Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in c...
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Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs. Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification-based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP-based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang-scheduling-based, Makespan-based, and Max-Min-based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication-sensitive PADS applications, the advantage of ERPA is more significant.
simulation based education and training, especially wargame simulations, are being used widely in the field of defense modeling and in simulation communities. In order to efficiently train students and trainees, the w...
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simulation based education and training, especially wargame simulations, are being used widely in the field of defense modeling and in simulation communities. In order to efficiently train students and trainees, the wargame simulations must have both high performance and high fidelity In this paper, we discuss design and implementation issues for a prototype of a parallel and distributed wargame simulation system. This wargame simulation system is based on High Level Architecture (HLA) and employs some optimization to achieve both high performance and high fidelity in the simulation system. The results show that the proposed optimization method is effective when optimization is applied to 93.5% or less of the moving objects (PFs) within the range of detection (RofD) of both the red and blue teams. Specifically, when each team has 1000 PFs we found that if the percentage of PFs within RofD is less than 50% for both teams, our method is over two times better than for the situation where there is no optimization.
parallel and distributed simulation is a powerful tool for developing complex agent-based simulation. Complex simulations require parallel and distributed high performance computing solutions. It is necessary because ...
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parallel and distributed simulation is a powerful tool for developing complex agent-based simulation. Complex simulations require parallel and distributed high performance computing solutions. It is necessary because their sequential solutions are not able to give answers in a feasible total execution time. Therefore, for the advance of computing science, it is important that High Performance Computing (HPC) techniques and solutions be proposed and studied. In literature, we can find some agent-based modeling and simulation tools that use HPC. However, none of these tools are designed to enable the HPC expert to be able to propose new techniques and solutions without great effort. In this paper, we introduce Care High Performance simulation (HPS), which is a scientific instrument that enables researchers to: (1) develop techniques and solutions of high performance distributedsimulations for agent-based models;and, (2) study, design and implement complex agent-based models that require HPC solutions. Care HPS was designed to easily and quickly develop new agent-based models. It was also designed to extend and implement new solutions for the main issues of parallel and distributed solutions such as: synchronization, communication, load and computing balancing, and partitioning algorithms. We conducted some experiments with the aim of showing the completeness and functionality of Care HPS. As a result, we show that Care HPS can be used as a scientific instrument for the advance of the agent-based parallel and distributed simulations field. (C) 2016 Elsevier B.V. All rights reserved.
Data partitioning is one of the main problems in parallel and distributed simulation. Distribution of data over the architecture directly influences the efficiency of the simulation. The partitioning strategy becomes ...
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Data partitioning is one of the main problems in parallel and distributed simulation. Distribution of data over the architecture directly influences the efficiency of the simulation. The partitioning strategy becomes a complex problem because it depends on several factors. In an Individual-oriented Model, for example, the partitioning is related to interactions between the individual and the environment. Therefore, parallel and distributed simulation should dynamically enable the interchange of the partitioning strategy in order to choose the most appropriate partitioning strategy for a specific context. In this paper, we propose a strip partitioning strategy to a spatially dependent problem in Individual-oriented Model applications. This strategy avoids sharing resources, and, as a result, it decreases communication volume among the processes. In addition, we develop an objective function that calculates the best partitioning for a specific configuration and gives the computing cost of each partition, allowing for a computing balance through a mapping policy. The results obtained are supported by statistical analysis and experimentation with an Ant Colony application. As a main contribution, we developed a solution where the partitioning strategy can be chosen dynamically and always returns the lowest total execution time.
The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency ...
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The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.
Data partitioning is one of the main problems in parallel and distributed simulation. Distribution of data over the architecture directly influences the efficiency of the simulation. The partitioning strategy becomes ...
