Average Arrival Rate Research Articles

Data-driven assessment of business process resilience

Process resilience represents a core competence for organizations in light of an increasing number of process disruptions, such as sudden increases in case arrivals or absences in the workforce. It reflects an organization’s ability to restore a process to its acceptable performance level after a disruption. In this regard, the first key step for organizations towards achieving resilience is to understand how resilient their processes actually are. Although recognized as important, few works focus on such resilience assessment in a data-driven manner, thus barring organizations from gaining the necessary insights into how much their processes are affected by disruptions and how long it takes them to recover. To address this problem, we propose an approach for automated resilience assessment, based on recorded event data. Our approach interprets relevant process characteristics, such as the average lead time or arrival rate, as time series, which capture the development of the process execution over time. Based on these time series, it uses statistical modeling, specifically a vector autoregressive model, to determine the inter-relations between those characteristics and assess how the process performance responds to a disruption, i.e., a significant and temporal change in one of the process characteristics. We validate our approach by comparing its accuracy with a what-if analysis using a simulation model and demonstrate its effectiveness by assessing the resilience of the same process to diverse disruptions across different organizations.

Features of the Development of International Trade in Tourist Services

Introduction. The relevance of studying the peculiarities of the development of international trade in tourist services is determined by the significant role of world tourism in the structure of the global economy. In the conditions of a complex international political situation, which is dynamically changing, it is crucial to identify the vectors of the development of the tourism industry to determine the future structure of the macro-regional distribution of tourist flows. Aim and tasks. The purpose of this study is to identify the dominant factors and prospects for the development of international tourism business in the conditions of modern global economic and political instability to determine the most favourable marketing strategies for interaction in the international market of tourist services by extrapolating the results to the possibilities of post-war recovery of the tourism industry in Ukraine. Results. An analysis of international tourism arrivals showed that the average annual growth rate of the industry from 1995 to 2020 average annual growth of international tourist arrivals was more than 4 per cent. Over the estimated period from 1995 to 2023, the average arrival rate decreased to 2.46 per cent. Macro-regions are considered the most promising for the scaling of international tourism, as well as the factors that will contribute to this and can inhibit economic processes. Special attention is paid to the positioning of Ukraine during the post-war recovery of the tourism industry. Forecast indicators indicate that international tourist arrivals to Ukraine could reach 61 million people in the tenth year of the post-war period. An alternative strategy for promoting national tourist products and brands in the target markets of Central and Eastern Europe, Asia, and the Middle East was proposed, and the feasibility of continuing the promotion of Ukraine as a tourist brand was substantiated. Conclusions. This study confirms the vector of the shift in global tourist flows under the influence of a number of factors that deepen the contradictions between the Global South and the North, revealing key trends in the growth and displacement of international tourist flows among countries belonging to two opposite camps. The directions of institutional and structural changes required to restore international tourism in Ukraine in the post-war period to strengthen Ukraine's position in the global tourism market are proposed.

Application of Queue Theory in Cafe Services with Erlang Distribution

As urban lifestyles evolve, culinary businesses, particularly cafes, have experienced rapid growth. This surge in popularity has led to an increase in customers and, consequently, longer queues. These extended wait times can frustrate customers and pose challenges to cafe management. To address this issue, we conducted a comprehensive eval___uation and optimization of the service system at a Samarinda cafe using the Erlang distribution queuing system. Primary data was meticulously collected over six days, amounting to a total of 12 hours of observation. Kolmogorov-Smirnov distribution fitting tests were employed, revealing that customer service times adhered to an exponential distribution. The average customer arrival rate was determined to be 0.351 per minute, while the average service time was calculated at 5.546 minutes per customer. Our analysis confirmed that the system operates in a steady state with a utility value of 0.06, indicating sufficient service capacity to handle the current customer load. Therefore, the study concludes that the cafe's service system is currently optimal.

Effective Energy Efficiency under Delay-Outage Probability Constraints and F-Composite Fading.

