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techniques Used to Protect A DC System with More than 100kA Short Circuit Current Capacity 36

Techniques Used to Protect A DC System with More than 100kA ...

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36th IEEE international conference on Telecommunications Energy conference (INTELEC)

作者： Coombes, Colin D. Alpha Technol Power Syst Burnaby BC V5J 5M4 Canada

ISBN: (纸本)9781479931040

With today's new battery technology and power hungry devices that may need many hours of emergency battery backup, there is the danger of short circuit currents reaching more than 100kA and exceeding the short circuit current ratings of existing DC fuses. Until manufacturers design, test and certify DC fuses with higher short circuit current rating, excessive short circuit currents pose a danger to both personnel and property. This paper discusses solutions to manage short circuit currents and make a system safe using available current limiting devices and techniques. An energy storage system project was deployed which consists of 3 sites with a total of 1MW hours of distributed lithium-ion batteries. These batteries provide stored energy to be used for uninterruptable power supply (UPS) back up and peak shaving (PS) applications. The biggest site, of the3, contains a distributed energy storage system with 64 lithium-ion batteries, rated at 416 kWh, installed in parallel, with a potential of 384kA of short circuit current and this is almost 4 times more current than a 100kA rated fuse can handle. In circuit measurements were used to determine the inductance and resistance of the system. Computer simulations were used to verify the rise times, RMS, and peak current levels required to safely interrupting the current in the fuses. Increasing the Impedance (resistance and inductance), along with proper fusing, were used to limit the short circuit current in a system. Using this combined approach, the required protection to limit the potential short circuit current, flowing through any fuse, could be shown to be reduced to fewer than 100kA.

关键词： Short circuit currents

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FastDCF: A Partial Index Based distributed and Scalable Near-Miss Code Clone Detection Approach for Very Large Code Repositories 22nd

FastDCF: A Partial Index Based Distributed and Scalable Near...

引用

22nd international conference on parallel and distributed Computing, applications and Technologies (PDCAT 2021)

作者： Yang, Liming Ren, Yi Guan, Jianbo Li, Bao Ma, Jun Han, Peng Tan, Yusong Natl Univ Def Technol Changsha Peoples R China CS&S Informat Syst Engn Co Ltd Beijing Peoples R China

ISBN: (纸本)9783030967727;9783030967710

Despite a number of techniques have been proposed over the years to detect clones for improving software maintenance, reusability or security, there is still a lack of language agnostic approaches with code granularity flexibility for near-miss clone detection in big code in scale. However, it is challenging to detect near-miss clones in big code since it requires more computing and memory resources as the scale of the source code increases. In this paper, we present FastDCF, a fast and scalable distributed clone finder, which is partial index based and optimized with multithreading strategy. Furthermore, it overcomes single node CPU and memory resource limitation with MapReduce and HDFS by scalable distributed parallelization, which further improves the efficiency. It cannot only detect Type-1 and Type-2 clones but also can discover the most computationally expensive Type-3 clones for large repositories. Meanwhile, it works for both function and file granularities. And it supports many different programming languages. Experimental results show that FastDCF detects clones in 250 million lines of code within 24 min, which is more efficient compared to existing clone detection techniques, with recall and precision comparable to state-of-the-art approaches. With BigCloneBench, a recent and widely used benchmark, FastDCF achieves both high recall and precision, which is competitive with other existing tools.

关键词： Clone detection distributed algorithm Large scale code analysis Efficiency and scalability Language agnostic Multiple granularities

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Automating software FMEA via formal analysis of dependence relations

Automating software FMEA via formal analysis of dependence r...

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32nd Annual IEEE international Computer Software and applications conference, COMPSAC 2008

作者： Dong, Wei Wang, Ji Zhao, Changzhi Zhang, Xian Tian, Jie School of Computer National University of Defense Technology China National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9780769532622

The paper presents the ongoing work of studying FMEA method for embedded safety critical software via formal analysis of various dependence relations among software elements, which can fairly improve the automation and precision of both system level and detailed level FMEA. These dependence relations are depicted by the formal models abstracted from software design and implementation, and the FMEA processes for both structural and object-oriented software are proposed respectively. The initial result of case study shows the effectiveness of the approach. I 2008 IEEE.

关键词： Safety engineering

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Energy-Efficient Scheduling of Moldable Streaming Computations for the Edge-Cloud Continuum 9

Energy-Efficient Scheduling of Moldable Streaming Computatio...