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Data partitioning is one of the main problems in parallel and distributed simulation. Distribution of data over the architecture directly influences the efficiency of the simulation. The partitioning strategy becomes a complex problem because it depends on several factors. In an Individual-oriented Model, for example, the partitioning is related to interactions between the individual and the environment. Therefore, parallel and distributed simulation should dynamically enable the interchange of the partitioning strategy in order to choose the most appropriate partitioning strategy for a specific context. In this paper, we propose a strip partitioning strategy to a spatially dependent problem in Individual-oriented Model applications. This strategy avoids sharing resources, and, as a result, it decreases communication volume among the processes. In addition, we develop an objective function that calculates the best partitioning for a specific configuration and gives the computing cost of each partition, allowing for a computing balance through a mapping policy. The results obtained are supported by statistical analysis and experimentation with an Ant Colony application. As a main contribution, we developed a solution where the partitioning strategy can be chosen dynamically and always returns the lowest total execution time.
A parallel and distributed simulation (federation) is composed of a number of simulation components (federates). Since the federates may be developed by different participants and executed on different platforms, they...
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A parallel and distributed simulation (federation) is composed of a number of simulation components (federates). Since the federates may be developed by different participants and executed on different platforms, they are subject to Byzantine failures. Moreover, the failure may propagate in the federation, resulting in epidemic effect. In this article, a three-phase (i.e., detection, location, and recovery) Byzantine Fault Tolerance (BFT) mechanism is proposed based on a transparent middleware approach. The replication, checkpointing and message logging techniques are integrated in the mechanism for the purpose of enhancing simulation performance and reducing fault tolerance cost. In addition, mechanisms are provided to remove the epidemic effects of Byzantine failures. Our experiments have verified the correctness of the three-phase BFT mechanism and illustrated its high efficiency and good scalability. For some simulation executions, the BFT mechanism may even achieve performance enhancement and Byzantine fault tolerance simultaneously. (C) 2015 Elsevier B.V. All rights reserved.
In the past, several time synchronization methods have been proposed for parallel and distributed simulation (PDS). Among them, one widely used conservative method is the Chandy-Misra-Bryant (CMB) algorithm. In the CM...
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ISBN:
(纸本)9781665435406
In the past, several time synchronization methods have been proposed for parallel and distributed simulation (PDS). Among them, one widely used conservative method is the Chandy-Misra-Bryant (CMB) algorithm. In the CMB algorithm, many null messages may be exchanged among logical processes to advance their clocks so that deadlock will not occur among them. In this work, using a data-plane programmable P4 hardware switch, we design and implement a data fusion-based approach inside the packet processing pipeline of the P4 switch. Our approach extracts the timestamp carried in exchanged null messages, computes the fusion results of these timestamps, drops unnecessary null messages inside the switch, generates new messages carrying the fusion results, and sends these generated messages to only the logical processes that can benefit from receiving these messages. Experimental results show that on an 8-host testbed, our approach can speed up a PDS by a factor of 2.75 and 1.65 when compared with the unicast and multicast approaches, respectively.
The master/worker (MW) paradigm can be used as an approach to parallel discrete event simulation (PDES) on metacomputing systems. MW PDES applications incur overheads not found in conventional PDES executions executin...
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The master/worker (MW) paradigm can be used as an approach to parallel discrete event simulation (PDES) on metacomputing systems. MW PDES applications incur overheads not found in conventional PDES executions executing on tightly coupled machines. We introduce four optimization techniques in MW PDES systems on public resource and desktop grid infrastructures. Work unit caching, pipelined state updates, expedited message delivery, and adaptive work unit scheduling mechanisms in the context of MW PDES are described. These optimizations provide significant performance benefits when used in tandem. We present results showing that an optimized MW PDES system using these techniques can exhibit performance comparable to a traditional PDES system for queueing network and particle physics simulation applications while providing execution capability across metacomputing systems.
This article advocates the use of a formal framework for analyzing simulation performance. simulation performance is characterized based on the three simulation development process boundaries: physical system, simulat...
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This article advocates the use of a formal framework for analyzing simulation performance. simulation performance is characterized based on the three simulation development process boundaries: physical system, simulation model, and simulator implementation. First, the authors formalize simulation event ordering using partially ordered set theory. A simulator implements a simulation event ordering and incurs implementation overheads when enforcing event ordering at runtime. Second, they apply their formalism to extract and formalize the simulation event orderings of both sequential and parallelsimulations. Third, they propose the relation stricter and a measure called strictness for comparing and quantifying the degree of event dependency of simulation event orderings, respectively. In contrast to the event parallelism measure, strictness is independent of time.
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