The paradigm of the Next Generation cellular network (6G) and beyond is machine-type communications (MTCs), where numerous Internet of Things (IoT) devices operate autonomously without human intervention over wireless channels. IoT's autonomous and energy-intensive characteristics highlight effective energy efficiency (EEE) as a crucial key performance indicator (KPI) of 6G. However, there is a lack of investigation on the EEE of random arrival traffic, which is the underlying platform for MTCs. In this work, we explore the distinct characteristics of F-composite fading channels, which specify the combined impact of multipath fading and shadowing. Furthermore, we evaluate the EEE over such fading under a finite blocklength regime and QoS constraints where IoT applications generate constant and sporadic traffic. We consider a point-to-point buffer-aided communication system model, where (1) an uplink transmission under a finite blocklength regime is examined; (2) we make realistic assumptions regarding the perfect channel state information (CSI) available at the receiver, and the channel is characterized by the F-composite fading model; and (3) due to its effectiveness and tractability, application data are found to have an average arrival rate calculated using Markovian sources models. To this end, we derive an exact closed-form expression for outage probability and the effective rate, which provides an accurate approximation for our analysis. Moreover, we determine the arrival and required service rates that satisfy the QoS constraints by applying effective bandwidth and capacity theories. The EEE is shown to be quasiconcave, with a trade-off between the transmit power and the rate for maximising the EEE. Measuring the impact of transmission power or rate individually is quite complex, but this complexity is further intensified when both variables are considered simultaneously. Thus, we formulate power allocation (PA) and rate allocation (RA) optimisation problems individually and jointly to maximise the EEE under a QoS constraint and solve such a problem numerically through a particle swarm optimization (PSO) algorithm. Finally, we examine the EEE performance in the context of line-of-sight and shadowing parameters.

A deep learning technique to detect distributed denial of service attacks in software-defined networks

Software-Defined Network (SDN) is an established networking paradigm that separates the control plane from the data plane. It has central network control, and programmability facilities, therefore SDN can improve network flexibility, management, performance, and scalability. The programmability and control centralization of SDN have improved network functions but also exposed it to security challenges such as Distributed Denial of Service (DDoS) attacks that target both control and data planes. This paper proposes an effective detection technique against DDoS attack in SDN control plane and data plane. For the control plane, the technique detects DDoS attacks through a Deep Learning (DL) model using new features extracted from traffic statistics. A DL method (AE-BGRU) for DDoS detection uses Autoencoder (AE) with Bidirectional Gated Recurrent Unit (BGRU). The proposed features for the control plane include unknown IP destination address, packets inter-arrival time, Transport layer protocol (TLP) header, and Type of service (ToS) header. For the data plane, the technique tracks the switch's average arrival bit rate with an unknown destination address in the data plane. Then, the technique detects DDoS attacks through a DL-based model which also uses AE with BGRU. The proposed features in the data plane include the switch's stored capacity, the average rate of packets with unknown destination addresses, the IP Options header, and the average number of flows. The dataset is generated from feature extraction and computations from normal and attack packets and used with the classifier. Also, additional Machine Learning (ML) methods are used to enhance the detection process. If the model detects an attack, the technique mitigates DDoS effects by updating the user's trust value and blocking suspicious senders based on the trust value. The experimental results proved that compared to related techniques, the suggested method had a higher accuracy and lower false alarm rate.

Spreading Factor and Coding Rate Allocation Method for LoRa Network

Aiming at the problems of frequent packet loss and high energy consumption in large-scale LoRa networks, this paper proposes a joint allocation method of Spreading Factor (SF) and Coding Rate (CR). Firstly, we establish the effect relationship of SF and CR on Frame Error Rate(FER), and pre-allocate SF and CR based on the node position to minimize the energy consumption of the node while meeting the FER requirement. Then, according to the collision model, the collision probability of each SF group is calculated, and the sequential water injection method is used to equalize the collision probability within each SF group, to improve the average packet arrival rate of the entire network. The simulation results show that compared to mainstream algorithms, the proposed algorithm obtains a 13% increment in terms of the network average packet success probability, and a 104% one in terms of the average energy efficiency. The proposed algorithm has high application value in many application scenarios such as smart agriculture and smart cities.

An investigation into the optimal allocation of resources for China's universities to participate in global education governance based on queuing theory

Abstract Queuing theory is used to classify the regularity of educational resource allocation by hierarchy in this paper. The Poisson distribution is used to label the resource queue interval parameters to obtain the expected value of individual queues. The average delay of each class of packets can be reasonably predicted by concatenating individual resources using the Lagrange multiplier algorithm. The load balancing algorithm in each queueing series calculates the average arrival rate of task requests to achieve an optimal model design. The results show that, under the guidance of queuing theory, the coefficient of variance in the participation of Chinese university students in global education governance is reduced by 0.25% on average, and the average of comprehensive efficiency reaches about 1.01%, which realizes the optimal and reasonable allocation of resources.