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9th IEEE international conference on Fog and Mobile Edge Computing (FMEC)

作者： Khosravi, Sajad Kessler, Christoph Litzinger, Sebastian Keller, Joerg Linkoping Univ Linkoping Sweden Fernuniv Hagen Germany

ISBN: (纸本)9798350366495;9798350366488

We consider the problem of cost-effectively mapping a swarm of soft real-time stream processing applications with moldable-parallel tasks to multicore resources in the device-edge-cloud continuum, consisting of mobile devices, edge resources and cloud resources. We leverage flexibility from different parallelization degrees and frequency levels (DVFS) for the tasks, keeping application throughput constraints and communication bandwidth limitations while minimizing overall cost (including device/edge resource energy and cloud resource renting). We present two offline algorithmic solutions with a global view of the environment: an integer linear program (ILP) extending the crown scheduling approach for multi-layer distributed systems and a greedy heuristic algorithm. Our experimental evaluation for several real-world and synthetic scenarios shows that the time required for solving the scheduling problem to cost-optimality by the ILP is feasible for nontrivial scenarios. The heuristic achieves about 12% worse cost efficiency on average, yet operates much faster (by 1-2 orders of magnitude), allowing to scale up the problem size more than the ILP approach.

关键词： distributed stream processing Mapping Scheduling Moldable tasks Edge-Cloud continuum Energy efficiency DVFS

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Exploring the parallel implementations of the three classical MST algorithms

Exploring the parallel implementations of the three classica...

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international conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Latha, N. R. Shyamala, G. Prasad, G. R. BMS Coll Engn Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781509052974

Minimum spanning tree (MST) is one among the most studied combinatorial problems with practical applications in VLSI layout and routing, wireless communication and distributed networks, current problems in biology and medicine such as cancer detection, medical imaging, and proteomics, and national security and bioterrorism[1]. Since Minimum Spanning Tree (MST) is one of the well-known classical graph problems it finds its application in various fields and many attempts are being done to improve the speed of MST using parallel computing. Most of the previous attempts made were either too complicated to implement or they perform well only on special graphs with regular structure. The widespread adaption of multicore platforms and GPU's has offered the opportunity to explore new implementation techniques for many algorithms that were originally designed for uniprocessors. By formulating new parallel schemes, the programmers would be able to exploit the multiple hardware frameworks offered in today's platforms in a more efficient way. A category of problems among the most challenging to parallelize are the ones that exhibit inherently sequential characteristics. The discovery of the Single Source Shortest Path or the composition of the Minimum Spanning Forest of a given graph falls into this category. This paper is a survey on the different parallel MST algorithms that are implemented on Multicore architectures.

关键词： parallel Minimum spanning tree algorithm Bor(o)uvka's algorithm Kruskal's algorithm Prim's algorithm

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Power Quality Analytical Methodology Using Signal processing and Machine Learning techniques for Grid Fault Classification and Location Detection 12

Power Quality Analytical Methodology Using Signal Processing...

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12th international conference on Smart Energy Grid Engineering

作者： Jabbar, Abdullah A. Wanik, Mohd Zamri Che Sanfilippo, Antonio P. HBKU QEERI Doha Qatar

ISBN: (纸本)9798350377385;9798350377378

The increase in global energy demand with a growing dynamic power system has made today's electrical network a complex infrastructure where increased faults and grid violations are being reported at higher rates. Also, with an increased penetration of intermittent distributed Energy Resource (DER), the detection of transient events occurring in a large network is becoming challenging and therefore encouraging potential areas of research in this field. Furthermore, the deployment of high-resolution devices such as PMU gives access to vast amounts of data and carries vital information to ensure grid safety and protection. This paper analyzes a methodology to detect and predict power system disturbances and fault location in electrical power network (distribution & transmission) using data-driven & machine learning techniques. It uses signal processing techniques based on multi-level discrete wavelet transforms, with wavelet spectral energy/entropy-based feature extraction stage to train ANN (Artificial Neural Network) classifiers to predict and classify faults in power system applications. Machine learning classification techniques are used based on a dataset which is generated with single measurement point in simulation environment where grid model is used with varying fault conditions such as alternating phase & neutral impedance, inception angle & distance. A comparative analysis is performed with ANN and other potential classifiers to identify potential limitation & improvements in feature extraction stage to better classify faults and location. Furthermore, PQ events such as voltage sag, voltage swell, and transient event have been analyzed and the propagating effect of faults in the network, medium voltage to low voltage network are also discussed.

关键词： Power quality discrete wavelet transform artificial neural network machine learning fault classification

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Minimizing Network Traffic for distributed Joins Using Lightweight Locality-Aware Scheduling 24th

Minimizing Network Traffic for Distributed Joins Using Light...

