Hybrid modelling and simulation of huge crowd over a hierarchical Grid architecture

Chen, Dan; Wang, Lizhe; Wu, Xiaomin; Chen, Jingying; Khan, Samee U.; Kołodziej, Joanna; Tian, Mingwei; Huang, Fang; Liu, Wangyang

doi:10.1016/j.future.2012.03.006

Cited by 48 publications

(24 citation statements)

References 29 publications

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“…The density of a cell k is denoted by ρk, which can be calculated by formula 2. (2) where N is the total number of cells around cell k within a given scope (e.g., a circle with radius of 5 in our experiments), Nk is the number of agents in these cells, and a is the edge length of a cell. Fig.…”

Section: Density Fieldmentioning

confidence: 99%

Parallel Simulation of Complex Evacuation Scenarios with Adaptive Agent Models

Chen

Wang

Zomaya

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Simulation study on evacuation scenarios has gained tremendous attention in recent years. Two major research challenges remain along this direction: (1) how to portray the effect of individuals' adaptive behaviors under various situations in the evacuation procedures and (2) how to simulate complex evacuation scenarios involving huge crowds at the individual level due to the ultrahigh complexity of these scenarios. In this study, a simulation framework for general evacuation scenarios has been developed. Each individual in the scenario is modeled as an adaptable and autonomous agent driven by a weight-based decision-making mechanism. The simulation is intended to characterize the individuals' adaptable behaviors, the interactions among individuals, among small groups of individuals, and between the individuals and the environment. To handle the second challenge, this study adopts GPGPU to sustain massively parallel modeling and simulation of an evacuation scenario. An efficient scheme has been proposed to minimize the overhead to access the global system state of the simulation process maintained by the GPU platform. The simulation results indicate that the "adaptability" in individual behaviors has a significant influence on the evacuation procedure. The experimental results also exhibit the proposed approach's capability to sustain complex scenarios involving a huge crowd consisting of tens of thousands of individuals. Site. He is the Founder of the IEEE International Symposium on High Performance Distributed Computing (HPDC) and the cofounder of the IEEE International Conference on Autonomic Computing. His current research focuses on autonomic computing, high performance distributed computing, design and analysis of high speed networks, benchmarking and evaluating parallel and distributed systems, developing software design tools for high performance computing and communication systems, and network-centric applications. He is co-author/editor of four books on parallel and distributed computing: Autonomic

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Section: Density Fieldmentioning

confidence: 99%

Parallel Simulation of Complex Evacuation Scenarios with Adaptive Agent Models

Chen

Wang

Zomaya

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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“…The aggregate throughput is more important than the price and overall performance of a grid system. What makes grid different from conventional HPC systems, such as cluster, is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed [139,140].…”

Section: Grid Computing Systemsmentioning

confidence: 99%

A survey on resource allocation in high performance distributed computing systems

et al. 2013

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a b s t r a c tAn efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

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“…Overall, task scheduling is a hot topic for parallel and distributed computing and has been addressed not only in the field of cluster computing but also other fields like grid computing (Kołodziej et al, 2012;2013b;Chen et al, 2013;Wang et al, 2011a). On the cloud specifically, task scheduling deals actually with the allocation of virtual machines.…”

Section: 4mentioning

confidence: 99%

Using a vision cognitive algorithm to schedule virtual machines

Zhao

Mhedheb

Tao

et al. 2014

International Journal of Applied Mathematics and Computer Science

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Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption.

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Hybrid modelling and simulation of huge crowd over a hierarchical Grid architecture

Cited by 48 publications

References 29 publications

Parallel Simulation of Complex Evacuation Scenarios with Adaptive Agent Models

Parallel Simulation of Complex Evacuation Scenarios with Adaptive Agent Models

A survey on resource allocation in high performance distributed computing systems

Using a vision cognitive algorithm to schedule virtual machines

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