distributed computing is one of the paradigms in the world of information technology. Middleware is the essential tool for implementing distributed computing for overtaking the heterogeneity of platform and language. ...
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distributed computing is one of the paradigms in the world of information technology. Middleware is the essential tool for implementing distributed computing for overtaking the heterogeneity of platform and language. DRDO's intranet, DRONA, has the potential of hosting distributed applications across the network. This paper deals with the essentials of distributed computing, architecture of DRONA network, and the scope of distributed computing in Defence applications. It also suggests a few possible applications of distributed computing.
We study the problem of the amount of information (advice) about a graph that must be given to its nodes in order to achieve fast distributed computations. The required size of the advice enables to measure the inform...
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We study the problem of the amount of information (advice) about a graph that must be given to its nodes in order to achieve fast distributed computations. The required size of the advice enables to measure the information sensitivity of a network problem. A problem is information sensitive if little advice is enough to solve the problem rapidly (i.e., much faster than in the absence of any advice), whereas it is information insensitive if it requires giving a lot of information to the nodes in order to ensure fast computation of the solution. In this paper, we study the information sensitivity of distributed graph coloring.
Applications of probit-based stochastic user equilibrium (SUE) principle on large-scale networks have been largely limited because of the overwhelming computational burden in solving its stochastic network loading pro...
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Applications of probit-based stochastic user equilibrium (SUE) principle on large-scale networks have been largely limited because of the overwhelming computational burden in solving its stochastic network loading problem. A two-stage Monte Carlo simulation method is recognized to have satisfactory accuracy level when solving this stochastic network loading. This paper thus works on the acceleration of the Monte Carlo simulation method via using distributed computing system. Three distributed computing approaches are then adopted on the workload partition of the Monte Carlo simulation method. Wherein, the first approach allocates each processor in the distributed computing system to solve each trial of the simulation in parallel and in turns, and the second approach assigns all the processors to solve the shortest-path problems in one trial of the Monte Carlo simulation concurrently. The third approach is a combination of the first two, wherein both different trials of the Monte Carlo simulation as well as the shortest path problems in one trial are solved simultaneously. Performances of the three approaches are comprehensively tested by the Sioux-Falls network and then a randomly generated network example. It shows that computational time for the probit-based SUE problem can be largely reduced by any of these three approaches, and the first approach is found out to be superior to the other two. The first approach is then selected to calculate the probit-based SUE problem on a large-scale network example. Copyright (c) 2011 John Wiley & Sons, Ltd.
The paper studies three fundamental problems in graph analytics, computing connected components (CCs), biconnected components (BCCs), and 2-edge-connected components (ECCs) of a graph. With the recent advent of big da...
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The paper studies three fundamental problems in graph analytics, computing connected components (CCs), biconnected components (BCCs), and 2-edge-connected components (ECCs) of a graph. With the recent advent of big data, developing efficient distributed algorithms for computing CCs, BCCs and ECCs of a big graph has received increasing interests. As with the existing research efforts, we focus on the Pregel programming model, while the techniques may be extended to other programming models including MapReduce and Spark. The state-of-the-art techniques for computing CCs and BCCs in Pregel incur O(m x #supersteps) total costs for both data communication and computation, where m is the number of edges in a graph and #supersteps is the number of supersteps. Since the network communication speed is usually much slower than the computation speed, communication costs are the dominant costs of the total running time in the existing techniques. In this paper, we propose a new paradigm based on graph decomposition to compute CCs and BCCs with O(m) total communication cost. The total computation costs of our techniques are also smaller than that of the existing techniques in practice, though theoretically almost the same. Moreover, we also study distributed computing ECCs. We are the first to study this problem and an approach with O(m) total communication cost is proposed. Comprehensive empirical studies demonstrate that our approaches can outperform the existing techniques by one order of magnitude regarding the total running time.
Most existing distributed systems are structured as statically compiled processes communicating with each other via messages. The system's ''intelligence'' is embodied in the processes, while the m...
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Most existing distributed systems are structured as statically compiled processes communicating with each other via messages. The system's ''intelligence'' is embodied in the processes, while the messages contain simple, passive pieces information (the communicating objects paradigm). In the autonomous objects paradigm, a message has its own identity and behavior. It decides at runtime where it wants to propagate and what tasks to perform there;the nodes become simply generic interpreters that enable messages to navigate and compute. In this scenario, an application's ''intelligence'' is embodied in and carried by messages as they propagate through the network, much as human agent or a robot would move in space, visiting different locales as it performs tasks. The autonomous objects paradigm is more flexible than the communicating objects paradigm because it allows developers to change the program's behavior after it has started to run. We based our system, MESSENGERS, on autonomous objects, and intended it for the composition and coordination of concurrent activities in a distributed environment. It combines powerful navigational capabilities found in other autonomous objects-based systems with efficient dynamic linking mechanisms supported by some new programming languages, like Java. MESSENGERS allows the dynamic construction of arbitrarily complex control sequences, which are carried through the network. The sequences can invoke node-resident computational programs and coordinate their operation by carrying information among them.
