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IEEE workshop on Computers in Power Electronics

作者： Lalit Kishore Marepalli Kaushik Gajula Luis Herrera University at Buffalo Buffalo NY USA

ISBN: (数字)9781728171609

ISBN: (纸本)9781728171616

Model Predictive Control (MPC) is used to regulate the voltage and desired current flows in a dc microgrid. The optimization problem is solved using a distributed approach, the alternating direction method of multipliers (ADMM). A four node dc electric system with simplified dynamics and a six node network with 6 state source models are used as case studies. Computation times are compared using CVX, quadprog and mpcqpsolver. real-time control hardware in the loop (CHIL) implementation using Opal RT and Simulink is demonstrated. The system can be extended to include higher dynamic models such as Modular Multilevel Converter (MMC) stations.

关键词： Optimization Microgrids Vehicle dynamics Computational modeling Predictive control real-time systems Power system dynamics

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Live Demonstration: Multi-Laptop HEVC Encoding

Live Demonstration: Multi-Laptop HEVC Encoding

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Sami Ahovainio Alexandre Mercat Jarno Vanne Computing Sciences Tampere University Finland

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

This paper presents a demonstration setup for distributed real-time HEVC encoding on a multi-computer system. The demonstrated multi-level parallelization scheme is implemented in the practical Kvazaar open-source HEVC encoder. It allows Kvazaar to exploit parallelism at three levels: 1) Single Instruction Multiple Data (SIMD) optimized coding tools at the data level; 2) Wavefront parallel Processing (WPP) and Overlapped Wavefront (OWF) parallelization strategies at the thread level; and 3) distributed slice encoding on multi-computer systems at the process level. This interactive demonstration allows visitors to gradually increase the degree of parallelism in Kvazaar and see the benefits of parallelization in live HEVC encoding. Exploiting all three levels of parallelism on a three-laptop setup speeds up Kvazaar by almost 21× over a non-parallelized single-core implementation of Kvazaar.

关键词： Encoding parallel processing High efficiency video coding Streaming media Portable computers real-time systems Open source software

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Multiobjective feature selection for microarray data via distributed parallel algorithms

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 100卷 952-981页

作者： Cao, Bin Zhao, Jianwei Yang, Po Yang, Peng Liu, Xin Qi, Jun Simpson, Andrew Elhoseny, Mohamed Mehmoode, Irfan Muhammad, Khan Hebei Univ Technol State Key Lab Reliabil & Intelligence Elect Equip Tianjin Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China Liverpool John Moores Univ Dept Comp Sci Liverpool Merseyside England Mansoura Univ Fac Computers & Informat Mansoura Egypt Univ Bradford Dept Media Design & Technol Fac Engn & Informat Bradford BD7 1DP W Yorkshire England Sejong Univ Dept Software Seoul 143747 South Korea

Many real-world problems are large in scale and hence difficult to address. Due to the large number of features in microarray datasets, feature selection and classification are even more challenging for such datasets. Not all of these numerous features contribute to the classification task, and some even impede performance. Through feature selection, a feature subset that contains only a small quantity of essential features can be generated to increase the classification accuracy and significantly reduce the time consumption. In this paper, we construct a multiobjective feature selection model that simultaneously considers the classification error, the feature number and the feature redundancy. For this model, we propose several distributed parallel algorithms based on different encodings and an adaptive strategy. Additionally, to reduce the time consumption, various tactics are employed, including a feature number constraint, distributed parallelism and sample-wise parallelism. For a batch of microarray datasets, the proposed algorithms are superior to several state-of-the-art multiobjective evolutionary algorithms in terms of both effectiveness and efficiency. (C) 2019 Published by Elsevier B.V.

关键词： Microarray dataset High dimension Multiobjective feature selection distributed parallelism Feature redundancy

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Speed up genetic algorithms in the cloud using software containers

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 276-289页

作者： Salza, Pasquale Ferrucci, Filomena USI Univ Svizzera Italiana Fac Informat Lugano Switzerland Univ Salerno Dept Comp Sci Salerno Italy

