Round Trip Time Research Articles

Utilizing Machine Learning as a Prediction Scheme for Network Performance Metrics of Self-Clocked Congestion Control Algorithm

Congestion Control (CC) is a fundamental mechanism to achieve effective and equitable sharing of network facilities. As future networks evolve towards more complex paradigms, traditional CC methods are required to become more powerful and reliable. On the other hand, Machine Learning (ML) has become increasingly popular for solving challenging and sophisticated problems, and scientists have started to turn their interest from rule-based approaches to ML-based methods. This paper employs machine learning models to construct a performance evaluation scheme to predict network metrics for the Self-Clocked Rate Adaptation for Multimedia (SCReAM) algorithm. It uses a rigorous data preprocessing pipeline and a systematic application of ML methods to enhance the performance of the regression model for SCReAM’s performance metrics. Also, we constructed a dataset that provides SCReAM’s input parameters and output metrics, such as network queue delay, smoothed Round Trip Time (sRTT), and network throughput. Each prediction process has several phases: choosing the best initial regressor model, hyperparameter tuning, ensemble learning, stacking regressors, and utilizing the holdout data. Each model’s performance was evaluated through various regression metrics; this study will mainly focus on the coefficient of determination (R2) score. The improvement between the initial best-selected model and the final improved model determined that we were able to increase R2 up to 96.64% for network throughput, 99.4% for network queue delay, and 100% for sRTT.

A Round Trip Time Weighting Model for One-Way Delay Estimation

A Round Trip Time Weighting Model for One-Way Delay Estimation

Count-Free Single-Photon 3D Imaging with Race Logic.

Single-photon cameras (SPCs) have emerged as a promising new technology for high-resolution 3D imaging. A single-photon 3D camera determines the round-trip time of a laser pulse by precisely capturing the arrival of individual photons at each camera pixel. Constructing photon-timestamp histograms is a fundamental operation for a single-photon 3D camera. However, in-pixel histogram processing is computationally expensive and requires large amount of memory per pixel. Digitizing and transferring photon timestamps to an off-sensor histogramming module is bandwidth and power hungry. Can we estimate distances without explicitly storing photon counts? Yes-here we present an online approach for distance estimation suitable for resource-constrained settings with limited bandwidth, memory and compute. The two key ingredients of our approach are (a) processing photon streams using race logic, which maintains photon data in the time-delay domain, and (b) constructing count-free equi-depth histograms as opposed to conventional equi-width histograms. Equi-depth histograms are a more succinct representation for "peaky" distributions, such as those obtained by an SPC pixel from a laser pulse reflected by a surface. Our approach uses a binner element that converges on the median (or, more generally, to another k-quantile) of a distribution. We cascade multiple binners to form an equi-depth histogrammer that produces multi-bin histograms. Our evaluation shows that this method can provide at least an order of magnitude reduction in bandwidth and power consumption while maintaining similar distance reconstruction accuracy as conventional histogram-based processing methods.

SCALABILITY IMPROVEMENT OF ACTIVE PROBING FOR FAILURE DETECTION IN LARGE-SCALE SOFTWARE DEFINED NETWORKS USING COMMUNITY DETECTION ALGORITHMS

Software-Defined Networking (SDN) is a networking approach that separates the network's control plane and data plane to provide better network control. The nature of SDN requires an essential focus on its reliability and availability, which includes failure detection aspects. Several techniques have been proposed to enhance SDN failure detection, including passive and active approaches. However, the Route Inspection (RI) algorithm, an example of the active probing method in active detection, has scalability issues when deployed in large-scale networks as it generates inefficient and lengthy probe paths. To address this, we propose using community detection (CD) algorithms to improve the probe path generation of the RI algorithm, resulting in the 'Failure Detection using Community Detection and probing (FDCD)' framework. With the aim to reduce travel paths, the proposed framework minimizes the network topology using CD algorithms to reduce the topology size into smaller communities. Then, the RI algorithm calculates the probe path in the communities instead of the single large topology, thus improving the efficiency of the failure detection mechanism. This paper adopts three community detection algorithms: Louvain, Label Propagation, and Girvan-Newman. We evaluate the framework using three well-known network topologies, COST266, PIORO40, and GERMANY50, in Mininet to observe the framework's performance. The results indicate that the proposed approach improves the algorithm's scalability against its baseline approach, improving the average probe round trip time by 66.87% and the average path installation time by 2.67%.

