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parallel Closed-Loop Connected Vehicle Simulator for Large-Scale Transportation Network Management: Challenges, Issues, and Solution Approaches

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2019年第4期11卷 62-77页

作者： Hoque, Mohammad A. Hong, Xiaoyan Ahmed, Salman East Tennessee State Univ Vehicular Network Lab 2109 W Market StSuite 125 Johnson City TN 37604 USA Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Virginia Tech Comp Sci Blacksburg VA USA

The augmented scale and complexity of urban transportation networks have significantly increased the execution time and resource requirements of vehicular network simulations, exceeding the capabilities of sequential simulators. The need for a parallel and distributed simulation environment is inevitable from a smart city perspective, especially when the entire city-wide information system is expected to be integrated with numerous services and ITS applications. In this paper, we present a conceptual model of an Integrated distributed Connected Vehicle Simulator (IDCVS) that can emulate real-time traffic in a large metro area by incorporating hardware-in-the-loop simulation together with the closed-loop coupling of SUMO and OMNET++. We also discuss the challenges, issues, and solution approaches for implementing such a parallel closed-loop transportation network simulator by addressing transportation network partitioning problems, synchronization, and scalability issues. One unique feature of the envisioned integrated simulation tool is that it utilizes the vehicle traces collected through multiple roadway sensors-DSRC onboard unit, magnetometer, loop detector, and video detector. Another major feature of the proposed model is the incorporation of hybrid parallelism in both transportation and communication simulation platforms. We identify the challenges and issues involved in IDCVS to incorporate this multi-level parallelism. We also discuss the approaches for integrating hardware-in-the-loop simulation, addressing the steps involved in preprocessing sensor data, filtering, and extrapolating missing data, managing large real-time traffic data, and handling different data formats.

关键词： real-time systems Data models Protocols Wireless networks Computational modeling

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distributed Computation of a Robust Estimator Based on Cauchy Noises

Distributed Computation of a Robust Estimator Based on Cauch...

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IEEE Conference on Decision and Control

作者： Nathaniel Snyder Moshe Idan Jason L. Speyer UCLA Faculty of Aerospace Engineering Technion Haifa Israel

ISBN: (纸本)9781665436601

A real-time, recursive, multivariate estimation algorithm for time-invariant and time-varying linear systems with modelled Cauchy noises is developed. When previously compared to the Kalman Filter, the Multivariate Cauchy Estimator was shown to be robust against impulsive disturbances in the process or measurement functions, but proved computationally intractable for real-time estimation applications. Two significant insights allow for a reformulation of the Multivariate Cauchy Estimator to possess a streamlined recursive and computationally reduced characteristic function of the conditional probability density function of the system state-vector given the measurement sequence. This characteristic function is represented by a sum of terms, expanding with each measurement. First, we show that a cell-enumeration matrix can be computed for each hyperplane arrangement embedded within each term of the characteristic function of the Cauchy Estimator. We then show that functions used to formulate the terms of this characteristic function can be expressed as a vector of parameters operating on basis functions constructed from this enumeration matrix. This vector is obtained by solving an under-determined system of equations. We demonstrate that our reformulation allows all terms with equal hyperplane arrangements to be reduced into a unique set. Secondly, we take advantage of advances in parallel processing to exploit the inherent parallelism found in the characteristic function of the Cauchy Estimator. A three state time-invariant system example is used to illustrate the performance of the Cauchy Estimator against the Kalman Filter when subjected to Gaussian and Cauchy noises. We report computational savings of over 99% when compared to the previous formulation. Furthermore, we discuss the real-time architecture of the Cauchy Estimator and report the execution speeds for a three-state system implemented on a single NVIDIA GeForce GTX 1060 graphics processing unit (GPU).

