Cagigas-Muniz, DanielDiaz-del-Rio, FernandoSevillano-Ramos, Jose LuisGuisado-Lizar, Jose-Luis...
16页
查看更多>>摘要：Graphics Processing Units (GPUs) can be used as convenient hardware accelerators to speed up Cellular Automata (CA) simulations, which are employed in many scientific areas. However, an important set of CA have performance constraints due to GPU memory bandwidth. Few studies have fully explored how CA implementations can take advantage of modern GPU architectures, mainly in the case of intensive memory usage. In this paper, we make a thorough study of techniques (stencil computing framework, look-up tables, and packet coding) to efficiently implement CA on GPU, taking into account its detailed architecture. Exhaustive experiments to validate these implementation techniques for a number of significant memory bounded CA are performed. The CA analysed include the classical Game of Life, a Forest Fire model, a Cyclic cellular automaton, and the WireWorld CA. The experimental results show that implementations using the presented techniques can significantly outperform a baseline standard GPU implementation. The best performance results of all known implementations of memory bounded CA were obtained. Moreover, some of the techniques, like look-up tables or temporal blocking, are indeed relatively easy to implement or to apply when the transition rules are simple. Finally, detailed descriptions and discussions of the indicated techniques are included, which may be useful to practitioners interested in developing high performance simulations in efficient languages based on CA on GPU.
查看更多>>摘要：Digital Twin in Industry 4.0 utilizes Internet of Things (IoT) to collect real-life data and combine it with simulation models for product design and development. The simulation process can be executed as a workflow, consisting of tasks with precedence constraints. In a container based workflow execution system, each task in the workflow is executed in a container within a virtual machine (VM). In this paper, a three-step scheduling model is proposed to combine scheduling of container-based workflows and the deployment of containers on a cloud- edge environment. In the first step, virtual CPU (vCPU) is allocated for each container to enable vCPU sharing among different containers. Next, two-step resource deployment is used to schedule the containers onto VM, and VM onto the physical machines in either edge or cloud environment. Multiple objectives are considered, including minimizing makespan, load imbalance, and energy consumption, from the perspective of cloud-edge resources as well as containerized workflows. To obtain a set of non-dominated solutions, three evolution strategies are designed and combined with two multi-objective algorithm frameworks - co-evolution strategy (CES), basic non-co-evolution strategy (B-NCS), and hybrid non-co-evolution strategy (H-NCS). Simulation results demonstrate that the proposed model outperforms the existing two-step scheduling model and H-NCS performs better than other strategies.
查看更多>>摘要：As the key technology of edge computing, computing offloading has attracted the attention of many scholars in recent years. Many people use heuristic algorithm as the basic algorithm to study the algorithm of computing offloading, but a single heuristic algorithm has some defects, such as some will fall into local optimal, and some will converge prematurely. In order to make up for the defects of single heuristic algorithm applied to the calculation offloading and improve the efficiency of the algorithm, this paper combines genetic algorithm with ant colony algorithm, and designs the calculation offloading strategy of gene-ant colony fusion algorithm. Firstly, a group of solutions are obtained through the selection, crossover, mutation and other operations of genetic algorithm, and the solution is improved as the initial solution of ant colony algorithm. The fusion algorithm makes full use of the feedback value of genetic algorithm and the high efficiency of ant colony algorithm to overcome the shortcomings of the two algorithms. The feasibility of the algorithm is verified by several groups of experiments. The simulation results show that compared with GA, ACA and PSO, the number of iterations is reduced by 17.96%, 24.43% and 36.25% respectively. When the base station remains unchanged, the G-ACA has the lowest objective function value. Compared with GA, ACA and PSO algorithm, the objective function value is reduced by 36.68%, 16.15% and 11.35% respectively. That is, the fused algorithm is better than the non fused GA,ACA and PSO in time delay, energy consumption and objective function value.