Optimizing Vertical Transportation in Buildings: Strategies for Efficiency, Safety, and User Experience

The design and planning of vertical transportation systems within buildings are critical components that profoundly impact the overall functionality, safety, and user experience. As urban landscapes evolve, with the construction of taller and more complex structures, the demand for efficient vertical mobility solutions becomes increasingly imperative. This abstract delves into key strategies for optimizing vertical transportation within buildings, encompassing considerations such as traffic analysis, smart systems, energy efficiency, and user experience. Key Words: vertical transportation, lifts, escalator, Round trip time, Waiting time, Rated speed.

Named Multipath Depth-First Search: An SDN-based Routing Strategy for Efficient Failure Handling and Content Delivery in NDN

Information-centric networking (ICN) architectures, such as named data networking (NDN), have emerged as potential solutions for efficiently retrieving and delivering content. However, challenges remain regarding routing scalability, resilience, and caching efficiency. Software-defined networking (SDN) offers opportunities to optimize NDN implementations through centralized control and programmability. In this paper, we propose Named Multipath DFS, an SDN-based routing and caching scheme for NDN networks. NMDFS leverages a centralized controller to pre-compute multipath routes and implement coordinated caching. We evaluate NMDFS on an emulated topology testbed against default NDN and Named-data link state routing. The results demonstrate significant improvements with NMDFS, reducing overhead signalling costs by 94% and 78%, respectively, compared with other schemes. Round-trip latencies for content retrieval were reduced by up to 98%. The SDN controller’s global network view and control are leveraged to optimize content caching through packet loss-driven adaptation and eliminate redundant messaging, leading to substantial performance gains.

Satellite-Assisted Disrupted Communications: IoT Case Study

In recent years, the space industry has witnessed a resurgence, characterized by a notable proliferation of satellites operating at progressively lower altitudes, promising extensive global coverage and terrestrial-level data transfer speeds, while remaining cost-effective solutions. In particular, Wireless Sensor Networks (WSNs) can benefit from the wide coverage of space infrastructure due to their extensive deployment, disrupted communication nature, and the potential absence of terrestrial support. This study explored the utility of Low-Earth Orbit (LEO) satellite constellations as a communication infrastructure for interconnecting “smart” devices via ground stations in Internet of Things (IoT) scenarios. To this end, we designed and implemented a series of experiments conducted within the OMNeT++ simulator, utilizing an updated iteration of the original Open Source Satellite Simulator (OS3) framework. Our research encompassed an IoT Case Study, incorporating authentic sensor data sourced from the Smart Santander testbed. Throughout our experimentation, we investigated the impact of the constellation design parameters such as the number of satellites and orbital planes, as well as the inter-satellite link configuration on the obtained Round-Trip Time (RTT) and packet loss rates.

Non-contact sensing for anomaly detection in wind turbine blades: A focus-SVDD with complex-valued auto-encoder approach

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view – cross sectional analysis – of these complex structures during assembly and curing. In this paper, we enhance the quality assurance of WTB manufacturing utilising FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterises an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application. The code and dataset will be made publicly available. The code and dataset are available here 11https://github.com/FrusqueGaetan/Focus_SVDD_EAD.

Vliyanie nagreva na generatsiyu i svoystva platikonov v vysokodobrotnykh opticheskikh mikrorezonatorakh

Pumping a high-Q optical microresonator by an external laser is inevitably associated with thermal effects. They have a significant impact on the dynamics of nonlinear processes in such structures, including the generation of optical frequency combs and dissipative solitons. The generation process and the properties of bright solitons in such heated microresonators with anomalous group velocity dispersion (GVD) have been well studied, and a number of methods have been developed to minimize the effect of thermal processes. However, for dark solitons or platicons excited at normal GVD, these issues have been studied significantly less. In this work, the properties of platicons in heated microresonators are analyzed, and it is shown that in the case of “positive” thermal effects, when the direction of the thermal shift of the resonance frequencies of a microresonator coincides with the direction of the nonlinear shift, the widest high-energy platicons with the duration close to the round trip time in the resonator are stable. In the case of “negative” thermal effects, narrow low-energy platicons remain stable. Moreover, in microresonators with “negative” thermal effects, the interaction between cubic nonlinear and thermal processes can ensure the generation of platicons without special techniques required in other cases.

