Simulation Modelling Practice and Theory最新文献

Complex Event Processing (CEP) is a successful method to transform simple IoT events created by sensors into meaningful complex business events. To enhance availability, an event fabrication mechanism is integrated within the CEP model, generating synthetic events to offset missing data, resulting in a quality-aware CEP model. In this model, generated complex events are characterized by quality properties, namely completeness and timeliness. To empirically assess the quality of complex events through experimentation, we have developed a hybrid simulation platform. The platform’s dual nature stems from its distinctive approach of simulating sensor behaviors while concurrently running the quality-aware CEP IoT platform. Users can conduct experiments that closely mimic actual operational scenarios and have, in real-time, full visibility and control over all involved aspects, including composite transformations, quality assessment, event fabrication and its effectiveness, and aggregated reports. A representative experiment in an IoT-enabled greenhouse with missing events is presented to demonstrate the usefulness of the platform. The contribution of the hybrid simulation platform is twofold: provide (a) quality assessment of complex events, using two established quality properties for IoT environments with specific computation formulas and (b) a comprehensive testbed covering all aspects of a typical IoT setup for realistic experimentation. Together, these elements provide significant cost–benefit advantages by enabling researchers and practitioners to pre-optimize operational efficiency and decision-making in IoT systems.

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained.

Cloud computing has revolutionized the IT landscape, providing scalable, on-demand computing resource. For efficiency in cloud environments, it is essential for modern organizations, where objectives often include cost reduction, resource consumption, operational efficiency and load balancing etc, to implement multi objective solutions. Single-objective systems can fail in handling dynamic and diverse workloads. This study introduces the Multi-Objective Whale Optimization-Based Scheduler (WOA-Scheduler) for efficient task scheduling in cloud computing environments. Leveraging the Whale Optimization Algorithm (WOA), the scheduler optimizes multiple objectives simultaneously, including cost, time, and load balancing. A key feature of the WOA-Scheduler is its flexibility in accommodating user-defined weights for different objectives, allowing organizations to prioritize optimization goals based on their specific requirements. Comparative analysis across various cloud environments demonstrates the superiority of the WOA-Scheduler over traditional single-objective approaches. By achieving a better balance between cost, time, and resource utilization, the scheduler enhances overall performance. Moreover, its multi-objective optimization capabilities enable dynamic adjustment of task assignments in response to changing workload conditions, ensuring efficient resource utilization and workload distribution. Overall, the WOA-Scheduler offers a customizable and adaptable solution for addressing the complexities of modern cloud services, ultimately improving performance and efficiency.

In an increasingly digitalized world, cybersecurity has emerged as a critical component of safeguarding sensitive information and infrastructure from malicious threats. The threat actors are often in line or even one step ahead of the defense, causing the increasing reliance of security teams on artificial intelligence while trying to detect zero-day attacks. However, most of the cybersecurity solutions based on artificial intelligence that can be found in the literature are trained and tested on reference datasets that are at least five or more years old, which gives a vague insight into their security performances. Moreover, they often tend to be designed as isolated, self-focused components. The aim of this paper is to design and implement a modular network intrusion detection architecture capable of simulating cyberattacks based on real-world scenarios while evaluating its defense capabilities. The architecture is designed as a full pipeline from real-time network data collection and transformation to threat-information presentation and visualization, with a pre-trained artificial intelligence module at its core. Well-known components like CICFlowMeter, Prometheus, and Grafana are used and modified to fit our data preparation and core modules to form the proposed architecture for real-world network traffic security monitoring. For the sake of cyberattack simulation, the proposed architecture is situated within a virtual environment, surrounded by the Kali Linux-based penetration simulation agent on one side and a vulnerable agent on the other. The intrusion detection artificial intelligence module is trained on the CICIDS-2017 dataset, and it is demonstrated using the proposed architecture that, despite being trained on an outdated dataset, the trained module is still effective in detecting sophisticated modern attacks. Two case studies are given to illustrate how modular architectures and virtual environments can be valuable tools to assess the security properties of artificial intelligence-based solutions through simulation in real-world scenarios.

This paper presents a model formulation for balanced twin lip vane pumps and an experimental activity to validate the model. The simulation model begins with a geometrical module that preprocesses the CAD drawings of a given unit. The model then performs a fluid dynamic analysis using a lumped-parameter formulation to solve for the pressures inside properly defined control volumes within the unit. The fluid dynamic model is solved simultaneously with a motion module that evaluates the planar motions of the vanes using Newton’s law of motion and with a lubricating interface solver based on the Reynolds equation. Contact dynamics formulations and elastohydrodynamic relations are applied at the vane locations in contact with the cam ring. The comparison with experimental results highlights a good match in volumetric and hydromechanical efficiencies. The measured outlet pressure ripple matches the simulated one for all tested speeds and pressures. The paper also shows a breakdown of the distribution of volumetric and power losses arising from various components of the machine. The proposed methodology is computationally inexpensive, so it can be used in future design and optimization studies aimed at improving the performance of such units.

