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International Symposium on Object-Oriented real-time distributed Computing

作者： Catherine Menon Saverio Iacovelli Raimund Kirner Department of Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9781728169583

ISBN: (纸本)9781728169590

In a safety-critical system each service has a specific level of safety criticality. Safety standards use classifications like Safety Integrity Levels (SIL), to describe the design requirements for the individual services of a system. Techniques like redundancy can be used to achieve a higher overall dependability than the used individual components provide. Using the notion of SIL, this can be called SIL *** this paper we describe the concept of SIL arithmetic and point out how different safety standards provide hints for their support of using SIL arithmetic. We highlight the principal benefits of SIL arithmetic and provide simple examples. But the use of SIL arithmetic in a concrete system design can also have its pitfalls, which we also discuss in this paper. We specifically discuss these issues in the context of scheduling techniques for mixed-criticality systems, where resource shortages are to be handled by the scheduler.

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On Verification of Designed Energy systems Using distributed Co-Simulations

On Verification of Designed Energy Systems Using Distributed...

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International workshop/Symposium on distributed Simulation and real-time Applications (DS-RT)

作者： Anselm Erdmann Anna Marcellan Dominik Hering Michael Suriyah Carolin Ulbrich Martin Henke André Xhonneux Dirk Müller Rutger Schlatmann Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Institute of Combustion Technology German Aerospace Center Stuttgart Germany Institute of Energy and Climate Research Energy Systems Engineering (IEK-10) Forschungszentrum Jülich GmbH Jülich Germany Institute of Electric Energy Systems and High-Voltage Technology Karlsruhe Institute of Technology Karlsruhe Germany PVcomB Helmholtz-Zentrum Berlin für Materialien und Energie GmbH Berlin Germany

ISBN: (数字)9781728173436

ISBN: (纸本)9781728173443

An essential part of the energy systems design procedure is simulation, since it serves as a tool for verification of the respective design. It serves the verifying of a stable operation of developed energy systems infrastructure, before it comes to the realization. As energy systems integration becomes an important part in a low carbon energy scenario in the future, the cooperation of experts specialized in various domains crucial to single aspects of the energy system is indispensable. Cosimulation, yet, enables the modelling in the familiar environment of the experts, but requires a detailed coordination of the simulation interfaces between the specific expert models. Hence, standardized interfaces are crucial to the efficient use of expert knowledge in distributed co-simulations. Therefore, in the presented paper a workflow for the co-simulation development of energy systems simulations, which simplifies the coordination procedure significantly by standardizing the interfaces between the models and their simulations, is introduced. The approach is exemplarily applied to the energy system design of a district comprising electricity and heat in order to show its successful performance.

关键词： Adaptation models Tools Mathematical model System analysis and design Data models Standards

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Design and implementation of reconfigurable acceleration for in-memory distributed big data computing

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FUTURE GENERATION COMPUTER systems-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 68-75页

作者： Hou, Junjie Zhu, Yongxin Du, Sen Song, Shijin Shanghai Jiao Tong Univ Sch Microelect Shanghai 200240 Peoples R China Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Apache Spark is an efficient distributed computing framework for big data processing. It supports in memory computation of RDDs (Resilient distributed Datasets) and provides a provision of reusability, fault tolerance, and real-time stream processing. However, the tasks in Spark framework are only performed on CPU. The low degree of parallelism and power inefficiency of CPU may restrict the performance and scalability of the cluster. In order to improve the performance and power dissipation of the data center, heterogeneous accelerators such as FPGA, GPU, MIC (Many Integrated Core) exhibit more efficient performance than general-purpose processors in big data processing. In this work, we propose a framework to integrate FPGA accelerators into a Spark cluster, which achieves performance improvement and power dissipation reduction for distributed applications. We propose a method for connecting Spark with OpenCL application which is a standard for cross-platform, parallel programming of diverse processors and widely used in heterogeneous computing, and use FPGA to accelerate the Spark tasks developed with Python. We illustrate the performance and the energy efficiency of FPGA based Spark framework with a case study of K-means algorithm acceleration. The results show that FPGA based Spark implementation achieves 3.5x speedup and 4.06x energy efficiency over original Spark framework. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Spark distributed computing Python FPGA OpenCL High performance Energy efficiency

