Throughput Reduction Research Articles

Identifying the Most Critical Evacuation Links Based on Road User Vulnerabilities

As one of the principal lifeline systems, transportation networks are crucial for evacuation during extreme weather events like hurricanes, and critical network links must remain intact. The conventional evaluation measures prioritize to achieve the maximum system efficiencies, and therefore they estimate the functional criticality of a road network using measures such as travel time increase or throughput reduction caused by a link disruption. This study asks a fundamental question on equity achievement of such measures and develops a new framework to incorporate road users’ vulnerabilities in identifying critical network links. This study introduces new evaluation measures that integrate the most vulnerable zones for evacuation prioritization based on social, environmental, and economic vulnerabilities. Results show that the critical links for the vulnerable population during an evacuation are not always identified by conventional link-based measures that emphasize overall system efficiencies. Among the links selected as critical using the throughput measure, only 25% serve socially vulnerable communities and 38% serve environmentally vulnerable populations. This highlights the importance of considering road users’ vulnerability when prioritizing resources to strengthen the links since a link disruption may cause more significant consequences for vulnerable road users. Decision-making to identify critical links and minimize the impact of disruptions remains critical to distribute resources more effectively during an emergency and support the timely and safe evacuation of vulnerable populations that should be prioritized to achieve more equitable evacuation and disaster responses. An online interactive map is developed based on the results of this study to show the exact location of the critical links and other important metrics.

RescueSNN: enabling reliable executions on spiking neural network accelerators under permanent faults.

To maximize the performance and energy efficiency of Spiking Neural Network (SNN) processing on resource-constrained embedded systems, specialized hardware accelerators/chips are employed. However, these SNN chips may suffer from permanent faults which can affect the functionality of weight memory and neuron behavior, thereby causing potentially significant accuracy degradation and system malfunctioning. Such permanent faults may come from manufacturing defects during the fabrication process, and/or from device/transistor damages (e.g., due to wear out) during the run-time operation. However, the impact of permanent faults in SNN chips and the respective mitigation techniques have not been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel methodology to mitigate permanent faults in the compute engine of SNN chips without requiring additional retraining, thereby significantly cutting down the design time and retraining costs, while maintaining the throughput and quality. The key ideas of our RescueSNN methodology are (1) analyzing the characteristics of SNN under permanent faults; (2) leveraging this analysis to improve the SNN fault-tolerance through effective fault-aware mapping (FAM); and (3) devising lightweight hardware enhancements to support FAM. Our FAM technique leverages the fault map of SNN compute engine for (i) minimizing weight corruption when mapping weight bits on the faulty memory cells, and (ii) selectively employing faulty neurons that do not cause significant accuracy degradation to maintain accuracy and throughput, while considering the SNN operations and processing dataflow. The experimental results show that our RescueSNN improves accuracy by up to 80% while maintaining the throughput reduction below 25% in high fault rate (e.g., 0.5 of the potential fault locations), as compared to running SNNs on the faulty chip without mitigation. In this manner, the embedded systems that employ RescueSNN-enhanced chips can efficiently ensure reliable executions against permanent faults during their operational lifetime.

Enhancing Hybrid Spectrum Access in CR-IoT Networks: Reducing Sensing Time in Low SNR Environments

The current utilization of the licensed spectrum band is not optimal. the abundance of Internet of Things (IoT) gadgets could lead to congestion in the unlicensed spectrum band. A potential solution is to integrate cognitive radios into IoT devices, specifically by developing CR-IoT (cognitive-radio-enabled IoT) devices that leverage the hybrid spectrum access (HSA) technique to access the licensed spectrum band and employ energy detectors for spectrum sensing. While HSA can enable high data throughput for CR-IoT networks, environments with low signal-to-noise ratio (SNR) may experience reduced performance. Particularly, in low SNR environments with SNR values ranging from -20dB to -24dB, the high level of noise uncertainty can cause the energy detector to spend more time sensing, which results in a significant reduction in transmission time and overall throughput. In this study, a novel approach is presented to address the challenge of noise uncertainties on the energy detector by implementing an adaptive sensing threshold. The simulation outcomes reveal that the proposed technique can significantly improve the throughput in scenarios characterized by low signal-to-noise ratio (SNR), with a maximum enhancement of up to 28%. This innovative approach can pave the way for more robust and efficient energy detectors in the presence of noise uncertainties .