Additional flight delays and magnetospheric–ionospheric disturbances during solar storms

Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called solar storm or space weather. It is known that aviation activities can be affected during solar storms, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~ 4 × 106 records), for the first time, we quantitatively investigate the flight delays during space weather events. It is found that compared to the quiet periods, the average arrival delay time and 30-min delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such correlation. Further studies show that the flight delay time and delay rate will monotonically increase with the geomagnetic field fluctuations and ionospheric disturbances. These results indicate that the interferences in communication and navigation during space weather events may be the most probable reason accounting for the increased flight delays. The above analyses expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications.

An Advanced Hierarchical Identity-Based Security Mechanism by Blockchain in Named Data Networking

Named data networking (NDN) has been viewed as a promising future Internet architecture due to its data-centric design. It requires a new security model that is orienting data but not devices. In this paper, an advanced hierarchical identity-based security mechanism by blockchain (AHISM-B) is to be proposed for the NDN networks. On one hand, the hierarchical identity-based cryptology is used to bind the data name to a public key. The valid public parameters would be requested by consumers with the Interest packets so that consumers would compose producers’ public keys to authenticate producers and verify the integrity of the Data packets. On the other hand, a blockchain is employed to manage public parameters to avoid catastrophes due to a single node failure. Both of the security proof result and the formal validation result indicate that the proposed AHISM-B is secure. Moreover, the simulation results show that the performance of our AHISM-B outperforms that of the classic NDN scheme. Especially, the average response delay of the AHISM-B scheme is less by 8% than that of the classic NDN scheme. With the increase of the average arrival rate of Interest packets, the advantage of the AHISM-B could be enhanced further to 11%.

Optimising acute stroke pathways through flexible use of bed capacity: a computer modelling study

BackgroundOptimising capacity along clinical pathways is essential to avoid severe hospital pressure and help ensure best patient outcomes and financial sustainability. Yet, typical approaches, using only average arrival rate and average lengths of stay, are known to underestimate the number of beds required. This study investigates the extent to which averages-based estimates can be complemented by a robust assessment of additional ‘flex capacity’ requirements, to be used at times of peak demand.MethodsThe setting was a major one million resident healthcare system in England, moving towards a centralised stroke pathway. A computer simulation was developed for modelling patient flow along the proposed stroke pathway, accounting for variability in patient arrivals, lengths of stay, and the time taken for transfer processes. The primary outcome measure was flex capacity utilisation over the simulation period.ResultsFor the hyper-acute, acute, and rehabilitation units respectively, flex capacities of 45%, 45%, and 36% above the averages-based calculation would be required to ensure that only 1% of stroke presentations find the hyper-acute unit full and have to wait. For each unit some amount of flex capacity would be required approximately 30%, 20%, and 18% of the time respectively.ConclusionsThis study demonstrates the importance of appropriately capturing variability within capacity plans, and provides a practical and economical approach which can complement commonly-used averages-based methods. Results of this study have directly informed the healthcare system’s new configuration of stroke services.

Netizens’ risk perception in new coronary pneumonia public health events: an analysis of spatiotemporal distribution and influencing factors

