Performance Modeling Of Networks Research Articles

An Efficient Deployment Scheme With Network Performance Modeling for Underwater Wireless Sensor Networks

An Efficient Deployment Scheme With Network Performance Modeling for Underwater Wireless Sensor Networks

Modeling more software performance antipatterns in cyber-physical systems

AbstractThe design of cyber-physical systems (CPS) is challenging due to the heterogeneity of software and hardware components that operate in uncertain environments (e.g., fluctuating workloads), hence they are prone to performance issues. Software performance antipatterns could be a key means to tackle this challenge since they recognize design problems that may lead to unacceptable system performance. This manuscript focuses on modeling and analyzing a variegate set of software performance antipatterns with the goal of quantifying their performance impact on CPS. Starting from the specification of eight software performance antipatterns, we build a baseline queuing network performance model that is properly extended to account for the corresponding bad practices. The approach is applied to a CPS consisting of a network of sensors and experimental results show that performance degradation can be traced back to software performance antipatterns. Sensitivity analysis investigates the peculiar characteristics of antipatterns, such as the frequency of checking the status of resources, that provides quantitative information to software designers to help them identify potential performance problems and their root causes. Quantifying the performance impact of antipatterns on CPS paves the way for future work enabling the automated refactoring of systems to remove these bad practices.

Wide-body aircraft fuel jettison system design and analysis

In order to design the highly integrated fuel jettison system, the hydrodynamic performance design flow chart for the highly integrated fuel system has been proposed, and the 1-D fuel line network performance model has been developed. An innovative fuel jettison system for wide-body aircraft has been designed. The system performance has been simulated and analyzed. The results show that the proposed flow chart can be used for the highly integrated fuel system design, and the fuel jettison performance requirements can be met. The system parameters including the wing tank main pump characteristic, the flow area of the orifice set in the fuel line connecting the feeding line and the refueling line, and the flight height have significant effects on the fuel jettison performance. Besides, the aircraft total fuel jettison performance is also affected by the central tank override pump characteristic, the engine fuel consumption, and the flow resistance of fuel line network.

Artificial Neural Network Performance Modeling and Evaluation of Additive Manufacturing 3D Printed Parts

This research article presents a comprehensive study on the performance modeling of 3D printed parts using Artificial Neural Networks (ANNs). The aim of this study is to optimize the mechanical properties of 3D printed components through accurate prediction and analysis. The study focuses on the widely employed Fused Deposition Modeling (FDM) technique. The ANN model is trained and validated using experimental data, incorporating input parameters such as temperature, speed, infill direction, and layer thickness to predict mechanical properties including yield stress, Young's modulus, ultimate tensile strength, flexural strength, and elongation at fracture. The results demonstrate the effectiveness of the ANN model with an average error below 10%. The study also reveals the significant impact of process parameters on the mechanical properties of 3D printed parts and highlights the potential for optimizing these parameters to enhance the performance of printed components. The findings of this research contribute to the field of additive manufacturing by providing valuable insights into the optimization of 3D printing processes and facilitating the development of high-performance 3D printed components.

Environmental inefficiencies for arrival flights at European airports.

In this paper, we analyze two months of trajectory data for aircraft landing in five major European airports. Based on open ADS-B data from the OpenSky Network and open performance models, we enrich all trajectories with automatically detected procedure information, fuel consumption, and emissions for supported aircraft types. To assess the inefficiencies associated with holding patterns, point merges, and continuous descent operations across different airports, we propose methodologies to quantify and compare these environmental inefficiencies. Holding patterns are found to have a higher negative impact on the environment than point merge and continuous descent operations. Furthermore, the paper provides recommendations for procedure evaluations of future airports, which could help policymakers and relevant stakeholders to evaluate the environmental performances of arrival procedures based on open data and open models.

On Quantum Algorithms for Random Walks in the Nonnegative Quarter Plane

It is well known that strong connections exist between random walks (RWs) in the nonnegative quarter plane and the mathematical performance modeling, analysis and optimization of computer systems and communication networks. Examples include adaptive threshold-based load balancing in concert with affinity scheduling (e.g., [16, 10]), scheduling parallel jobs on parallel computer systems (e.g., [15]), and asymptotic scalabilty in wireless and linear loss networks (e.g., [9]).