查看更多>>摘要：Residual stress affects component performance, and the existence of pre-stress changes the residual stress of machined surfaces as well, emphasizing the importance of studying the evolution of residual stress in sequential cutting. This paper reports a numerical investigation of the machining-induced residual stress profile of sequential cuts for orthogonal cutting of AISI 304, considering the effects of edge radius and cutting depth. A Coupled Eulerian-Lagrangian (CEL) model is employed for the first time to stably simulate the evolution of residual stress of multiple sequential cuts. The effectiveness of the proposed method is verified by comparing the chip formation and surface residual stress between simulated and experimental results. The cutting force and cutting temperature, as well as mechanical and thermal loads, are extracted to explain the generation and evolution of residual stress in sequential cutting. It is found that the residual stress on the machined surfaces will decrease during sequential cutting, and a stable value can be reached after approximately six sequential cuts. With the progress of sequential cutting, a larger honed tool edge radius and cutting depth will lead to a slower reduction of residual stress.
查看更多>>摘要：Complex simulation systems, such as those involving emergency medical services (EMS), are often too computationally demanding to be used in optimization problems. Metamodeling is an attractive alternative, in which a sample of system configurations is evaluated using simulation, and a fast predictive model is developed as a surrogate for the slow simulator. Though the metamodeling literature is extensive, there has been little exploration of how much data is required to construct metamodels that can be used to solve optimization problems effectively, particularly in the context of a complicated rural-urban EMS system environment. In this work, the EMS system in northern St. Louis County, Minnesota is studied, with the goal of discovering station configurations with improved response times. The underlying physical system is complex, with 12 stations spread across both rural and urban areas and a fairly large geographic footprint. A decade of call data is used to develop and validate a stochastic discrete event simulator (DES) for this system, and then the simulator and raw data are used to select realistic station configurations to train the metamodel. Results are first given for just a single station within the system, and then increasingly complex settings are examined culminating with consideration of all 12 stations. Overall, though the metamodeling approach was effective for simpler cases, it requires a tremendous amount of data for larger settings. Specifically for the St. Louis County example, improved configurations were found for the one- and two-station cases, but the amount of data required to produce effective metamodels for the five- and twelve-station versions of the system was computationally infeasible given current DES and optimization heuristic implementations.
查看更多>>摘要：Interdependencies among urban critical infrastructure systems (CISs) significantly impact the reliability and performance of CISs and the resilience of modern societies. Although several approaches exist for modeling interdependent CISs and studying their behavior, models developed in previous studies often fail to incorporate CIS domain knowledge, capture systemic heterogeneities among the CISs, and accurately model CISs interdependencies. Consequently, existing models have a limited ability to simulate interdependent CISs with sufficient detail and accuracy. To address these limitations, this study proposes a high-level architecture (HLA)-based framework for modeling interdependent CISs that can leverage and integrate well-tested practices, knowledge, data and simulation tools accumulated over years of wide usage in various CIS domains. The framework provides a methodology for co-simulating heterogeneous fine-grained CIS domain-specific models and modeling complex interactions between them and with their external environments, hence reproducing with high fidelity the complex coupled systems. A case study of two interdependent power and water systems was conducted, which demonstrated the efficacy of the proposed framework. Simulation results revealed that the HLA-based CISs model could capture the heterogeneous behaviors of the CISs and reveal a variety of failure-induced system vulnerabilities and feedback loops which may not be observable when using other existing modeling approaches.
Chalapathi, G. S. S.Chamola, VinayJohal, WafaAryal, Jagannath...
18页
查看更多>>摘要：Edge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, many cloudlets are required in such an edge computing network, leading to a tremendous increase in carbon emissions of computing networks globally. This increase in carbon emission envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). But GCNs must deal with the problem of the unpredictability of green energy available to them while optimizing the performance (in terms of latency) delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs to address this issue. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reduction in the latency while ensuring up to 50.15% of reduction in the energy consumption than other comparable task-assignment schemes.Thus, Ge-LATA suggests that it leads to an optimal task assignment by considering the various factors mentioned above during the task assignment process. Thus, Ge-LATA considers the above-mentioned extensive set of parameters during the task allotment process. It also proposes an efficient green energy allotment scheme that adapts itself to actual weather and network conditions, leading to optimal task assignment decisions in GCNs.
