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Robust scalable stabilisability conditions for large-scale heterogeneous multi-agent systems with uncertain nonlinear interactions: towards a distributed computing architecture

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INTERNATIONAL JOURNAL OF CONTROL 2016年第6期89卷 1203-1213页

作者： Manfredi, Sabato Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy Univ London Imperial Coll Sci Technol & Med Elect & Elect Engn Dept Control & Power Grp London England

Large-scale dynamic systems are becoming highly pervasive in their occurrence with applications ranging from system biology, environment monitoring, sensor networks, and power systems. They are characterised by high dimensionality, complexity, and uncertainty in the node dynamic/interactions that require more and more computational demanding methods for their analysis and control design, as well as the network size and node system/interaction complexity increase. Therefore, it is a challenging problem to find scalable computational method for distributed control design of large-scale networks. In this paper, we investigate the robust distributed stabilisation problem of large-scale nonlinear multi-agent systems (briefly MASs) composed of non-identical (heterogeneous) linear dynamical systems coupled by uncertain nonlinear time-varying interconnections. By employing Lyapunov stability theory and linear matrix inequality (LMI) technique, new conditions are given for the distributed control design of large-scale MASs that can be easily solved by the toolbox of MATLAB. The stabilisability of each node dynamic is a sufficient assumption to design a global stabilising distributed control. The proposed approach improves some of the existing LMI-based results on MAS by both overcoming their computational limits and extending the applicative scenario to large-scale nonlinear heterogeneous MASs. Additionally, the proposed LMI conditions are further reduced in terms of computational requirement in the case of weakly heterogeneous MASs, which is a common scenario in real application where the network nodes and links are affected by parameter uncertainties. One of the main advantages of the proposed approach is to allow to move from a centralised towards a distributed computing architecture so that the expensive computation workload spent to solve LMIs may be shared among processors located at the networked nodes, thus increasing the scalability of the approach than the network

关键词： distributed computing linear matrix inequality Large-scale multi-agent system stabilisability

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Teaching distributed computing with WorkQueue (Abstract Only) 17

Teaching Distributed Computing with WorkQueue (Abstract Only...

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Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

作者： Aaron Dingler Peter Bui Seattle Pacific University Seattle WA USA University of Notre Dame Notre Dame IN USA

ISBN: (纸本)9781450346986

With the recent emphasis on Parallel and distributed computing topics in the Computer Science Curricula 2013, instructors are increasingly incorporating these topics into their undergraduate courses. Unfortunately, many universities lack the dedicated computing resources to provide hands-on experiences in this area. This workshop guides attendees through the open source WorkQueue software to teach parallel and distributed computing principles to undergraduate students. WorkQueue is a distributed master worker framework developed by the Cooperative computing Lab at the University of Notre Dame. WorkQueue is well-suited for inclusion in undergraduate courses due to the ease of use and deployment on a wide range of computer systems, low administrative overhead, and scalability. WorkQueue can be deployed on any system, from a small Raspberry Pi cluster to a high-performance grid computing environment. This workshop walks attendees through the use of WorkQueue with three demonstrations: a "live demo" such as would be used to engage students in the classroom with a hands-on introduction to distributed computing principles, and a guided "tour" through two lab assignments. The first lab assignment will give attendees a hands-on example of a simple distributed computing problem from implementation to deployment. The second lab will demonstrate WorkQueue MapReduce, a simple framework that can be used to introduce the MapReduce programming model without the overhead of a Hadoop cluster or equivalent. A laptop is required to participate in the workshop; the presenters will provide a pre-configured Linux VirtualBox virtual machine to facilitate software setup, or attendees can use their own Linux installations.

关键词： parallel computing MapReduce WorkQueue distributed computing

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I Can Has Supercomputer? A Novel Approach to Teaching Parallel and distributed computing Concepts Using a Meme-Based Programming Language

I Can Has Supercomputer? A Novel Approach to Teaching Parall...

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IEEE International Symposium on Parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David A Richie James A Ross Brown Deer Technology Forest Hill MD U.S. Army Research Laboratory Aberdeen Proving Ground MD

A novel approach is presented to teach the parallel and distributed computing concepts of synchronization and remote memory access. The single program multiple data (SPMD) partitioned global address space (PGAS) model presented in this paper uses a procedural programming language appealing to undergraduate students. We propose that the amusing nature of the approach may engender creativity and interest using these concepts later in more sober environments. Specifically, we implement parallel extensions to LOLCODE within a source-to-source compiler sufficient for the development of parallel and distributed algorithms normally implemented using conventional high-performance computing languages and APIs.

