Dynamical behaviour of multi-particle large-scale systems

Collective behaviours can be observed in both natural and man-made systems composed of a large number of elemental subsystems. Typically, each elemental subsystem has its own dynamics but, whenever interaction between individuals occurs, the individual behaviours tend to be relaxed, and collective behaviours emerge. In this paper, the collective behaviour of a large-scale system composed of several coupled elemental particles is analysed. The dynamics of the particles are governed by the same type of equations but having different parameter values and initial conditions. Coupling between particles is based on statistical feedback, which means that each particle is affected by the average behaviour of its neighbours. It is shown that the global system may unveil several types of collective behaviours, corresponding to partial synchronisation, characterised by the existence of several clusters of synchronised subsystems, and global synchronisation between particles, where all the elemental particles synchronise completely.

System engineering; an introduction to the design of large-scale systems

Mode of access: Internet.

Architecture of Large-Scale Systems

In this paper various techniques in relation to large-scale systems are presented. At first, explanation of large-scale systems and differences from traditional systems are given. Next, possible specifications and requirements on hardware and software are listed. Finally, examples of large-scale systems are presented.

Large-scale dynamic systems : the proceedings of the Utah State University--Ames Research Center Seminar Workshop on Large-Scale Dynamic Systems, held at Utah State University, Logan, Utah, August 12-16, 1974 /

Includes bibliographical references

Fault tolerant decentralised K-Means clustering for asynchronous large-scale networks

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks, such as massively parallel processors and clusters of workstations. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. The lack of scalable and fault tolerant global communication and synchronisation methods in large-scale systems has hindered the adoption of the K-Means algorithm for applications in large networked systems such as wireless sensor networks, peer-to-peer systems and mobile ad hoc networks. This work proposes a fully distributed K-Means algorithm (EpidemicK-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art sampling methods and shows that the proposed method overcomes the limitations of the sampling-based approaches for skewed clusters distributions. The experimental analysis confirms that the proposed algorithm is very accurate and fault tolerant under unreliable network conditions (message loss and node failures) and is suitable for asynchronous networks of very large and extreme scale.

Efficient group communication for large-scale parallel clustering

Global communication requirements and load imbalance of some parallel data mining algorithms are the major obstacles to exploit the computational power of large-scale systems. This work investigates how non-uniform data distributions can be exploited to remove the global communication requirement and to reduce the communication cost in iterative parallel data mining algorithms. In particular, the analysis focuses on one of the most influential and popular data mining methods, the k-means algorithm for cluster analysis. The straightforward parallel formulation of the k-means algorithm requires a global reduction operation at each iteration step, which hinders its scalability. This work studies a different parallel formulation of the algorithm where the requirement of global communication can be relaxed while still providing the exact solution of the centralised k-means algorithm. The proposed approach exploits a non-uniform data distribution which can be either found in real world distributed applications or can be induced by means of multi-dimensional binary search trees. The approach can also be extended to accommodate an approximation error which allows a further reduction of the communication costs.

Planning and operation of large-scale water distribution systems with preemptive priorities

This paper describes the development of an optimization model for the management and operation of a large-scale, multireservoir water supply distribution system with preemptive priorities. The model considers multiobjectives and hedging rules. During periods of drought, when water supply is insufficient to meet the planned demand, appropriate rationing factors are applied to reduce water supply. In this paper, a water distribution system is formulated as a network and solved by the GAMS modeling system for mathematical programming and optimization. A user-friendly interface is developed to facilitate the manipulation of data and to generate graphs and tables for decision makers. The optimization model and its interface form a decision support system (DSS), which can be used to configure a water distribution system to facilitate capacity expansion and reliability studies. Several examples are presented to demonstrate the utility and versatility of the developed DSS under different supply and demand scenarios, including applications to one of the largest water supply systems in the world, the Sao Paulo Metropolitan Area Water Supply Distribution System in Brazil.

Localizing objects in large-scale cyber- physical systems

We use the term Cyber-Physical Systems to refer to large-scale distributed sensor systems. Locating the geographic coordinates of objects of interest is an important problemin such systems. We present a new distributed approach to localize objects and events of interest in time complexity independent of number of nodes.

Middleware for Large-scale Distributed Systems

Nos últimos anos o aumento exponencial da utilização de dispositivos móveis e serviços disponibilizados na “Cloud” levou a que a forma como os sistemas são desenhados e implementados mudasse, numa perspectiva de tentar alcançar requisitos que até então não eram essenciais. Analisando esta evolução, com o enorme aumento dos dispositivos móveis, como os “smartphones” e “tablets” fez com que o desenho e implementação de sistemas distribuidos fossem ainda mais importantes nesta área, na tentativa de promover sistemas e aplicações que fossem mais flexíveis, robutos, escaláveis e acima de tudo interoperáveis. A menor capacidade de processamento ou armazenamento destes dispositivos tornou essencial o aparecimento e crescimento de tecnologias que prometem solucionar muitos dos problemas identificados. O aparecimento do conceito de Middleware visa solucionar estas lacunas nos sistemas distribuidos mais evoluídos, promovendo uma solução a nível de organização e desenho da arquitetura dos sistemas, ao memo tempo que fornece comunicações extremamente rápidas, seguras e de confiança. Uma arquitetura baseada em Middleware visa dotar os sistemas de um canal de comunicação que fornece uma forte interoperabilidade, escalabilidade, e segurança na troca de mensagens, entre outras vantagens. Nesta tese vários tipos e exemplos de sistemas distribuídos e são descritos e analisados, assim como uma descrição em detalhe de três protocolos (XMPP, AMQP e DDS) de comunicação, sendo dois deles (XMPP e AMQP) utilzados em projecto reais que serão descritos ao longo desta tese. O principal objetivo da escrita desta tese é demonstrar o estudo e o levantamento do estado da arte relativamente ao conceito de Middleware aplicado a sistemas distribuídos de larga escala, provando que a utilização de um Middleware pode facilitar e agilizar o desenho e desenvolvimento de um sistema distribuído e traz enormes vantagens num futuro próximo.

Interpolatory methods for model reduction of large-scale dynamical systems

Magdeburg, Univ., Fak. für Mathematik, Diss., 2013

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Computational methods for model reduction of large-scale sparse structured descriptor systems

Magdeburg, Univ., Fak. für Mathematik, Diss., 2015

Sensitivity of large-scale atmospheric analyses to humidity observations and its impact on the global water cycle and tropical and extratropical weather systems in ERA40

Reanalysis data obtained from data assimilation are increasingly used for diagnostic studies of the general circulation of the atmosphere, for the validation of modelling experiments and for estimating energy and water fluxes between the Earth surface and the atmosphere. Because fluxes are not specifically observed, but determined by the data assimilation system, they are not only influenced by the utilized observations but also by model physics and dynamics and by the assimilation method. In order to better understand the relative importance of humidity observations for the determination of the hydrological cycle, in this paper we describe an assimilation experiment using the ERA40 reanalysis system where all humidity data have been excluded from the observational data base. The surprising result is that the model, driven by the time evolution of wind, temperature and surface pressure, is able to almost completely reconstitute the large-scale hydrological cycle of the control assimilation without the use of any humidity data. In addition, analysis of the individual weather systems in the extratropics and tropics using an objective feature tracking analysis indicates that the humidity data have very little impact on these systems. We include a discussion of these results and possible consequences for the way moisture information is assimilated, as well as the potential consequences for the design of observing systems for climate monitoring. It is further suggested, with support from a simple assimilation study with another model, that model physics and dynamics play a decisive role for the hydrological cycle, stressing the need to better understand these aspects of model parametrization. .