关键词： Linear systems Graphics processing units Estimation Probability density function parallel processing real-time systems Mathematical models

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parallel Mining of Partial Periodic Itemsets in Big Data 1

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33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent systems (IEA/AIE)

作者： Saideep, C. Kiran, R. Uday Zettsu, Koji Wu, Cheng-Wei Reddy, P. Krishna Toyoda, Masashi Kitsuregawa, Masaru Int Inst Informat Technol Hyderabad Hyderabad Telangana India Natl Inst Informat & Commun Technol Tokyo Japan Univ Tokyo Tokyo Japan Univ Aizu Aizu Wakamatsu Fukushima Japan Natl Ilan Univ Yilan Taiwan Natl Inst Informat Tokyo Japan

ISBN: (数字)9783030557898

ISBN: (纸本)9783030557881;9783030557898

Partial Periodic itemsets are an important class of regularities that exist in a temporal database. A Partial Periodic itemset is something persistent and predictable that appears in the data. Past studies on Partial Periodic itemsets have been primarily focused on centralized databases and are not scalable for Big Data environments. One cannot ignore the advantage of scalability by using more resources. This is because we deal with large databases in a real-time environment and using more resources can increase the performance. To address the issue we have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines and mining the identical itemsets independently over the different machines. Experiments on Apache Spark's distributed environment show that the proposed approach speeds up with the increase in a number of machines.

关键词： Data mining Periodic pattern mining time series MapReduce Big data analytics Spark

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Efficient Operator Placement for distributed Data Stream Processing Applications

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IEEE TRANSACTIONS ON parallel AND distributed systems 2019年第8期30卷 1753-1767页

作者： Nardelli, Matteo Cardellini, Valeria Grassi, Vincenzo Lo Presti, Francesco Univ Roma Tor Vergata I-00133 Rome Italy

In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentralize the execution of DSP applications. Since determining the Optimal DSP Placement (for short, ODP) is an NP-hard problem, we need efficient heuristics that can identify a good application placement on the computing infrastructure in a feasible amount of time, even for large problem instances. In this paper, we present several DSP placement heuristics that consider the heterogeneity of computing and network resources;we divide them in two main groups: model-based and model-free. The former employ different strategies for efficiently solving the ODP model. The latter implement, for the problem at hand, some of the well-known meta-heuristics, namely greedy first-fit, local search, and tabu search. By leveraging on ODP, we conduct a thorough experimental evaluation, aimed to assess the heuristics' efficiency and efficacy under different configurations of infrastructure size, application topology, and optimization objectives.

关键词： distributed data stream processing geo-distributed systems heuristics operator placement quality of service

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Intra-Task Priority Assignment in real-time Scheduling of DAG Tasks on Multi-Cores

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IEEE TRANSACTIONS ON parallel AND distributed systems 2019年第10期30卷 2283-2295页

作者： He, Qingqiang Jiang, Xu Guan, Nan Guo, Zhishan Hong Kong Polytech Univ Dept Comp Hung Hom Hong Kong Peoples R China Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

real-time scheduling and analysis of parallel tasks modeled as directed acyclic graphs (DAG) have been intensively studied in recent years. However, no existing work has explored the execution order of eligible vertices within a DAG task. In this paper, we show that this intra-task vertex execution order has a large impact on system schedulability and propose to control the execution order by vertex-level priority assignment. We develop analysis techniques to bound the worst-case response time for the proposed scheduling strategy and design heuristics for proper priority assignment to improve system schedulability as much as possible. We further extend the proposed approach to the general setting of multiple recurrent DAG tasks. Experiments with both realistic parallel benchmark applications and randomly generated workload show that our method consistently outperforms state-of-the-art methods with different task graph structures and parameter configurations.

关键词： Intra-task priority assignment response time analysis parallel real-time tasks multi-cores

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Collaborative Internet of Things (C-IoT) Data Analysis for Enhancing Activity Recognition and Preventing Serious Health Problems

Collaborative Internet of Things (C-IoT) Data Analysis for E...