TCP BBR-n: Increased throughput for wireless-AC networks

Google proposed a new TCP congestion control algorithm (CCA), Bottleneck Bandwidth and Round-trip propagation time (BBR) which has opened up new dimensions in congestion control. BBR tries to operate near Kleinrock’s operating point to avoid excessive queue formation at the bottleneck and to use the link bandwidth optimally. BBR creates a model of the network path by measuring the bottleneck bandwidth and minimum round-trip time (RTT) to maximize the delivery rate and minimize latency. BBR v2 is an updated version of BBR which addresses many shortcomings of the original BBR (BBR v1) such as interprotocol fairness, RTT fairness, and excessive retransmissions. However, BBR v2 has certain limitations in its operation in IEEE 802.11ac (Wi-Fi 5) networks. The default BBR v2 limits the throughput of Wi-Fi 5 and an increased latency has been observed. This is because the Wi-Fi 5 frame aggregation logic is underutilized and fewer frames are being sent to the Wi-Fi 5 interface. In this paper, we have proposed BBR-n (BBR new) which provides better throughput than the generic BBR v2 in the Wi-Fi 5 networks. Real-time experiments were performed over a physical testbed using Flent to confirm that BBR-n achieves over double throughput as compared to generic BBR v2 and reduced latency in networks as compared to pure loss-based variants such as Cubic and Reno.

Plausibility of BBR as CUBIC’S Replacement and proposed improvement to BBR using GENET

Recent advancements in deep reinforcement learning (RL) have opened new avenues for enhancing network congestion control algorithms. Our research builds upon these developments, particularly focusing on the BBR (Bottleneck Bandwidth and Round-trip propagation time) congestion control algorithm. We propose integrating GENET's reinforcement learning framework, a novel training paradigm that has demonstrated success in various network adaptation algorithms, including adaptive video streaming, congestion control, and load balancing. GENET leverages curriculum learning to effectively train RL models by progressively introducing more challenging network environments. This method counters the common pitfalls in RL training, such as suboptimal performance in a wide range of environments and poor generalization in narrowly defined training scenarios. Our approach exploits the strengths of GENET in identifying and emphasizing network conditions where the current RL model underperforms compared to traditional rule-based baselines, thereby facilitating significant improvements. This research aims to demonstrate that applying GENET's methodology to the BBR congestion control algorithm can yield RL policies that surpass both regularly trained RL policies and conventional baselines, thereby advancing the efficiency and reliability of network congestion control.

Reliably Controlling Massive Traffic between a Sensor Network End Internet of Things Device Environment and a Hub Using Transmission Control Protocol Mechanisms

The constant growth of Internet of Things traffic is ensured by the ongoing evolution of the hierarchy of all hardware links of sensor networks. At the same time, the implementation of the Edge computing ideology regulates the complexity of the “first-mile” section (from the sensors array to the peripheral server). Here, the authors suggest paying attention to the growing share of massive traffic from target sensors in the total traffic of the sensors array. This circumstance makes it expedient to introduce an additional link to the peripheral server for summarizing massive traffic from target sensors. The authors present a sensor network end IoT device (SNEIoTD), implemented grounded on a reliable and cheap Raspberry Pi computing platform, as such a link. The introduction of this SNEIoTD makes it possible to reduce the probability of information loss from the critical infrastructure of a smart city and increase the flexibility of controlling the massive traffic of the first mile. In this context, the urgent task is the reliable control of information transfer from the SNEIoTD environment to a hub, which the authors formalize based on Transmission Control Protocol (TCP). This article proposes a mathematical model of the interaction of the main mechanisms of the TCP in the form of a queuing system. As part of this model, a semi-Markov process of an information transfer with a unified speed is selected and its stationary distribution is analytically formalized. A computationally efficient information technology for determining the TCP Window Size is formulated, taking into account the interaction of TCP mechanisms in the process of massive traffic control. Using the example of TCP Westwood+ protocol modification, it is shown that the results of the application of information technology permit increases in the stability of data transfer under the circumstances of increasing Round-Trip Times.