Enhancing Media Streaming in Wireless Networks using IFW-CFH Algorithm

One of the major concerns for service providers and application developer are Quality of experience (QoE), where high traffic congestion on the Internet leads to the degradation of video quality. However, the effectiveness of video transmission is minimized due to the network based on packet loss, bandwidth, and delay. Because of bandwidth limitations, the videos transmitted are obtained in low quality. Meanwhile, various outcomes such as reduction in throughput, re-buffering, or mosaic are determined in packet loss which validated the video streaming obtained in reliable or unreliable mode. Therefore this paper proposes an Improved Fuzzy Weighted queueing based Crossover Fire Hawk (IFW-CFH) algorithm for effective real-time video transmission. The objective of the IFW-CFH approach is to reduce the delay, packet loss, and bandwidth to enhance the video quality via two key mechanisms namely congestion control mechanism as well as packet scheduling mechanism. During the generation of encoded video frames, the packaged packets to the local buffer are transmitted by the scheduler using our proposed IFW-CFH algorithm. Finally, the experimentation is conducted and the results show that the proposed method minimized transmission delay, packet loss, and bandwidth by 13.8% for effective real-time video transmission compared to the existing methods.

Toward Industry 4.0 in Surface Mount Technology: Smart Manufacturing in Stencil Printing Operations

As the foremost point of contact in surface mount technology (SMT) operations, the stencil/solder paste printer (SPP) is a major contributor to assembly defects in the printed circuit board (PCB) fabrication process. Initial printing defects lead to reductions in throughput, quality and reliability of the entire downstream system. Because of the high variation in SMT processes, a predictive, real-time machine learning (ML) algorithm to optimize printer settings based on the estimation of printing volumes is a promising solution to improving production performance and process capability. Building upon existing literature, this research discusses the implementation of a lab-trained ML model to predict optimal printing parameters and reduce changeover time within SPP processes through the training and testing of large solder paste inspection (SPI) datasets. To sustain growing market demand for smart electronics manufacturing (SEM), successful development and validation of a dynamic prediction system for optimizing printing parameters provides significant potential for improvements in first pass yield rates and system throughput.

Area-Time-Efficient Scalable Schoolbook Polynomial Multiplier for Lattice-Based Cryptography

Lattice-based cryptography (LBC) stands out as one of the most viable classes of quantum-resistant schemes. This brief explores a time-sharing approach, with different parallelism levels, for a crucial operation in LBC cryptosystems, i.e., polynomial multiplication. We also employ an innovative coefficient ordering method in our time-shared schoolbook polynomial multiplication (SPM) to combine the best of two worlds: design compactness and lower processing latency. Thus, our work offers a choice of design points with performance vs. resource trade-offs. Our fastest proposed design exhibits 80% and 57% reductions in LUTs and throughput, respectively, compared to the existing fully parallel SPM architecture (on Xilinx Ultrascale+), which lead to a 53% improvement in the area-time-product efficiency. Our smallest proposed design is more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.2\times $ </tex-math></inline-formula> faster than the existing low-cost parallel SPM architecture (on Xilinx Kintex-7) at the expense of 85% additional area resources.

Dynamic bond exchangeable thermoset vitrimers in 3D‐printing

AbstractDynamic bond exchanging vitrimers have emerged recently due to their malleability, self‐heal ability, recyclability, and mechanical stability. Likewise, 3D printing is consciously introduced at different platforms for ease of fabrication, high throughput, cost‐effectiveness, and waste reduction. These two distinctive techniques have recently made their consensus performance, resulting from a phenomenal change in the printing field. Conventionally, thermoplastic inks have been primarily used in 3D printing, owing to their effortless processability. At the same time, thermosets were utilized for their superior mechanical strength. However, these two essential properties have been required to be presented in the printed material. In that scenario, thermoset vitrimer materials have been introduced in 3D printing, where malleability and mechanical stability have been observed in the same material. Thus, this article details the recent vitrimer material included with the different 3D printing system systems with their reported results to understand and make them widespread. Eventually, the outlook and perspectives could be helpful to understand and enhance this specific field.

200 A Quantitative Assessment of Emergency Department Boarding and its Association With Decreases in Operational Efficiency: A Multicenter Nationwide Study

Boarding of emergency department (ED) patients is associated with worse patient care and increased mortality. Our local, multicenter study demonstrated an association between increased boarding and reductions in ED intake and throughput.