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IEEE International workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： Imad Belkacem Aymen Gammoudi Nasredine Cheniki Yacine Sam Nizar Messai University of Tours LIFAT Laboratory Tours France

ISBN: (纸本)9781665427906

Collaborative Internet of Things (C-IoT) is an emerging paradigm that has generated a large amount of accessible and usable data in real-time, constituting an interesting tool for decision-making problems. The paper presents a healthcare data analytics approach based on collecting and analyzing data from C-IoT. We first provide an architectural plan involving several distributed sites collaborating to collect necessary data for patient's health conditions monitoring. We use for that well-known IoT technologies like Radio Frequency IDentification (RFID), Near Field Communication(NFC), Beacons and Ambient Assisted Living sensors. We then learn on such data to create adequate patient profiles and use the Pearson Correlation Coefficient (PCC) over a long period of time data to detect potential health risks. Finally, decisions are made regarding the patient's condition according to the symptoms/diseases detected.

关键词： Correlation coefficient Data analysis Decision making Collaboration distributed databases Medical services real-time systems

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基于消息调度的分布式雷达产品生成系统

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计算机系统应用 2023年第6期32卷 107-114页

作者：赵子诚郭威张浩博中国电子科技集团公司第三十二研究所上海201808

随着天气雷达新技术的发展,传统单一封闭式的雷达产品生成系统已不能较好地适应新体制天气雷达系统快速的探测速率和多源的数据形式.为了提高雷达产品生成系统的实时性和可扩展性,本文提出一种基于消息调度的分布式雷达产品生成系统.基... 详细信息

随着天气雷达新技术的发展,传统单一封闭式的雷达产品生成系统已不能较好地适应新体制天气雷达系统快速的探测速率和多源的数据形式.为了提高雷达产品生成系统的实时性和可扩展性,本文提出一种基于消息调度的分布式雷达产品生成系统.基于ActiveMQ消息中间件设计了分布式集群的任务调度策略,采用Ceph分布式文件系统构建统一高效的存储服务,并利用GPU实现了对气象算法的并行加速.目前系统已在实际项目中交付使用,取得了良好的运行效果,具有一定的推广价值和应用意义.

关键词：消息中间件分布式并行计算雷达产品生成

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IMAGE-DRIVEN BIOPHYSICAL TUMOR GROWTH MODEL CALIBRATION

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2020年第3期42卷 B549-B580页

作者： Scheufele, Klaudius Subramanian, Shashank Mang, Andreas Biros, George Mehl, Miriam Univ Stuttgart Inst Parallel & Distributed Syst Univ Str 38 D-70569 Stuttgart Germany Univ Austin Oden Inst Computat Engn & Sci 201 E 24th St Austin TX 78712 USA Univ Houston Dept Math 3551 Cullen Blvd Houston TX 77204 USA

We present a novel formulation for the calibration of a biophysical tumor growth model from a single-time snapshot, multiparametric magnetic resonance imaging (MRI) scan of a glioblastoma patient. Tumor growth models are typically nonlinear parabolic partial differential equations (PDEs). Thus, we have to generate a second snapshot to be able to extract significant information from a single patient snapshot. We create this two-snapshot scenario as follows. We use an atlas (an average of several scans of healthy individuals) as a substitute for an earlier, pretumor, MRI scan of the patient. Then, using the patient scan and the atlas, we combine image-registration algorithms and parameter estimation algorithms to achieve a better estimate of the healthy patient scan and the tumor growth parameters that are consistent with the data. Our scheme is based on our recent work (Scheufele et al., Comput. Methods Appl. Mech. Engrg., to appear), but we apply a different and novel scheme where the tumor growth simulation in contrast to the previous work is executed in the patient brain domain and not in the atlas domain yielding more meaningful patient-specific results. As a basis, we use a PDE-constrained optimization framework. We derive a modified Picard-iteration-type solution strategy in which we alternate between registration and tumor parameter estimation in a new way. In addition, we consider an l(1) sparsity constraint on the initial condition for the tumor and integrate it with the new joint inversion scheme. We solve the sub-problems with a reduced space, inexact Gauss-Newton-Krylov/quasi-Newton method. We present results using real brain data with synthetic tumor data that show that the new scheme reconstructs the tumor parameters in a more accurate and reliable way compared to our earlier scheme.

