade-off to the application programmers by using the C++11 attributes' annotation mechanism. In SPar and other programming frameworks for stream processing applications, the manual definition of the number of replicas to be used for the stream operators is a challenge. In addition to that, low latency is required by several stream processing applications. We noted that explicit latency requirements are poorly considered on the state-of-the-art parallel programming frameworks. Since there is a direct relationship between the number of replicas and the latency of the application, in this work we propose an autonomic and adaptive strategy to choose the proper number of replicas in SPar to address latency constraints. We experimentally evaluated our implemented strategy and demonstrated its effectiveness on a real-world application, demonstrating that our adaptive strategy can provide higher abstraction levels while automatically managing the latency.

关键词： Autonomic computing Stream processing parallel programming Adaptive degree of parallelism

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