DECC: Achieving Low Latency in Data Center Networks With Deep Reinforcement Learning

Data Center Networks (DCNs) suffer from synchronized bursts for segmentation and aggregation modes, leading to buffer overflows at switches and increasing network delay. To overcome this problem, some congestion control algorithms like DCTCP use Explicit Congestion Notification (ECN) to notify in-network congestion and reduce switch buffer occupancy. However, the traditional Additive Increase Multiplicative Decrease (AIMD) method causes high fluctuation of round-trip time (RTT) in DCNs. Some intelligent congestion control algorithms designed for Internet can achieve great flexibility, but are not applicable in DCNs for a lack of accurate congestion feedback. In this paper, we analyze the deficiencies of utilizing RTT as congestion signals and the applicability of learning algorithms in DCNs. Then, we propose DECC, a smart TCP congestion control algorithm for DCNs, which combines Deep Reinforcement Learning (DRL) with ECN to achieve high bandwidth utilization as well as low queuing delay. DECC fully utilizes precise in-network feedback and formulates several QoS requirements to a multi-objective function. Meanwhile, it decouples cwnd adjustment with DRL decision making to gradually learn the optimal congestion control policy in real-time. We evaluate the performance of DECC in various scenarios. Simulation results show that DECC can reduce the queue length at bottleneck switches by more than 50% compared to DCTCP, while maintaining high bandwidth utilization and reducing Flow Completion Time (FCTs) under burst traffic.

ML-based pre-deployment SDN performance prediction with neural network boosting regression

Software defined networking (SDN) has been proposed as an effective approach to improve network management efficiency and increase network intelligence in various networks. However, configuring and deploying suitable SDN functionalities is challenging. To overcome these obstacles in SDN network deployment, machine learning models have been introduced to monitor and predict the quality of service (QoS) in an SDN-enabled network. However, most existing studies are applicable only to deployed SDN networks with specific network topologies. In this study, a prediction-based SDN network configuration and deployment scheme is proposed for unseen network topologies before the actual deployment of SDN functionalities. In addition, a machine learning-based pre-deployment SDN performance prediction problem is formulated, and a neural network boosting regression model (i.e., NNBoost) is proposed as the solution. Numerical experiments demonstrate that NNBoost outperforms other machine learning algorithms, including deep learning methods, proposed in the literature, on datasets generated with both real-world and synthetic network topologies, compared with random forest (RF), AdaBoost, XGBoost, LightGBM, deep neural network (DNN) and long short-term memory (LSTM). It is found that NNBoost achieves lower root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) results for the maximum and mean values for the Round-Trip Time (RTT), Switch-to-Controller (S2C) traffic, and Controller-to-Controller (C2C) traffic. It is also found that by adding extra training data samples generated with synthetic network topologies, NNBoost achieves a better prediction performance on the test data samples for real-world network topologies.

Enhancing QoS of Telecom Networks through Server Load Management in Software-Defined Networking (SDN)

In the modern era, with the emergence of the Internet of Things (IoT), big data applications, cloud computing, and the ever-increasing demand for high-speed internet with the aid of upgraded telecom network resources, users now require virtualization of the network for smart handling of modern-day challenges to obtain better services (in terms of security, reliability, scalability, etc.). These requirements can be fulfilled by using software-defined networking (SDN). This research article emphasizes one of the major aspects of the practical implementation of SDN to enhance the QoS of a virtual network through the load management of network servers. In an SDN-based network, several servers are available to fulfill users’ hypertext transfer protocol (HTTP) requests to ensure dynamic routing under the influence of the SDN controller. However, if the number of requests is directed to a specific server, the controller is bound to follow the user-programmed instructions, and the load on that server is increased, which results in (a) an increase in end-to-end user delay, (b) a decrease in the data transfer rate, and (c) a decrease in the available bandwidth of the targeted server. All of the above-mentioned factors will result in the degradation of network QoS. With the implementation of the proposed algorithm, dynamic active sensing server load management (DASLM), on the SDN controller, the load on the server is shared based on QoS control parameters (throughput, response time, round trip time, etc.). The overall delay is reduced, and the bandwidth utilization along with throughput is also increased.