BackgroundInternet search volume reflects the level of Internet users’ risk perception during public health events. The Internet search volume index model, an algorithm of concentration of Internet users, and statistical analysis of popular topics on Weibo are used to analyze the effects of time, space, and space-time interaction. We conducted in-depth research on the characteristics of the spatial and temporal distribution of Internet users’ risk perceptions of public health events and the associated influential factors.MethodsWe analyzed the spatiotemporal distribution characteristics of Internet users’ risk perception after the Wuhan “city closing” order during the coronavirus disease 2019 (COVID-19) pandemic. We established five linear regression models according to different time periods and analyzed factors influencing Internet users’ risk perception by employing a Poisson and spatial distribution and topic modeling analysis.ResultsEconomy, education, health, and the degree of information disclosure affect Internet users’ risk perception significantly. Internet users’ risk perception conforms to the exponential distribution law in time and has periodic characteristics and stability trends. Additionally, Internet users’ average arrival rate dropped from week 1 to week 8 after the “city closing.” Internet users’ risk perception has a uniform distribution in space, economic and social development level distribution consistency, spatial agglomeration, and other characteristics. The results of the time-space interaction show that after 8 weeks of COVID-19, Internet search hot topics have become more stable, and Internet users’ information demand structure has become more rational.ConclusionsThe Internet search cycle of the COVID-19 event is synchronized with the evolution cycle of the epidemic. The physical risk of Internet users is at the top of the risk structure, focusing on the strong concern about the government’s ability to control COVID-19 and its future trend. The government should strengthen network management; seize the risk control focus of key time nodes, regional locations, and information content of online communication; actively adjust the information content supply; effectively control the rebound of Internet users’ risk perception; establish a data-driven, risk-aware intelligence system for internet users; and guide people to actively face and overcome the potential risks and threats of COVID-19.

A Mining Strategy for Minimizing Waiting Time in Blockchains for Time-Sensitive Applications

Blockchain, as proposed in Bitcoin, focuses on securing financial transactions. However, in recent years, the use of blockchain has expanded to a wide range of networks and application domains. This includes time-sensitive applications which need transactions to be processed fast enough to meet delay requirements. Reducing the transaction waiting time in the mining process is key to the successful adoption of blockchain in such applications. In this paper, we propose a mining strategy that is aimed at minimizing the average waiting time per transaction by ensuring a certain minimum required block size, based on the average transaction arrival rate, mining service rate, and maximum block size. We derive an expression for the average transaction waiting time of the proposed mining strategy and determine the optimal mining rule. Numerical results show that the average waiting time per transaction can be reduced by up to 15 % using the proposed mining strategy compared to the traditional strategy in which miners immediately start the next mining round using the transactions already waiting in the pool.

Back to basics: fundamental principles of system dynamics and queueing theory

Back to basics: fundamental principles of system dynamics and queueing theory

Data Transmission in SAGIN with FANET/MANET of Drones

The purpose of this article is to simulate data transmission and calculate traffic parameters in SAGIN air segment for which Ad Hoc network of flying drones is considered as a model. Traffic modeling is based on the manet-routing-compare example from the ns3 simulator library, which has been supplemented with code to calculate packet loss, throughput, and message transmission delays. The program allowed considering drones movement at both low and high speeds from 3.6 km/h to 72 km/h. The dependences of traffic losses on data transmission power, transaction sizes and data transmission speed are obtained and analyzed. The distribution of the average effective arrival rate λ and the throughput/goodput for drones has been studied. Comparing traffic characteristics in models with different numbers of drones allows to judge how the required quality of service can be achieved by choosing the right transmission parameters.

Analysis of Ship Waiting Time to Get Shipyard Service at PT. Dokp And Shipping Waiame in Ambon City

The purpose of this research is to determine the best service system that can be applied by PT. Dokp and Shipping Waiame Ambon. so that the waiting time can be pressed. This research is descriptive research, with the population in this study is the entire number and type of ships that have docked at PT. DOKP and Waiame shipping for 13 months. The sampling method used in this research is Convenience Sampling. The sample used in this study is the number of ships repaired in a period of up to 13 months. The analysis tool used is queueing theory of multi channel-single phase model. The results showed that the average arrival rate of ships per month is as many as 9 units of ships, while the average length of docking as much as 3 units of ships per month. Therefore, it can be seen that the level of service is not optimal to reduce the arrival rate of ships, and the chance of idleness by 2. Therefore, the arrival of ships is more than the level of ship service at PT. Dokp and Shipping Waiame Ambon.