Questions of effective parallel realization for some algorithms of the Monte Carlo method are discussed. Parallel modification of the generator of basic pseudorandom numbers uniformly distributed in the unit interval ...
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Questions of effective parallel realization for some algorithms of the Monte Carlo method are discussed. Parallel modification of the generator of basic pseudorandom numbers uniformly distributed in the unit interval is described. The technique of distributed computing in the personal computer network with the use of the MONC program system worked out by the authors is described.
Modem digital material approaches for the simulation and visualization of heterogeneous materials allow to investigate the behavior of complex multiphase materials with their physical nonlinear material response at va...
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Modem digital material approaches for the simulation and visualization of heterogeneous materials allow to investigate the behavior of complex multiphase materials with their physical nonlinear material response at various scales. However, these computational techniques require extensive hardware resources with respect to computing power and main memory to solve numerically large-scale discretized models in 3D. Due to a very high number of degrees of freedom, which may rapidly be increased to several 10 million degrees of freedom, the limited hardware resources are to be utilized in a most efficient way to enable an execution of the numerical algorithms in minimal computation time. The computational efficiency and the distribution among available hardware resources (often based on a parallel hardware architecture) can significantly be improved. In the past years, high-performance computing (HPC) based computation techniques were established for the investigation of scientific objectives. Their application results in the modification of existing and the development of new computational methods for the numerical implementation, which enables to take advantage of massively clustered computer hardware resources. Due to the high numerical effort for such simulations, an alternative approach for the nonlinear finite element analysis, based on the sequential linear analysis, was implemented in respect to scalable HPC. The incremental-iterative procedure in finite element analysis (FEA) during the nonlinear step was then replaced by a sequence of linear FE analysis, known in literature as saw-tooth approach. As a result, qualitative (smeared) crack initiation in 3D multiphase specimens has efficiently been simulated. (C) 2013 Elsevier Ltd. All rights reserved.
Low carbon footprint energy sources such as solar and wind power typically suffer from unpredictable or limited availability. By globally distributing a number of these renewable sources, these effects can largely be ...
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Low carbon footprint energy sources such as solar and wind power typically suffer from unpredictable or limited availability. By globally distributing a number of these renewable sources, these effects can largely be compensated for. We look at the feasibility of this approach for powering already distributed data centers in order to operate at a reduced total carbon footprint. From our study we show that carbon footprint reductions are Possible, but that these are highly dependent on the approach and parameters involved. Especially the manufacturing footprint and the geographical region are critical parameters to consider. Deploying additional data centers can help in reducing the total carbon footprint, but substantial reductions can be achieved when data centers with nominal capacity well below maximum capacity redistribute processing to sites based on renewable energy availability. (C) 2011 Elsevier B.V. All rights reserved.
distributed computation consists of local computations and their interactions. We consider a molecular robot system as a distributed system composed of mobile computing entities with very weak capabilities, i.e., comp...
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distributed computation consists of local computations and their interactions. We consider a molecular robot system as a distributed system composed of mobile computing entities with very weak capabilities, i.e., computing entities are anonymous (indistinguishable), oblivious (memory-less), and uniform (following a common local computation rule). The key property of such a distributed system is self-organization. In this survey, we first introduce shape formation by mobile computing entities and present characterizations of formable shapes. We then consider global behavior realized by shapes of a distributed system. We demonstrate general computational power of mobile computing entities in terms of computing languages and predicates. Finally, we demonstrate dynamic behavior, such as locomotion and search, realized by a sequence of shapes.
Given the ubiquity of Web browsers and the performance gains being achieved by javaScript virtual machines, a question arises: Could Internet browsers become yet another middleware for distributed computing? With 2 bi...
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Given the ubiquity of Web browsers and the performance gains being achieved by javaScript virtual machines, a question arises: Could Internet browsers become yet another middleware for distributed computing? With 2 billion users online, computing through Internet browsers could amass immense resources and transform the Internet into a distributed computer ideal for common classes of distributed scientific applications such as parametric studies. The proposed WeevilScout prototype framework builds on this idea, and offers results from a proof-of-concept cluster of globally distributed Internet browsers that computed thousands of bio-informatics tasks.
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