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24th international European conference on parallel and distributed Computing (Euro-Par)

作者： Cheng, Long Murphy, John Liu, Qingzhi Hao, Chunliang Theodoropoulos, Georgios Univ Coll Dublin PEL Dublin Ireland Eindhoven Univ Technol Eindhoven Netherlands Chinese Acad Sci Inst Software Beijing Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9783319969831;9783319969824

Large computing systems such as data centers are becoming the mainstream infrastructures for big data processing. As one of the key data operators in such scenarios, distributed joins is still challenging current techniques since it always incurs a significant cost on network communication. Various advanced approaches have been proposed to improve the performance, however, most of them just focus on data skew handling, and algorithms designed specifically for communication reduction have received less attention. Moreover, although the state-of-the-art technique can minimize network traffic, it provides fine-grained optimal schedules for all individual join keys, which could result in obvious overhead. In this paper, we propose a new approach called LAS (Lightweight Locality-Aware Scheduling), which targets reducing network communication for large distributed joins in an efficient and effective manner. We present the detailed design and implementation of LAS, and conduct an experimental evaluation using large data joins. Our results show that LAS can effectively reduce scheduling overhead and achieve comparable performance on network reduction compared to the state-of-the-art.

关键词： Scheduling

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Autonomous adaptation of cloud applications

Autonomous adaptation of cloud applications

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13th IFIP WG 6.1 international conference on distributed applications and Interoperable Systems, DAIS 2013, Held as Part of the 8th international Federated conference on distributed Computing techniques, DisCoTec 2013

作者： Cavalcante, Everton Batista, Thais Lopes, Frederico Almeida, André De Moura, Ana Lúcia Rodriguez, Noemi Alves, Gustavo Delicato, Flavia Pires, Paulo UFRN - Federal University of Rio Grande Do Norte Natal Brazil PUC-Rio - Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil UFRJ - Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9783642385407

Cloud-based applications are built from services offered by distinct third-party cloud providers. Most cloud-related information (service properties such as price, availability, response time, etc.) can change at any time during application execution. Therefore, it is essential to support the adaptation of applications in such dynamic conditions in order to ensure that the cloud services currently provided to deployed applications adhere to the established requirements. In this paper we present an autonomous adaptation process for cloud-based applications by replacing a service by an alternative one that fulfills the application needs. We discuss the factors that trigger an adaptation in a Cloud Computing scenario and describe the adaptation process within Cloud Integrator, a service-oriented middleware platform for composing, executing, and managing services provided by different cloud platforms. © 2013 IFIP international Federation for Information processing.

关键词： Cloud computing

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Colony: parallel Functions as a Service on the Cloud-Edge Continuum 27th

Colony: Parallel Functions as a Service on the Cloud-Edge Co...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Lordan, Francesc Lezzi, Daniele Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain

ISBN: (纸本)9783030856656;9783030856649

Although smart devices markets are increasing their sales figures, their computing capabilities are not sufficient to provide good-enough-quality services. This paper proposes a solution to organize the devices within the Cloud-Edge Continuum in such a way that each one, as an autonomous individual -Agent-, processes events/data on its embedded compute resources while offering its computing capacity to the rest of the infrastructure in a Function-as-a-Service manner. Unlike other FaaS solutions, the described approach proposes to transparently convert the logic of such functions into task-based workflows backing on task-based programming models;thus, agents hosting the execution of the method generate the corresponding workflow and offloading part of the workload onto other agents to improve the overall service performance. On our prototype, the function-to-workflow transformation is performed by COMPSs;thus, developers can efficiently code applications of any of the three envisaged computing scenarios - sense-process-actuate, streaming and batch processing - throughout the whole Cloud-Edge Continuum without struggling with different frameworks specifically designed for each of them.

关键词： Edge Fog Cloud Compute Continuum distributed systems Programming model Runtime system Serverless Function-as-a-Service Stream-processing Task-based workflow

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Exploiting Apache Flink's Iteration Capabilities for distributed Apriori: Community Detection Problem as an example 5

Exploiting Apache Flink's Iteration Capabilities for Distrib...

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international conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Rathee, Sanjay Kashyap, Arti IIT Sch Comp & Elect Engn Mandi Himachal Pardes India

ISBN: (纸本)9781509020287

Extraction of useful information from large datasets is one of the most important research problem. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exists many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these type of distributed applications, mapreduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software framework with mapreduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. But heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes hadoop inefficient. A number of map reduce based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted lot of attention because of their inbuilt support to distributed computations. Earlier we had proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, firstly because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, present work is a natural sequel of our earlier work and targets on implementing, testing and benchmarking Apriori on Apache Flink and compares it with Spark implementation. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori algorithm on Flink. We also use community detection graph mi

关键词： Apriori Apache Flink Mapreduce Spark Hadoop R-Apriori Frequent itemset mining

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