Scalability issues might prevent Genetic Algorithms from being applied to real world problems. Exploiting parallelisation in the cloud might be an affordable approach to getting time efficient solutions that benefit of the appealing features of scalability, resource discovery, reliability, fault-tolerance and costeffectiveness. Nevertheless, parallel computation is very prone to cause considerable overhead for communication. Also, making Genetic Algorithms distributed in an on-demand fashion is not trivial. Aiming at keeping under control the communication cost and, at the same time, supporting developers in the construction and deployment of parallel Genetic Algorithms, we designed and implemented a novel approach to distribute Genetic Algorithms in the form of a cloud-based application. It is based on the master/slave model, exploiting software containers, their cloud orchestration and message queues. We also devised a conceptual workflow covering each cloud Genetic Algorithms distribution phase, from resources allocation to actual deployment and execution, in an engineered fashion. Then, the application performance has been evaluated using a benchmark problem. According to the performance and setup times results, it emerged that the cloud can be considered a compelling way of scaling Genetic Algorithms and an excellent alternative to other technologies strictly related to the physically owned hardware. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Genetic algorithms parallel genetic algorithms Cloud computing Software containers Software engineering

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MobileEdge: Enhancing On-board Vehicle Computing Units using Mobile Edges for CAVs 25

MobileEdge: Enhancing On-board Vehicle Computing Units using...

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25th IEEE International Conference on parallel and distributed systems (IEEE ICPADS)

作者： Wang, Lin Zhang, Qingyang Li, Youhuizi Zhong, Hong Shi, Weisong Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Hangzhou Dianzi Univ Key Lab Complex Syst Modeling & Simulat Hangzhou Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9781728125831

As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more third-party applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in real-time videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the on-board vehicle computing unit (VCU) and increasing use of computing-intensive in-vehicle applications, the burden of on-board VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable on-board VCU, which leverages mobile devices as edge nodes, e.g., the passengers' smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near real-time when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.

关键词： edge computing vehicular data analysis distributed computing connected and autonomous vehicles

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Run-time Performance Monitoring, Verification, and Healing of End-to-End Services 5

Run-time Performance Monitoring, Verification, and Healing o...

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5th IEEE Conference on Network Softwarization (IEEE NetSoft) / 2nd International workshop on Advances in Slicing for Softwarized Infrastructures (S4SI)

作者： Choi, Nakjung Jagadeesan, Lalita Kim, Young Jin Mohanasamy, Nishok Narasimha Rahman, Muntasir Raihan Sabnani, Krishan Thottan, Marina Nokia Bell Labs Murray Hill NJ 07974 USA

ISBN: (纸本)9781538693766

Softwarization enables tremendous flexibility for networks as the use of software-defined networking (SDN) and programmable data planes (e.g. P4) together support dynamic reconfiguration of networks in real-time, in response to network conditions and new service requests. In such networks, it is imperative to ensure that end-to-end network services continue to satisfy SLAs, especially performance requirements. However, in the presence of unpredictable dynamics due to network reconfigurations, it is not possible to guarantee prior to deployment that a service will meet such SLAs. Run-time verification - in combination with programmable control and data plane monitoring - can provide a basis for detecting potential performance SLA violations, together with identifying and executing appropriate network mitigations. In this paper, we propose a verification transverse based on formal specifications, that spans performance SLAs across the distributed SDN control and programmable data planes, and can coordinate with both planes to execute dynamic reconfiguration that mitigate the detected issues. We demonstrate a proof-of-concept prototype based on an extension of the Aerial run-time verification tool, together with the Inband Network Telemetry (INT) capability, on a network running distributed ONOS controllers together with a P4 data plane.

关键词： Monitoring real-time systems distributed databases Voting Tools Telemetry Streaming media distributed databases Streaming Video Vote real-time systems telemetry plane Network dynamic reconfiguration Wound Healing Network reconfiguration Programmable control runtime performance Verification end-to-end Formal specifications

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real-time testbed for validating distributed antenna scenarios 20

Real-time testbed for validating distributed antenna scenari...

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20th International ITG workshop on Smart Antennas, WSA 2016

作者： Peethala, Sundar Daniel V. Kreul, Theo Kaiser, Thomas Institut für Digitale Signalverarbeitung Universität Duisburg-Essen Duisburg47057 Germany

ISBN: (纸本)9783800741779

distributed Antenna systems (DAS) promise to deliver solutions for the future wireless challenges using multiple remote radio heads (RRHs) in a distributed architecture, thereby supporting high frequency re-use, increased coverage, decreasing interference and latency. DAS architecture consists of multiple RRHs connected to a centralized base station via an optical fiber network. This work explains different DAS deployment techniques on a real-time LTE system. It focuses on the implementation details of LTE PHY on a TI (Texas Instruments) DSP and corresponding processing blocks that need to be adapted to support the functionality of DAS. Different DAS use case scenarios of deployment, their advantages and key performance indicators, are analyzed. This work also includes the measurement results of deploying antenna selection scenario to investigate and validate DAS on a real-time LTE system. © VDE VERLAG GMBH · Berlin · Offenbach, Germany.