关键词： tumor progression inversion biophysical model calibration image registration PDE-constrained optimization Picard iteration

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A Holistic Energy-Efficient real-time Scheduler for Mixed Stream and Batch Processing Workloads

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IEEE TRANSACTIONS ON parallel AND distributed systems 2019年第12期30卷 2624-2635页

作者： Maroulis, Stathis Zacheilas, Nikos Kalogeraki, Vana Athens Univ Econ & Business Informat Athens 10434 Greece

In recent years we have experienced a wide adoption of novel distributed processing frameworks such as Apache Spark for handling batch and stream processing big data applications. An important aspect that has not been examined in these systems yet, is the energy consumption during the applications' execution. Reducing the energy consumption of modern datacenters is a necessity, as datacenters contribute over 2 percent of the total US electric usage. However, efficiently scheduling applications in distributed processing systems can be challenging as there is a trade-off between minimizing the datacenter & x0027;s energy usage and satisfying the application performance requirements. In this work we propose, ExpREsS, a scheduler for orchestrating the execution of Spark applications in a way that enables us to minimize the energy consumption while ensuring that the applications' performance requirements are met. Our approach exploits time-series segmentation for capturing the applications' energy usage and execution times, and then applies a novel DVFS technique to minimize the energy consumption. In order to tackle the limited number of application & x0027;s profiling runs, we exploit regression techniques to predict the applications' execution times and power consumption. Our detailed experimental evaluation using realistic workloads on our local cluster illustrates the working and benefits of our approach.

关键词： Power demand Energy consumption Sparks Cluster computing Central Processing Unit time-frequency analysis distributed processing distributed systems scheduling green computing apache spark cluster management

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Prompt: Dynamic Data-Partitioning for distributed Micro-batch Stream Processing systems 20

Prompt: Dynamic Data-Partitioning for Distributed Micro-batc...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Abdelhamid, Ahmed S. Mahmood, Ahmed R. Daghistani, Anas Aref, Walid G. Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781450367356

Advances in real-world applications require high-throughput processing over large data streams. Micro-batching has been proposed to support the needs of these applications. In micro-batching, the processing and batching of the data are interleaved, where the incoming data tuples are first buffered as data blocks, and then are processed collectively using parallel function constructs (e.g., Map-Reduce). The size of a micro-batch is set to guarantee a certain response-time latency that is to conform to the application's service-level agreement. In contrast to tuple-at-a-time data stream processing, micro-batching has the potential to sustain higher data rates. However, existing micro-batch stream processing systems use basic data-partitioning techniques that do not account for data skew and variable data rates. Load-awareness is necessary to maintain performance and to enhance resource utilization. A new data partitioning scheme, termed Prompt is presented that leverages the characteristics of the micro-batch processing model. In the batching phase, a frequency-aware buffering mechanism is introduced that progressively maintains run-time statistics, and provides online key-based sorting as data tuples arrive. Because achieving optimal data partitioning is NP-Hard in this context, a workload-aware greedy algorithm is introduced that partitions the buffered data tuples efficiently for the Map stage. In the processing phase, a load-aware distribution mechanism is presented that balances the size of the input to the Reduce stage without incurring inter-task communication overhead. Moreover, Prompt elastically adapts resource consumption according to workload changes. Experimental results using real and synthetic data sets demonstrate that Prompt is robust against fluctuations in data distribution and arrival rates. Furthermore, Prompt achieves up to 200% improvement in system throughput over state-of-the-art techniques without degradation in latency.

关键词： distributed Data Processing Micro-batch Stream Processing Data Partitioning Elastic Stream Processing

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