Environmental efficiency of port and regional system: A two-stage network efficiency model

Ports have a strong driving effect on regional economic growth. In turn, the prosperity and development of the regional economy also provide the port with various resources and services that enhance its comprehensive strength. Therefore, the harmonious development of the port and region is particularly important. In this context, the efficiency evaluation of the port–region system (PRS) has practical significance. To evaluate the PRS, this study constructs a feasible target-setting two-stage network performance model, which has two main advantages, unlike those from previous studies. First, the proposed model considers the linkage between ports and regional systems. Second, the traditional meta-frontier has infeasible combinations of inputs and outputs, whereas the proposed model modifies the selection rule of the meta-frontier and can provide a feasible meta-frontier for inefficient PRSs. The empirical analysis yields the following valuable research findings. First, the proposed models can effectively overcome the underestimation of efficiency. Second, in China, provincial PRS performance has considerable potential for improvement in the P- or R-subsystem. Third, all PRSs need to enhance their management ability, and the seven PRSs also need to improve their technical level. Finally, this study presents suggestions for the corresponding performance improvement of inefficient PRS.

Mutation-based analysis of queueing network performance models

Performance models have been used in the past to understand the performance characteristics of software systems. However, the identification of performance criticalities is still an open challenge, since there might be several system components contributing to the overall system performance. This work combines two different areas of research to improve the process of interpreting model-based performance analysis results: (i) software performance engineering that provides the ground for the evaluation of the system’s performance; (ii) mutation-based techniques that nicely supports the experimentation of changes in performance models and contribute to a more systematic assessment of performance indices. We propose mutation operators for specific performance models, i.e., queueing networks, that resemble changes commonly made by designers when exploring the properties of a system’s performance. Our approach consists in introducing a mutation-based approach that generates a set of mutated queueing network models. The performance of these mutated networks is compared to that of the original network to better understand the effect of variations in the different components of the system. A set of benchmarks is adopted to show how the technique can be used to get a deeper understanding of the performance characteristics of software systems.

Performance Modeling and Evaluation of the Inter-Blockchain Network: A Queuing Approach

Performance Modeling and Evaluation of the Inter-Blockchain Network: A Queuing Approach

Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.

User learning can affect software system's response time

A session-based software system provides various services to its end users through graphical user interfaces. A novice user of a service's user interface takes more think time—the average time to comprehend the content and its layout on the interface—in comparison to expert users. The think time gradually decreases as she repeatedly comprehends the same interface over time. This decrease in think time is the user learning phenomenon. Owing to this learning behavior, the proportion of users—at various learning levels for different services—changes dynamically leading to a difference in the workload. Traditionally though, workload specifications (required for system performance evaluation) never accounted for user learning behavior. They generally assumed a global mean think time, instead. In this work, we first report an experimental study that demonstrates the impact of user learning of the graphical user interfaces of session-based systems. We measure the performance data of a real system while it is getting used by synthetic users (who are) at various learning levels. Statistical tests on our measured data reveal that different human learning curves, combined with different amounts of users arriving the system with various learning levels for different services, produce different system performance. Next, we explain a queueing network (QN) performance model called CogQN that accounts for user learning. It is a multi-class QN model where each service and its learning level constitute a class of users for the service. We present the concept of learning-level dependent, class-switching probabilities to model the probabilistic transition of learning levels of users for different services. This novel concept enables us to avoid the exponential explosion of classes that would otherwise be suffered by traditional class-switching theory, had it been used in this work. We solve the model using discrete-event simulation and validate it against the empirical data. Our CogQN model captures the effect of user learning on overall mean response times across different learning conditions within 10% absolute error in comparison to empirical data.

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

A Performance Model of Controller Area Networks for Vehicles under Unsaturated Traffic

A Performance Model of Controller Area Networks for Vehicles under Unsaturated Traffic

Automated model-based performance analysis of software product lines under uncertainty

Context: A Software Product Line (SPL) can express the variability of a system through the specification of configuration options. Evaluating performance characteristics, such as the system response time and resource utilization, of a software product is challenging, even more so in the presence of uncertain values of the attributes. Objective: The goal of this paper is to automate the generation of performance models for software products derived from the feature model by selection heuristics. We aim at obtaining model-based predictive results to quantify the correlation between the features, along with their uncertainties, and the system performance. This way, software engineers can be informed on the performance characteristics before implementing the system. Method: We propose a tool-supported framework that, starting from a feature model annotated with performance-related characteristics, derives Queueing Network (QN) performance models for all the products of the SPL. Model-based performance analysis is carried out on the models obtained by selecting the products that show the maximum and minimum performance-based costs. Results: We applied our approach to almost seven thousand feature models including more than one hundred and seventy features. The generation of QN models is automatically performed in much less than one second, whereas their model-based performance analysis embeds simulation delays and requires about six minutes on average. Conclusion: The experimental results confirm that our approach can be effective on a variety of systems for which software engineers may be provided with early insights on the system performance in reasonably short times. Software engineers are supported in the task of understanding the performance bounds that may encounter when (de)selecting different configuration options, along with their uncertainties.