Tramways decrease time, cost, and environmental impact of urban transport, while requiring multimodal intersections where trams arriving with nominal periodic timetables may have right of way over road vehicles. Quantitative evaluation of stochastic models enables early exploration and online adaptation of design choices, identifying operational parameters that mitigate impact on road transport performance.

We present an efficient analytical approach for offline scheduling of traffic signals at multimodal intersections among road traffic flows and tram lines with right of way, minimizing the maximum expected percentage of queued vehicles of each flow with respect to sequence and duration of phases. To this end, we compute the expected queue size over time of each vehicle flow through a compositional approach, decoupling analyses of tram and road traffic. On the one hand, we define microscopic models of tram traffic, capturing periodic tram departures, bounded delays, and travel times with general (i.e., non-Exponential) distribution with bounded support, open to represent arrival and travel processes estimated from operational data. On the other hand, we define macroscopic models of road transport flows as finite-capacity vacation queues, with general vacation times determined by the transient probability that the intersection is available for vehicles, efficiently evaluating the exact expected queue size over time. We show that the distribution of the expected queue size of each flow at multiples of the hyperperiod, resulting from temporization of nominal tram arrivals and vehicle traffic signals, reaches a steady state within few hyper-periods. Therefore, transient analysis starting from this steady-state distribution and lasting for the hyper-period duration turns out to be sufficient to characterize road transport behavior over time intervals of arbitrary duration.

We implemented the proposed approach in the novel OMNIBUS Java library, and we compared against Simulation of Urban MObility (SUMO). Experimental results on case studies of real complexity with time-varying parameters show the approach effectiveness at identifying optimal traffic signal schedules, notably exploring in few minutes hundreds of schedules requiring tens of hours in SUMO.

Several scenarios that use the Internet of Drones (IoD) networks require a Fog paradigm, where the Fog devices, provide time-sensitive functionality such as task allocation, scheduling, and resource optimization. The problem of efficient task allocation/scheduling is critical for optimizing Fog-enabled Internet of Drones performance. In recent years, many articles have employed meta-heuristic approaches for task scheduling/allocation in Fog-enabled IoT-based scenarios, focusing on network usage and delay, but neglecting execution time. While promising in the academic area, metaheuristic have many limitations in real-time environments due to their high execution time, resource-intensive nature, increased time complexity, and inherent uncertainty in achieving optimal solutions, as supported by empirical studies, case studies, and benchmarking data. We propose a task allocation method named F-DTA that is used as the fitness function of two metaheuristic approaches: Particle Swarm Optimization (PSO) and The Krill Herd Algorithm (KHA). We compare our proposed method by simulation using the iFogSim2 simulator, keeping all the settings the same for a fair evaluation and only focus on the execution time. The results confirm its superior performance in execution time, compared to the metaheuristics.

Educational big data analysis is facilitated by the significant amount of unstructured data found in education institutions. Python has various toolkits for both structured and unstructured data processing. However, its ability for processing large-scale data is limited. On the other hand, Spark is a big data processing framework, but it does not have the needed toolkits for processing unstructured rich text documents, 3D model and image processing. In this study, we develop a generic framework that integrates Python toolkits and Spark based on service-oriented architecture. The framework automatically extends the serial algorithm written in Python to distributed algorithm to accomplish parallel processing tasks seamlessly. First, our focus is on achieving non-intrusive deployment to Spark servers and how to run Python codes in Spark environment to process rich text documents. Second, we propose a compression-based schema to address the poor performance of small sized files in HDFS. Finally, we design a generic model that can process different types of poly-structured data such as 3D models and images. We published the services used in the system for sharing them at https level for constructing different systems. It is evaluated through simulation experiments using large-scale rich text documents, 3D models and images. According to the results, the speedup is 49 times faster than the standalone Python-docx in the simulations of extracting 232 GB docx files when eight physical nodes with 128 cores are used. It reaches about 89 times after further compression schema is applied. In addition, simulations for 3D model descriptors' extraction show that the simulation achieves a speedup of about 116 times. In the large-scale image's HOG features extraction simulation task of up to 256.7 GB (6,861,024 images), a speedup of up to 110 times is achieved.

This study proposes a simulation model for allocating counterbalanced forklifts in a logistics distribution center (LDC) with aisle constraints. Modeling the case study for a consumer goods firm, the performance measures of the logistics operation were calculated and certain experimental scenarios were purposed for decision-making regarding the number of forklifts and their productivity. The relevance of this research is validated by the gap in existing literature on enhancing forklift assignments in massive storage systems with restrictions. The simulation scenarios contribute toward standardizing logistics operations with similar characteristics, starting from the layout stage of an LDC. The designed simulation model demonstrates that the simulated allocation incorporates technical and human resources in warehouse operations. Utilizing discrete-event simulation (DES) as a framework, this study assesses various scenarios in an LDC with restrictions on the forklift. The hypothesis of the problem was analyzed, and the simulation model was used to characterize the system behavior under different scenarios and guide the decision-making processes impacting operational costs and client service levels. This research employs DES to address performance indicators and operational costs, serving as a methodological guide for resource allocation in logistics operations at distribution centers.