Analisis Markov Chain, Quene Problem dan Decision Support System pada PT.UVW dan PT.XYZ

Every customer wish to go to a workshop always consider in selecting the official workshop or the public. The high level of the intensity of the queue and the service provided make the customer consider in determining service for the owned vehicle. The purpose of this study is to determine the market share of the authorised and general. In addition the right strategy to attract customers so it remains to choose the workshop. Then the method that will be used gradually, starting from the markov chain, quene problem and the decision support system. To quene problem using the model multiple channel query system and decision support sytem using simple additipe weighting method. The results obtained gradually, starting from the market share for a period of 1 to 3 by 60% and 40% as a result of markov chain. While the results of the quene problem obtained the average arrival rate of 4 customers with a level of service of 1.02/h and waiting time for 30 minutes. As for the decision support system with a total labor cost to 5 mechanical man Rp.20.800/h and obtained the cost of waiting for customer Rp.79,000/day

Performance evaluation for Robotic Mobile Fulfillment Systems with time-varying arrivals

This paper analyzes the performance of Robotic Mobile Fulfillment Systems with a semi-open queueing network (SOQN) with time-varying arrivals. We use a Markov-modulated Poisson process to characterize the variability in the order arrival rate and offer a matrix-geometric method (MGM)-based approach to solve the SOQN. Simulations are used to validate the analytical model. We verify that assuming time-homogeneous order arrivals will underestimate order throughput time. Numerical experiments are also conducted to explore the effect of peak order arrival rates, peak order arrival durations, and average order arrival rates on system performance. The results show that peak order arrival rates and peak order arrival durations have the most significant impact on order throughput time. Moreover, we compare the order throughput time of various workstation layouts with different width-to-length ratios.

Simulation of non-homogeneous Poisson process of customer arrival in stereo garage

This paper studies the customer arrival process of stereo garage, considering both the Poisson process and non-homogeneous Poisson process for customer arrival. The relationship between average arrival rate and arrival time is obtained by regression analysis. By simulating different arrival processes, K-S test results are used to determine whether they belong to the same distribution as that of the actual arrival process. According to the actual operation process of stereo garage, two kinds of simulation programs under the arrival process are compiled. The simulation results show that the actual arrival process of customers in the garage is a non-homogeneous Poisson process. For the garage system with non-homogeneous customer source, non-homogeneous property is one of the reasons affecting its operation efficiency. This paper gives a reference for simulating the customer arrival process of such garage operation, and provides a new idea for improving the operation efficiency of the garage.

BS-HTIS: Buffer Sizing for Heterogeneous Traffic and Integrated System

Buffer sizing for switching and routing devices is of significance for guaranteeing the Quality of Service (QoS) of critical services on the Internet of Things (IoT), continuously evolving scheduling mechanisms and complex traffic characteristics pose new challenges for the traditional method of static buffer sizing based on rule-of-thumb. In this paper, the scope of buffer sizing is extended from a basic scheduling system under homogeneous arrival traffic input to an integrated scheduling system under heterogeneous arrival traffic input which is more ubiquitous. In this context, Voices, videos and other heterogeneous data in the IoT are categorized into short-range-dependent (SRD) and long-range-dependent (LRD) traffic, and the integrated scheduling system is decomposed into single-server-single-queue (SSSQ) systems by not only decoupling the complex dependencies among heterogeneous traffic inputs but also taking the impact of SRD and LRD traffic burstiness on the buffer sizing into account. On this basis, expressions for the relation between the minimum buffer size and the maximum overflow probability are presented. The numerical analysis results and simulation analysis results reveal that the average arrival rate, traffic burst level and scheduling priority are positively correlated with the required buffer size, and once the overflow probability is set, the minimum buffer size can be determined correspondingly. The achievements of this paper will provide theoretical guidance for IoT manufacturers and technicians to set buffers more reasonably and use resources more efficiently.

F4Tele: FSO for data center network management and packet telemetry

The proliferation of bandwidth-hungry applications and services forces datacenter (DC) administrators to optimize the utilization of available resources. Precisely, the network share of management traffic (NMT) has grown significantly because DC networks are becoming more sophisticated and require a massive amount of data for efficient debugging and troubleshooting. Accordingly, we use free space optics communication (FSO) with wavelength division multiplexing (WDM) technology to build a flexible yet high-performance logical network responsible for NMT. The FSO-WDM can provide reconfigurable multi-terabit topology over indirect line-of-sight (LoS) links. Due to space and processing capacity reasons, we cannot offer direct connections from every NMT source to the network management cluster. Alternatively, the NMT sources are grouped together as each group is serviced for a duration of time matches its average arrival-rate. Since the NMT sources showed different arrival-rates, the hotspot racks are allocated with longer service time. The evaluation results show that F4Tele carried out a throughput 72% of the expensive solution (benchmark).