Experimental investigation of presence of air on performance of steam heating process

A Presence of air and other non-condensable gasses poses a serious nuisance for the steam network in a process industry. From a reduction in the process throughput to an acceleration in corrosion effects, presence of air & non-condensable can hamper the productivity as well as life of a process plant. For instance, the thermal conductivity (in W/mk) of air is 0.000049; in contrast with 0.002 for water, 0.20 for iron, and 0.96 for copper. Thus, even a thin layer of air can increase the resistance to heat flow significantly. Even the convective heat transfer coefficient for air is quite low (∼15 W/m2K) as compared to condensing steam (∼2000 W/m2K); so that even if the air convicts heat, it is likely to do so poorly.The objective of this paper is to show experimentally, how air affects the heat transfer process. We investigate the heat transfer performance in terms of the rate of temperature rise (ΔT/Δt) for a water-bath-type indirect heat exchanger. The bath is filled with 100 Litre of water each time and heated with steam; where an air vent is bypassed or taken in-line alternatively, while maintaining the initial conditions constant as far as feasible. The water bath is heated from 25 to 30℃ (room temperature) to 90-95℃ and the time taken for heating is noted in each case. The experimental set-up is equipped with electronic sensors for various temperatures, pressures, steam flow rates etc. Over a set of 10 such experiments, we have found that the presence of the air vent in the steam line consistently improves the rate of temperature rise (ΔT/Δt) with uniform heating in all sections of the heat exchanger. Furthermore, when the air vent is bypassed, the different sections of the heat exchanger are heated at different rates leading to distinct hot & cold spots in the heat exchanger sections.

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

Cyberattacks on connected automated vehicles: A traffic impact analysis

Cybersecurity has become one of the major challenges for Connected Automated Vehicles. Previous work in this field mostly focused on the detection of and defence against Connected Automated Vehicle-related cyberattacks. Using performance measures collected from traffic micro-simulations, the study analysed the traffic impacts of cyberattacks on Connected Automated Vehicles. Two types of adversary models, namely, time-delay attacks and disturbance attacks, were applied to the simulated traffic on freeway segments and un-signalised intersections, respectively. The effects of various cyberattacks were evaluated based on safety indicators, including Time-To-Collision, Deceleration Rate to Avoid Collision, and efficiency indicators, including speed and flow-density diagrams. The results revealed that both attacks increase the risk and severity of collisions on freeway segments and un-signalised intersections. Location-based time-delay attacks will result in significant deceleration, congestion and reduction in road throughput. Disturbance attacks not only cause congestion but also result in frequent acceleration/deceleration and uneven distribution of traffic density. The impacts of attacks are more severe on heavy traffic. At un-signalised intersections, location-based disturbance attacks lead to a significantly increased risk of right-angled collisions. The results could help better understand the effects of Connected Automated Vehicle-related cyberattacks and shed light on proactive defence against such attacks.

A review of the influence of blast fragmentation on downstream processing of metal ores

Rock fragmentation is one of the most commonly optimized blasting outcomes due to its significant influence on the efficiency of downstream operations. This paper reviews the influence of blast fragmentation on processing of metal ores. Two aspects of rock fragmentation that influence downstream mineral processing operations include the particle size of the run-of-mine ore and the degree of internal microfracturing in the fragments induced by blasting. The main influences identified include productivity of comminution equipment, energy consumption in comminution equipment and mineral recovery. An increase in blasting energy results in higher throughput and energy reduction in comminution circuits. Increase in blasting energy results in energy savings in the overall mining operations. However, when increasing the blasting energy, the environmental and safety impacts must be taken into consideration. Blast-induced microfractures increase the permeability of the fragments, therefore enhancing mineral exposure to leaching reagents and consequently may lead to higher mineral recoveries, particularly in heap and dump leaching operations which employ-one-stage comminution. However, the textural characteristics of the ore also influence the mineral recovery. Further research should focus on establishing the relationship between blast intensity and mineral leaching recovery for metal ores of different textural properties.

Analysis and Hardware Architecture on FPGA of a Robust Audio Fingerprinting Method Using SSM

The significant volume of sharing of digital media has recently increased due to the pandemic, raising the number of unauthorized uses of these media, such as emerging unauthorized copies, forgery, the lack of copyright, and electronic fraud, among others. In particular, several applications integrate services or products such as music distribution, content management, audiobooks, streaming, and so on, which require users to demonstrate and guarantee their audio ownership. The use of acoustic fingerprint technology has emerged as a solution that is widely used to secure audio applications. This technique extracts and analyzes certain information that identifies the inherent properties of a partial or complete audio file. In this paper, we introduce two audio fingerprinting hardware architectures with a feature extraction system based on spectrogram saliency maps (SSM) and a brute-force search. The first of these conducts a search in 33 saliency maps of 32 × 32 pixels in size. After analyzing the first algorithm, a second architecture is proposed, in which the saliency map is reduced to 27 × 25 pixels, requiring 75.67% fewer hardware resources, lowering the power consumption by 64.58%, and improving the efficiency by 3.19 times via a throughput reduction of 22.29%.