A dynamic delay-based reliability evaluation model for communication networks

The traditional network reliability research generally focuses on the topological connectivity among given nodes based on graph theory. Without considering the relationship between network failures and network performance degradation, traditional network reliability analysis cannot reflect actual network ability reaching up to a certain accomplishment level over a utilization interval. As for the performability analysis of network with specific requirement of transmission time, this paper proposes a dynamic delay-based network reliability evaluation method, which considers network’s ability that transmission delay can be controlled under expected upper limit level in the course of network normal operating period, and puts forward a three-layer network reliability analysis architecture. By integrating a network component reliability model, a network topology model and a network performance model, the dynamic delay-based network reliability model is established, and the network reliability influence factors are integrated into network reliability evaluation. A simple example shows that for a given network, network reliability is different from specified performance requirement. Furthermore, network failures will result in network performance degradation in network long-term operation. It is also proven that network reliability relates not only to network topological connectivity but also to network application environment and specified performance requirement. The key contribution of this paper is to guide network engineers to design a high performance network by optimizing the network parameters according to the delay-based network reliability evaluation method proposed in this paper.

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

Objective: Optical networks exploit the Wavelength Division Multiplexing (WDM) to meet the ever-growing bandwidth demands of upcoming communication applications. This is achieved by dividing the enormous transmission bandwidth of fiber into smaller communication channels. The major problem with WDM network design is to find an optimal path between two end users and allocate an available wavelength to the chosen path for the successful data transmission. Methods: This communication over a WDM network is carried out through lightpaths. The merging of all these lightpaths in an optical network generates a virtual topology which is suitable for the optimal network design to meet the increasing traffic demands. But, this virtual topology design is an NP-hard problem. This paper aims to explore Mixed Integer Linear Programming (MILP) framework to solve this design issue. Results: The comparative results of the proposed and existing mathematical models show that the proposed algorithm outperforms with the various performance parameters. Conclusion: Finally, it is concluded that network congestion is reduced marginally in the overall performance of the network.

Capacity-Constrained Network Performance Model for Urban Rail Systems

This paper proposes a general network performance model (NPM) for monitoring the performance of urban rail systems using smart card data. NPM is a schedule-based network loading model with strict capacity constraints and boarding priorities. It distributes passengers over the network given origin-destination demand, operations, route choice, and effective train capacity. A Bayesian simulation-based optimization method for calibrating the effective train capacity is introduced, which explicitly recognizes that capacity may be different at different stations depending on congestion levels. Case studies with data from the Mass Transit Railway network in Hong Kong are used to validate the model and illustrate its applicability. NPM is validated using survey data on left-behind passengers and exiting passenger flow extracted from smart card data. The use of NPM for performance monitoring is demonstrated by analyzing the spatial-temporal crowding patterns in the system and evaluating dispatching strategies.

Secrecy and performance models for query processing on outsourced graph data

Database outsourcing is a challenge concerning data secrecy. Even if an adversary, including the service provider, accesses the data, she should not be able to learn any information from the accessed data. In this paper, we address this problem for graph-structured data. First, we define a secrecy notion for graph-structured data based on the concepts of indistinguishability and searchable encryption. To address this problem, we propose an approach based on bucketization. Next to bucketization, it makes use of obfuscated indexes and encryption. We show that finding an optimal bucketization tailored to graph-structured data is NP-hard; therefore, we come up with a heuristic. We prove that the proposed bucketization approach fulfills our secrecy notion. In addition, we present a performance model for scale-free networks which consists of (1) a number-of-buckets model that estimates the number of buckets obtained after applying our bucketization approach and (2) a query-cost model. Finally, we demonstrate with a set of experiments the accuracy of our number-of-buckets model and the efficiency of our approach with respect to query processing.

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

Performance Modeling and Analysis of WDM Optical Networks under Wavelength Continuity Constraint using MILP

A Self-Tuning Algorithm for Optimal QoE-Driven Traffic Steering in LTE

Due to the wide diversity of services in mobile networks, cellular operators have changed their focus from Quality of Service (QoS) to Quality of Experience (QoE). To manage this change, Self-Organizing Networks (SON) techniques have been developed to automate network management, with traffic steering as a key use case. Traditionally, traffic steering aims to balance traffic volume or load among adjacent cells. Although more advanced schemes have been devised to balance QoE among cells, these do not guarantee that the overall system QoE is improved. In this work, a novel self-tuning algorithm for parameters in a classical mobility load balancing scheme is proposed to steer traffic among adjacent cells in a Long-Term Evolution (LTE) network driven by QoE criteria. Unlike previous approaches, based on heuristic rules, the proposed algorithm takes a gradient ascent approach to ensure that parameter changes always improve the overall system QoE. For this purpose, the impact of parameter changes on system QoE is estimated with an analytical network performance model that can be adjusted with statistics taken from the real network. The proposed algorithm is tested in a system-level simulator implementing a realistic LTE scenario. Results show that the method outperforms classical load and QoE mobility load balancing schemes.