Risco-Martin, Jose L.Henares, KevinMittal, SaurabhAlmendras, Luis F....
20页
查看更多>>摘要：Cloud infrastructure provides rapid resource provision for on-demand computational require-ments. Cloud simulation environments today are largely employed to model and simulate complex systems for remote accessibility and variable capacity requirements. In this regard, scalability issues in Modeling and Simulation (M & S) computational requirements can be tackled through the elasticity of on-demand Cloud deployment. However, implementing a high performance cloud M & S framework following these elastic principles is not a trivial task as parallelizing and distributing existing architectures is challenging. Indeed, both the parallel and distributed M & S developments have evolved following separate ways. Parallel solutions has always been focused on ad-hoc solutions, while distributed approaches, on the other hand, have led to the definition of standard distributed frameworks like the High Level Architecture (HLA) or influenced the use of distributed technologies like the Message Passing Interface (MPI). Only a few developments have been able to evolve with the current resilience of computing hardware resources deployment, largely focused on the implementation of Simulation as a Service (SaaS), albeit independently of the parallel ad-hoc methods branch. In this paper, we present a unified parallel and distributed M & S architecture with enough flexibility to deploy parallel and distributed simulations in the Cloud with a low effort, without modifying the underlying model source code, and reaching important speedups against the sequential simulation, especially in the parallel implementation. Our framework is based on the Discrete Event System Specification (DEVS) formalism. The performance of the parallel and distributed framework is tested using the xDEVS M & S tool, Application Programming Interface (API) and the DEVStone benchmark with up to eight computing nodes, obtaining maximum speedups of 15.95x and 1.84x, respectively.
Raheja, SupriyaObaidat, Mohammad S.Kumar, ManojSadoun, Balqies...
16页
查看更多>>摘要：Monitoring of cities based on different air pollutants is required to manage the quality of air. The worldwide ranking is based on the one air pollutant PM2.5. The main purpose of the work is to find the worst air polluted city in India based on multiple pollutants. A hybrid framework namely "Analytic Hierarchy Process-Combinative Distance-Based Assessment (A-CODAS) is introduced to anSIMPAT102540alyze and rank the cities based on quality of air. The AHP technique is used to assign the priority weights to the multiple air pollutants whereas CODAS approach is used to rank the cities and to find the city with the worst air quality in 2020. The simulation analysis is performed on the seven worst polluted cities of India. The proposed framework is validated by performing the spearman's correlation ranking with other two existing multicriteria decision making approaches namely "Analytic Hierarchy Process" and "Technique for Order Preference by Similarity to Ideal Solution". Sensitivity analysis is also performed considering different scenarios to determine the dependency of a particular pollutant on the quality of air or to check whether the priority weights affect the ranking of the cities. The results prove the consistency and efficiency of the proposed model.
Tahat, AshrafBadr, Bashar E. A.Edwan, Talal A.Phillips, Iain W....
17页
查看更多>>摘要：We revisit the problem of link capacity under-utilisation in TCP Congestion Control (TCP-CC) when working in High-Bandwidth-Delay-Product (High-BDP) networks. We approach this problem using a multi-mode approach and propose TCP-Gentle as an example of TCP-CC that uses this approach. While General Additive Increase Multiplicative Increase (GAIMD) congestion control algorithms received a lot of attention in the literature, little was mentioned about modelling multi-mode GAIMD. To this aim, we provide a tractable optimisation-theoretic model for TCP-Gentle which can be generalised to any multi-mode GAIMD. We show through analysis, simulation, and real-test-bed experiments of single flow, double flow, and single flow with background web traffic, that the proposed TCP-Gentle is competitive with existing TCP variants. Particularly, under certain assumption, TCP-Gentle can outperform TCP-YeAH in terms of fairness to TCP-NewReno. Besides, the proposed TCP-Gentle is more gentle to network; it maintains minimal average queues of less than 1.5% of pipe's BDP, and reassembles to a great extent a highly-concave congestion window.
NSTL
Elsevier