MODELING OF IEEE 802.11 COMPUTER NETWORKS OPERATION AT INCREASED INTERFERENCE INTENSITY

Context. High level of industrial noise increases the loss of information frames during transmission, which in turn decreases the network throughput. We propose a mathematical model of IEEE 802.11 networks operation under conditions of increased interference intensity. Objective. The purpose of this paper is to express in an explicit analytical form the effect of bit error rate (BER) on the probability of frame transmission and the network throughput. Method. We have proposed the method for constructing a model that allows you to directly calculate the dependence of the frame transmission probability on the number of stations operating in saturation mode, which is convenient for engineering calculations. The values of the model coefficients were selected by comparing the calculation results with the results obtained using the known Bianchi model, which describes the network operation in the form of a Markov process. In the range of up to 23 stations working with one access point, which corresponds to a collision probability of up to 0.5, the indicated dependences for both models satisfy each other with an accuracy sufficient for the practical application. An expression for the network throughput has been defined. Results. The results of the model development were used to take into account the effect of interference intensity on the information transfer process. This made it possible to explicitly express the effect of BER on the probability of frame transmission and the network throughput in the case of variations in the length of the frames and with a different number of competing stations. The degree of throughput reduction has been determined for BER = 10–5, 5∙10–5, 10–4 and increasing value of minimum contention window. Conclusions. In this work, a mathematical model has been developed for direct calculation of the probability of frame transmission and network throughput at different levels of BER.

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost

Degrowth and the city

Degrowth is both an academic debate and an activist call for a necessary socio-ecological transformation. It proposes a just and selective quantitative reduction of societal throughput to achieve ecological sustainability, social justice and individual well-being. What does such a transformation imply for cities, for place and space in general? Recently research has begun to explore this question, at the intersections of the degrowth project with geography, urban and planning studies. The present systematic review of this stream of the degrowth literature argues that contributions convincingly criticise mainstream solutions of sustainable urban development and portray an inspiring variety of local and sectoral alternatives. They also discuss the possibilities of spatial planning for degrowth. But the literature, related to a limited conceptualisation of space, lacks consideration for larger geographical scales (localism is prevalent). Also, limited attention is paid to material flows (the focus is on formal outcomes in the built environment) and there sometimes is a lack of reflection about positionality (with a tendency to apparently universalist solutions). Drawing in particular on Doreen Massey’s conceptualisation of the relationality of space and place, a conceptual framework is proposed for further research. It evidences questions neglected in the reviewed literature: how to spatialise degrowth beyond the local scale, not reducing the argument to a dualism between local = good and global = bad? And, how to transform not only the physicality of places but also the material and immaterial relations they are based on? The proposed framework, embracing a situated, relational and multiscalar understanding of space and its socio-ecological transformation, might be a first step in approaching these and other open questions in the debate on degrowth, cities and space.

Lightweight and Amplitude-Free Ultrasonic Imaging Using Single-Bit Digitization and Instantaneous Phase Coherence.

In the field of ultrasonic nondestructive testing (NDT), the total focusing method (TFM) and its derivatives are now commercially available on portable devices and are getting more popular within the NDT community. However, its implementation requires the collection of a very large amount of data with the full matrix capture (FMC) as the worst case scenario. Analyzing all the data also requires significant processing power, and consequently, there is an interest in: 1) reducing the required storage capacity used by imaging algorithms, such as delay-and-sum (DAS) imaging and 2) allowing the transmission and postprocessing of inspection data remotely. In this study, a different implementation of the TFM algorithm is used based on the vector coherence factor (VCF) that is used as an image itself. This method, also generally known as phase coherence imaging, presents certain advantages, such as a better sensitivity to diffracting geometries, consistency of defect restitution among different views, and an amplitude-free behavior as only the instantaneous phase of the signal is considered. Some drawbacks of this method must also be mentioned, including the fact that it poorly reproduces planar reflectors and presents a lower signal-to-noise ratio (SNR) than amplitude-based methods. However, previous studies showed that it can be used as a reliable tool for crack-like defect sizing. Thus, a lightweight acquisition process is proposed through single-bit digitization of the signal, followed by a phase retrieval method based on the rising and falling edge locations, allowing to feed the phase coherence imaging algorithm. Simulated and experimental tests were first performed in this study on several side-drilled holes (SDHs) in a stainless steel block and then extended to an experimental study on angled notches in a 19.05-mm ( 3/4" )-thick steel sample plate through multiview imaging. Results obtained using the array performance indicator (API) and the contrast-to-noise ratio (CNR) as quantitative evaluation parameters showed that the proposed lightweight acquisition process, which relies on binary signals, allows a reduction of the data throughput of up to 47 times. This throughput reduction is achieved while still presenting very similar results to phase coherence imaging based on the instantaneous phase derived from the Hilbert transform of the full waveform. In an era of increasing wireless network speed and cloud computing, these results allow considering interesting perspectives for the reduction of inspection hardware costs and remote postprocessing.