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real-time Model Predictive Control for a parallel Hybrid Electric Vehicle using Outer Approximation and Semi-Convex Cut Generation**This work has been carried out within the framework of KU Leuven - BOF PFV/10/002 Centre of Excellence: Optimization in Engineering (OPTEC) and DrivetrainCodesign: Flanders Make ICON: Physical and control co-design of electromechanical drivetrains for machines and vehicles. Flanders Make is the Flemish strategic research centre for the manufacturing industry.

Real-Time Model Predictive Control for a Parallel Hybrid Ele...

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International workshop on Advanced Motion Control (AMC)

作者： Massimo De Mauri Joris Gillis Goele Pipeleers Jan Swevers KU Leuven and DMMS lab Flanders Make Leuven Belgium

ISBN: (数字)9781728131894

ISBN: (纸本)9781728131900

Hybrid Electric Vehicles (HEVs) have the potential of yielding better fuel economy and lower emissions compared to their traditional counterparts. This advantage comes with the drawback that drivers are no longer capable of efficiently controlling the power-train of their vehicles. Consequently, good automatic control is crucial for HEV design. In this paper the full development of the power-train control system of a parallel-HEV is presented. The proposed control approach is Mixed-Integer Model Predictive Control (MI-MPC). A suitable MI-MPC formulation is developed starting from the modeling of the propulsion components of the power-train based on a set of real-world data. After guiding the reader through the development of the model. An innovative solution approach is presented to deal with the inherit non-convexity of the resulting problem formulation. The peculiar form of non-convexity in the model is harnessed in order to develop a variation of the Outer Approximation algorithm capable of efficiently tackling the solution of the mixed-integer optimization problems required by the MI-MPC framework. In conclusion, a numerical experiment will show the real-time applicability of the resulting control system and highlight its limits.

关键词： Control systems Prediction algorithms real-time systems Numerical models Hybrid electric vehicles Optimization Predictive control

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Optimization Methods for Computing System in Mobile CPS 19

Optimization Methods for Computing System in Mobile CPS

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2nd International Conference on Big Data Technologies (ICBDT) / 3rd International Conference on Business Information systems workshop (ICBIS)

作者： Zhang, Yang Xing, Zuocheng Wang, Yu Chen, Lirui Wang, Qinglin Zhu, Yufei Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781450371926

In Mobile Cyber-physical system (CPS) is a popular research field in recent years. It aims to control and monitor mobile devices in complex and real-time scenes, and provide people with convenience and economy by using intelligent applications. The scenes in mobile CPS have close relationships with everyone's life and it has pervasive effect on people's life anytime and anywhere. In the paper, we focus on the intelligent transportation systems (ITS) in mobile CPS. Specifically, we analyse the bottlenecks in automated and autonomous vehicles and find the on-car computer is the key component for low latency and low power self-driving vehicles. We show detailed research developments on the most advanced CPU-GPU platform including four aspects, mainly putting focus on the methods to reduce latency and energy in the system. The four aspects cover reducing data transferring overhead, reducing power consumption with power-gating, optimizing warp scheduling scheme and optimizing cache performance in coprocessors. Finally, we give the prospects and development trends in the computing platform of self-driving on-car system.

关键词： CPS Intelligent Transportation systems CPU-GPU platform

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Large-Scale Dynamic Graph Updating Algorithm in distributed Computing System 19

Large-Scale Dynamic Graph Updating Algorithm in Distributed ...