关键词： Digital signal processing

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Multi-Stage distributed Computing for Big Data: Evaluating Connective Topologies

Multi-Stage Distributed Computing for Big Data: Evaluating C...

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IEEE Annual Computing and Communication workshop and Conference (CCWC)

作者： Rasha S. Gargees Grant J. Scott University of Missouri Columbia Mo USA

ISBN: (数字)9781728137834

ISBN: (纸本)9781728137841

With the increase in computation and data intensive needs along with the real-time requirements in the big data era, a distributed framework that can handle parallel processing at various levels becomes a necessity. In this paper, we propose a multi-stage framework that leverages distributed computational agents to handle large-scaled data in parallel. The computational agents are distributed both in breadth (parallelism) and depth (various stages) to pursue the scalability, parallelism, and heterogeneity of modern computational big data workflows. Additionally, the proposed framework allows the interleaving of computation among the stages to speed up the processing. This high throughput computing (HTC) framework is resource efficient, depth and breadth scalable, and supports both distributed agents and centralized analytics. Herein, three computational agent sequential-stage connection topologies are investigated: fully connected, partition, and mesh connectivity. To evaluate the performance of the proposed framework, a large-scale, remote sensing imagery data deep feature extraction and clustering application has been adapted as a use case. Various experiments show that the internal performances of the framework based on the queue size varies according to the type of the topology. While the total time of the experiments are affected mostly by the number of agents in each stage as expected. Additionally, it has been observed that the setup of the partition topology affects the time as well. The system is shown to maintain its resource efficient HTC performance under all the connection topologies, wherein measurement metrics, such as the average CPU usage and average memory usage, reveal nearly the same performance for all the topologies except the partitioning topology.

关键词： Topology Computer architecture Feature extraction Metadata Big Data Task analysis distributed databases

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9th International workshop on Business Intelligence for the real-time Enterprise, BIRTE 2015, 10th International workshop on Enabling real-time Business Intelligence, BIRTE 2016 and 11th International workshop on real-time Business Intelligence and Analytics, BIRTE 2017 held in conjunction with the International Conference on Very Large Data Bases, VLDB 2017

9th International Workshop on Business Intelligence for the ...

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9th International workshop on Business Intelligence for the real-time Enterprise, BIRTE 2015, 10th International workshop on Enabling real-time Business Intelligence, BIRTE 2016 and 11th International workshop on real-time Business Intelligence and Analytics, BIRTE 2017 held in conjunction with the International Conference on Very Large Data Bases, VLDB 2017

ISBN: (纸本)9783030241230

The proceedings contain 13 papers. The special focus in this conference is on International workshop on Business Intelligence for the real-time Enterprise, International workshop on Enabling real-time Business Intelligence, and International workshop on real-time Business Intelligence and Analytics, held in conjunction with the International Conference on Very Large Data Bases. The topics include: Towards Interactive Data Exploration;DCS: A Policy Framework for the Detection of Correlated Data Streams;towards Dynamic Data Placement for Polystore Ingestion;a Federated In-memory Database System for Life Sciences;an Integrated Architecture for real-time and Historical Analytics in Financial Services;processing of Aggregate Continuous Queries in a distributed Environment;high-Availability at Massive Scale: Building Google’s Data Infrastructure for Ads;past and Future Steps for Adaptive Storage Data systems: From Shallow to Deep Adaptivity;polyRecs: Improving Page–View Rates Using real-time Data Analysis;Enabling real time Analytics over Raw XML Data;Multi-engine Analytics with IReS.

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distributed Processing of Electrical Meters Surveying

Distributed Processing of Electrical Meters Surveying

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Moscow workshop on Electronic and Networking Technologies (MWENT)

作者： Anton Ivaschenko Arkadiy Krivosheev Computing Department Samara State Technical University Samara Russia

ISBN: (数字)9781728125725

ISBN: (纸本)9781728125732

distributed processing of measurement results in a complex system is concerned with increasing computational load and challenges of reliable data fixation and association. In this paper there is proposed an intelligent system intended to collect, transfer, store, and process the data series gathered by distributed devices based on Big Data technologies. The example is given for stream processing of photo images produced by a number of distributed cameras. The introduced solution was implemented to process and analyze the results of electrical meters that are manually monitored by a group of patrol personnel inspectors using hand held devices. This example illustrates a necessity to provide autonomous preprocessing of photo images by an intelligent mobile application.

关键词： Meters Monitoring Neural networks Liquid crystal displays real-time systems Cameras Data processing

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