Future cost projections for photovoltaic module manufacturing using a bottom-up cost and uncertainty model

Photovoltaic module prices have typically decreased faster than projections. There are two methods usually used for these projections; cumulative market shipment experience curves or detailed bottom-up cost calculations for specific technologies. The former suffers from a lack of specificity in terms of technology or changing market situations, and the latter from a lack of quantifiable uncertainty. We present an alternative bottom-up future cost model for a new vertically integrated c-Si PV factory, from poly silicon to module, incorporating input ranges and uncertainty via a Monte Carlo analysis. Neglecting profit margins, the majority of projected scenarios for global 2025 module manufacturing cost fall between 0.10 US$/W and 0.18 US$/W, due mostly to reductions in raw materials costs for module fabrication. The lowest 10th percentile projections, below 0.10 US$/W, would be realised by the largest scale manufacturers with access to very low materials costs and low operational costs. The model projects production cost learning rates between 29% and 43% compared to a long-term historical average module selling price learning rate of 24%. Analysis of the competitive position of silicon heterojunction cell technology in combination with multi-wire module technology is performed. Access to the multi-wire technology improves competitiveness of 2025 manufacturing in high labour rate countries if silver prices remain high and the cost of the multi-wire material is reduced in line with other materials, motivating continued development of this technology. Finally, continued reduction in labour intensity, through automation and throughput will enable more competitive manufacturing in higher labour cost locations.

Location-Based LTE-M Uplink Power Control and Radio Resource Scheduling.

Long-Term Evolution for Metro (LTE-M) is adopted as the data communication system in urban rail transit to exchange bio-direction train-wayside information. Reliable data communication is essential in LTE-M systems for ensuring trains’ operation safety and efficiency. However, the inter-cell inference problem exists in LTE results in throughput reduction, especially when trains are in the edge area of adjacent cells, and has negative effects on train operation. The uplink power control and radio resource scheduling scheme is studied in LTE-M system which differentiates from public cellular networks in user numbers and the availability of the trains’ locations. Since the locations of the trains are available, the interferences from the neighbouring cells can be calculated, and a location based algorithm together with soft frequency reuse is designed. In addition, a proportional fair algorithm is taken to improve uplink radio resource scheduling considering the fairness to different train-wayside communication service requirements. Through simulation, the practicability of the proposed schemes in communication system of urban rail transit is verified in aspects of radio power control and data communication throughput.

Dynamic Scheduling of Contextually Categorised Internet of Things Services in Fog Computing Environment.

Fog computing emerged as a concept that responds to the requirements of upcoming solutions requiring optimizations primarily in the context of the following QoS parameters: latency, throughput, reliability, security, and network traffic reduction. The rapid development of local computing devices and container-based virtualization enabled the application of fog computing within the IoT environment. However, it is necessary to utilize algorithm-based service scheduling that considers the targeted QoS parameters to optimize the service performance and reach the potential of the fog computing concept. In this paper, we first describe our categorization of IoT services that affects the execution of our scheduling algorithm. Secondly, we propose our scheduling algorithm that considers the context of processing devices, user context, and service context to determine the optimal schedule for the execution of service components across the distributed fog-to-cloud environment. The conducted simulations confirmed the performance of the proposed algorithm and showcased its major contribution—dynamic scheduling, i.e., the responsiveness to the volatile QoS parameters due to changeable network conditions. Thus, we successfully demonstrated that our dynamic scheduling algorithm enhances the efficiency of service performance based on the targeted QoS criteria of the specific service scenario.