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2nd International Conference on Big Data Technologies (ICBDT) / 3rd International Conference on Business Information systems workshop (ICBIS)

作者： Rong Xuanyu Cui Huanqing Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781450371926

distributed graph computing technology for processing large-scale graph data has been widely used in social network, communication network and so on. The foundation of distributed graph computing is reasonable partitioning of large-scale graph in distributed system. Most proposed graph partitioning algorithms cannot achieve the goals of load balance and minimizing the number of edge-cuts at the same time. This paper constructs a cost function to measure the efficiency of partitioning large-scale graphs in a distributed system, where the graph is dynamically updated in real time. Based on the cost function, the update algorithm is proposed for the addition of vertex and edge. Experimental results show that the proposed algorithm can yield significant reduction in load imbalance and number of edge-cuts.

关键词： Graph computing Dynamic graph Graph partitioning

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Two-dimensional indexing to provide one-integrated-memory view of distributed memory for a massively-parallel search engine (vol 22, pg 2437, 2018)

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION systems 2019年第6期22卷 2469-2470页

作者： Yun, Tae-Seob Whang, Kyu-Young Kwon, Hyuk-Yoon Kim, Jun-Sung Song, Il-Yeol Korea Adv Inst Sci & Technol Dept Comp Sci Daejeon South Korea Seoul Natl Univ Sci & Technol Dept Global Fus Ind Engn Seoul South Korea Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA

We propose two-dimensional indexing—a novel in-memory indexing architecture that operates over distributed memory of a massively-parallel search engine. The goal of two-dimensional indexing is to provide a one-integrated-memory view as in a single node system using one large integrated memory. In two-dimensional indexing, we partition the entire index into n× m fragments and distribute them over the memories of multiple nodes in such a way that each fragment is entirely stored in main memory of one node. The proposed architecture is not only scalable as it uses a scaled-out shared-nothing architecture but also is capable of achieving low query response time as it processes queries in main memory. We also propose the concept of the one-memory point, which is the amount of the memory space required to completely store the entire index in main memory providing a one-integrated-memory view. We first prove the effectiveness of two-dimensional indexing with single-keyword queries, and then, extend the notion so as to be able to handle multiple-keyword queries. To handle multiple-keyword queries, we adopt pre-join that materializes a multiple-keyword query a priori as well as a new notion of semi-memory join that obviates extensive communication overhead to perform join across multiple nodes. In experiments using the real-life search query set over a database consisting of 100 million Web documents crawled, we show that two-dimensional indexing can effectively provide a one-integrated-memory view without too much of additional memory compared with the single node system using one large integrated memory. We also show that, with a six-node prototype, in an ideal case, it significantly improves the query processing performance over a disk-based search engine with an equivalent amount of in-memory buffer but without two-dimensional indexing — by up to 535.54 times. This improvement is expected to get larger as the system is scaled-out with a larger number of machines.

关键词： Massively-parallel search engine DB-IR integration Pre-join Multiple-keyword search queries distributed memory

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Emergent Scheduling of distributed Execution Frameworks 4

Emergent Scheduling of Distributed Execution Frameworks

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IEEE 4th International workshops on Foundations and Applications of Self* systems (FAS*W)

作者： Dean, Paul Allan Porter, Barry Univ Lancaster Sch Comp & Commun Lancaster England