关键词： Reactive power Syntactics Programming distributed computing Computer languages Electronics packaging Standards

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A distributed Edge computing Architecture to Support Sensing and Detecting Leaks in Waterworks Based on Advanced FDM

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IEEE SENSORS JOURNAL 2017年第23期17卷 7820-7827页

作者： Lay-Ekuakille, Aime Griffo, Giuseppe Fazio, Maria Villari, Massimo Univ Salento Dept Innovat Engn I-73100 Lecce Italy Univ Messina Dept Engn I-98122 Messina Italy

High costs of urban services, namely, waterworks, transportation, waste collection, wastewater collection and treatment, energy, and public lighting, require their optimization in management. This optimization can be mostly achieved using dedicated technology and strategy by "building" smart cities and smart grids. This paper illustrates findings related to the application of a designed distributed edge computing system for supervising a network of sensors, located on a special configuration of a pipeline, to detect leaks. The plant to be supervised is a zigzag waterworks with leaks to be simulated by opening and closing taps. The pressure variation is detected by magnetic sensors, which convert pressure variation into electric signal to be processed on-line thanks to an advanced and robust algorithm called a filter diagonalization method that performs a spectral analysis. In this paper, we have also developed a 2-D representation of the leak within the pipeline or waterworks, which is a robust way to see the dimensions or the expansion of the leak in a specific space.

关键词： Magnetic sensors leaks detection health pipeline monitoring filter diagonalization method smart cities smart grids distributed computing edge computing environmental measurements

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Pladipus Enables Universal distributed computing in Proteomics Bioinformatics

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JOURNAL OF PROTEOME RESEARCH 2016年第3期15卷 707-712页

作者： Verheggen, Kenneth Maddelein, Davy Hulstaert, Niels Martens, Lennart Barsnes, Harald Vandel, Marc VIB Ctr Med Biotechnol Albert Baertsoenkaai 3 B-9000 Ghent Belgium Univ Ghent Dept Biochem Albert Baertsoenkaai 3 B-9000 Ghent Belgium Univ Ghent Bioinformat Inst Ghent Albert Baertsoenkaai 3 B-9000 Ghent Belgium Univ Bergen Dept Biomed Prote Unit Postboks 7804 N-5020 Bergen Norway Univ Bergen Dept Clin Sci KG Jebsen Ctr Diabet Res Postboks 7804 N-5020 Bergen Norway

The use of proteomics bioinformatics substantially contributes to an improved understanding of proteomes, but this novel and in-depth knowledge comes at the cost of increased computational complexity. Parallelization across multiple computers, a strategy termed distributed computing, can be used to handle this increased complexity;however, setting up and maintaining a distributed computing infrastructure requires resources and skills that are not readily available to most research groups. Here we propose a free and open -source framework named Pladipus that greatly facilitates the establishment of distributed computing networks for proteomics bioinformatics tools. Pladipus is straightforward to install and operate thanks to its user-friendly graphical interface, allowing complex bioinformatics tasks to be run easily on a network instead of a single computer. As a result, any researcher can benefit from the increased computational efficiency provided by distributed computing, hence empowering them to tackle more complex bioinformatics challenges. Notably, it enables any research group to perform large-scale reprocessing of publicly available proteomics data, thus supporting the scientific community in mining these data for novel discoveries.

关键词： distributed computing large scale data processing cluster computing parallelization

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Coding for edge-facilitated wireless distributed computing with heterogeneous users

Coding for edge-facilitated wireless distributed computing w...

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Asilomar Conference on Signals, Systems & Computers

作者： Mehrdad Kiamari Chenwei Wang A. Salman Avestimehr Department of Electrical Engineering University of Southern California Los Angeles CA DOCOMO Innovations Inc Palo Alto CA

We consider the edge-facilitated wireless distributed computing with three users, each communicating with the other two users via the help of an access point (AP) only. Motivated by the idea of MapReduce-based wireless coded distributed computing (CDC) proposed by Li et al. in [1] for homogeneous systems where each user's file storage size is identical, we attempt to extend MapReduce-based CDC to heterogeneous systems where each user has arbitrary file storage size. In this paper, we provide an achievable communication load region of the uplink-downlink transmission pair by appropriately designing file placement over the users and developing coding schemes at both the users and the AP. Moreover, we provide the converse for the communication load region, by decoupling the uplink and downlink transmissions. Finally, we provide a discussion on the shape of the load region for several examples and observations.