ISBN: (纸本)9781728124063

distributed execution Frameworks (DEFs) provide a platform for handling the increasing volume of data available to distributed computational processes, forming the creation and usage of a large number of DEFs for performing distributed computations. For example, sorting and analyzing large data sets through map and reduce operations, performing a set of operations across points in a data stream to provide near real-time analysis, and the training and testing of machine learning models for varying methods of learning, such as, supervised, unsupervised and reinforcement learning, exploiting the vast amounts of data available. Leading to varying DEFs becoming optimal for either fine or coarse grained computations, for example Apache Spark provides a framework for coarse grained data parallel processes providing data locality adding latency to scheduling decisions which would hinder performance of fine-grained computation. Whereas Ray and Apache Flink provide solutions to avoid the latency incurred by the scheduling method used by apache Spark while potentially incurring longer job completion times as data locality is no longer a priority. Therefore, this PhD will focus on overcoming the issue of trading performance for differing workloads by exploiting the capabilities presented by emergent software systems which learn how to assemble and re-assemble themselves in response to their current deployment conditions and input pattern. This allows the creation of a component based DEF capable of altering both the local behaviour of a DEF (i.e. Local Schedulers and placement polices within a centralised scheduler) to potentially improve the performance of single DEF as well as global behaviour of a DEF, for example the adaptation of a centralised to two-level scheduler.

关键词： Reinforcement learning Task analysis Cluster computing Processor scheduling real-time systems Training

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Advances in OpenDSS smart inverter modelling for quasi-static time-series simulations

Advances in OpenDSS smart inverter modelling for quasi-stati...

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CIRED 2020 Berlin workshop (CIRED 2020)

作者： Paulo Radatz Celso H.S. Rocha Jouni Peppanen Matthew Rylander Electric Power Research Institute Palo Alto California USA

The fleet of smart distributed energy resource (DER) inverters is expected to grow rapidly as a result of growing DER penetration levels and requirements in interconnection standards/rules such as IEEE Std 1547-2018. Smart inverters have the potential to help to maintain and/or enhance grid safety, reliability and customer affordability. Advanced modelling and analytical capabilities are required from distribution planning tools to understand the full potential of smart inverters. This study presents enhanced smart inverter modelling and simulation features included in a recent release of the open-source distribution system modelling and simulation software suite OpenDSS. The enhanced features include detailed modelling of the inverter capability curve along with significant improvements to the simulation speed and convergence. The enhanced features make it more practical to perform long quasi-static time-series load flow simulations with thousands of time steps on large-scale utility feeder models with up to thousands of autonomously controlled smart inverters. Some of the enhanced speed, convergence and other features are demonstrated on a large-scale real U.S. utility feeder model with >1100 solar photovoltaic systems.

关键词： invertors power system simulation smart power grids time series distributed power generation power system interconnection IEEE standards power distribution planning load flow photovoltaic power systems

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DistStream: An Order-Aware distributed Framework for Online-Offline Stream Clustering Algorithms

DistStream: An Order-Aware Distributed Framework for Online-...

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International Conference on distributed Computing systems

作者： Lijie Xu Xingtong Ye Kai Kang Tian Guo Wensheng Dou Wei Wang Jun Wei State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Worcester Polytechnic Institute Institute of Software Technology Chinese Academy of Sciences Nanjing

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Stream clustering is an important data mining technique to capture the evolving patterns in real-time data streams. Today's data streams, e.g., IoT events and Web clicks, are usually high-speed and contain dynamically-changing patterns. Existing stream clustering algorithms usually follow an online-offline paradigm with a one-record-at-a-time update model, which was designed for running in a single machine. These stream clustering algorithms, with this sequential update model, cannot be efficiently parallelized and fail to deliver the required high throughput for stream clustering. In this paper, we present DistStream, a distributed framework that can effectively scale out online-offline stream clustering algorithms. To parallelize these algorithms for high throughput, we develop a mini-batch update model with efficient parallelization approaches. To maintain high clustering quality, DistStream's mini-batch update model preserves the update order in all the computation steps during parallel execution, which can reflect the recent changes for dynamically-changing streaming data. We implement DistStream atop Spark Streaming, as well as four representative stream clustering algorithms based on DistStream. Our evaluation on three real-world datasets shows that DistStream-based stream clustering algorithms can achieve sublinear throughput gain and comparable (99%) clustering quality with their single-machine counterparts.

关键词： Heuristic algorithms Computational modeling Clustering algorithms distributed databases Throughput real-time systems Sparks

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