关键词： Encoding distributed computing Wireless communication Computational modeling Task analysis Uplink Downlink

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Bottom-Up Norm Creation in Open distributed computing Grids by Means of eXtended Classifier Systems

Bottom-Up Norm Creation in Open Distributed Computing Grids ...

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ARCS 2017;30th International Conference on Architecture of computing Systems

作者： Sarah Edenhofer Youssef Madkour Anthony Stein Christopher Stifter Joerg Haehner Organic Computing Group University of Augsburg

Open, distributed multi-agent systems with heterogeneous agent societies need to be robust against malicious agents in the system. These malicious agents can show complex behaviours to manipulate or exploit the system. Trust mechanisms are a popular method to improve the performance and robustness of such systems. Yet, these mechanisms leave room for improvement. In this paper, we present a norm approach to enhance the robustness and, in consequence, the performance of the open multi-agent system. We introduce a concept, where norms are created bottom-up by the agents with the help of an eXtended Classifier System learning mechanism. We evaluate our approach within the simulation of a trust-based desktop computing grid and different agent stereotypes.

关键词： Multi-agent systems Agents Economic Resources bottom-up Classifiers Robustness distributed computing grids Grates

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BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments

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BIOINFORMATICS 2016年第5期32卷 798-800页

作者： Thomas, Brandon R. Chylek, Lily A. Colvin, Joshua Sirimulla, Suman Clayton, Andrew H. A. Hlavacek, William S. Posner, Richard G. No Arizona Univ Dept Biol Sci Box 5640 Flagstaff AZ 86011 USA Cornell Univ Dept Chem & Chem Biol Ithaca NY USA St Louis Coll Pharm Dept Basic Sci St Louis MO USA Swinburne Univ Technol Fac Sci Engn & Technol Cell Biophys Lab Ctr Microphoton Hawthorn Vic 3122 Australia Los Alamos Natl Lab Div Theoret Los Alamos NM USA

Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive.

关键词： distributed computing Computer networks distributed sensors Cyberinfrastructure Computer systems

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Flexible distributed heterogeneous computing in traffic noise mapping

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2017年第Sep.期65卷 1-14页

作者： Li, Nan Feng, Tao Wu, Rui Beijing Technol & Business Univ Sch Mat & Mech Engn 11 FuCheng Rd Beijing 10048 Peoples R China State Environm Protect Engn Ctr Urban Noise & Vib Beijing Peoples R China

In China, fast city rebuilding poses the challenge of frequent refresh cycle of urban traffic noise mapping. Computational complexity and lack of resources are the primary bottleneck in traffic noise mapping. In this paper, we present a flexible distributed heterogeneous computing method based on GPU-CPU cooperation, which reduces the overhead, improves the efficiency of parallel computing and consistently generates good quality results for traffic noise mapping. A genetic algorithm based large-scale task partition algorithm is employed to solve load balancing problem in distributed noise mapping calculation. The methodology is evaluated by an example, whose results show that the proposed task partition method can significantly improve running efficiency. Parallel efficiency increases from 54% to 78%. In addition, test speed is further improved by 21% with the GPU-CPU collaborative computing, even with only low-end type GPUs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Noise mapping Environmental noise distributed computing Noise prediction GPU Genetic algorithm

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Tasklets: Overcoming Heterogeneity in distributed computing Systems 36

Tasklets: Overcoming Heterogeneity in Distributed Computing ...

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36th IEEE International Conference on distributed computing Systems (ICDCS)

作者： Schaefer, Dominik Edinger, Janick VanSyckel, Sebastian Becker, Christian Paluska, Justin Mazzola Univ Mannheim Mannheim Germany MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781509014828

distributed computing is a good alternative to expensive supercomputers. There are plenty of frameworks that enable programmers to harvest remote computing power. However, until today, much computation power in the edges of the Internet remains unused. While idle devices could contribute to a distributed environment as generic computation resources, computation-intense applications could use this pool of resources to enhance their execution quality. In this paper, we identify heterogeneity as a major burden for distributed and edge computing. Heterogeneity is present in multiple forms. We draw our vision of a comprehensive distributed computing system and show where existing frameworks fall short in dealing with the heterogeneity of distributed computing. Afterwards, we present the Tasklet system, our approach for a distributed computing framework. Tasklets are fine-grained computation units that can be issued for remote and local execution. We tackle the different dimensions of heterogeneity and show how to make use of available computation power in edge resources. In our prototype, we use middleware and virtualization technologies as well as a host language concept.

关键词： distributed computing Edge-centric computing Heterogeneity Resource Sharing Grid computing

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