Demonstration of 75 km-fiber quantum clock synchronization in quantum entanglement distribution network

The quantum entanglement distribution network, serviced as the communication infrastructure which distributes quantum information among remote users, enables many applications beyond the reach of classical networks. Recently, the applications such as quantum key distribution and quantum secure direct communication, have been successfully demonstrated in the quantum entanglement distribution network. In this article, we propose a multi-user round-trip quantum clock synchronization (QCS) scheme in the quantum network, which can be implemented with one single entangled photon source located at the server. The server distributes the entangled photons to remote multiple users with the wavelength division multiplexing strategy, and each user feeds partial received photons back to the server. The clock difference between the server and each user is calculated from the one-way and round-trip propagation times, which are determined according to the time correlation of entangled photons. Afterwards, the demonstration has been conducted between the server and a user over a 75-km-long fiber link, where the measured clock difference uncertainty is 4.45 ps, and the time deviation is 426 fs with an average time of 4000 s. Furthermore, the proposed QCS scheme is linearly scalable to many users, with respect to user hardware and number of deployed fibers.

Melting-solidification cycle of finned heat storage tank: Optimization of fin structure by response surface method

The impact of the enhanced heat transfer performance of longitudinal fins on the entire process of melting and solidification in a phase change heat storage unit is investigated through experiments and numerical simulations in this paper. The position and structure of the longitudinal fins are optimized by using the response surface method while ensuring that the total volume of the heat storage medium remains unchanged. Studies have identified that some challenging zones in melting and solidification significantly impact the heat transfer of the entire heat storage cycle. Specifically, the challenging zone in melting primarily exists in the lower part of the unit during the charing process, while the challenging zone in solidification is mainly distributed around the unit during the discharging process. Through optimization, the optimized structure (fin spacing is 22.5 mm, fin width is 6.05 mm) can reduce the charging and discharging time by 16.94 % and 45.90 %, respectively. Additionally, the round trip time is significantly reduced by 39.19 %, and the mean heat absorption rate during the melting process is enhanced by 20.28 %. Moreover, the mean heat release rate during solidification is enhanced by 80.23 %.

Experimental Analysis and Optimization Approach of Self-Clocked Rate Adaptation for Multimedia Congestion Control Algorithm in Emulated 5G Environment.

The congestion problem has driven many researchers to address it, among other networking issues. In a packet-switched network, congestion is essential; it leads to a high response time to deliver packets due to heavy traffic, which eventually causes packet loss. Hence, congestion control mechanisms are utilized to prevent such cases. Several interesting algorithms are proposed to focus on this dilemma, such as the Self-Clocked Rate Adaptation for Multimedia (SCReAM) designed for interactive real-time video streaming applications. One of the main issues of SCReAM is the high design complexity due to the large size of its documentation and coding. Furthermore, there is a considerable number of parameters that can be adjusted to accomplish the desired performance. This study proposes a guided parameters' tuning approach to assess and optimize the SCReAM algorithm in an emulated 5G environment through a detailed exploration of its parameters. The proposed approach consists of three phases, namely, the initialization phase, the standalone experimentation phase, and the hybrid experimentation phase. In the first phase, we illustrate the method of initializing and implementing the environment, followed by specifying the investigated parameters' settings, testing, and validation. The second phase aims to investigate SCReAM parameters in isolation to identify the effect on the performance in relation to network queue delay, smoothed Round Trip Time (sRTT), and throughput. The final phase discusses the possibility of achieving the optimum performance by combining various sets to provide researchers with clear and explicit guidelines to establish an adequate SCReAM behavior for the desired application. To the best of our knowledge, this is the first study that proposes a preliminary and comprehensive analysis of the SCReAM algorithm. Based on the proposed approach, when L4S/ECN is disabled, we reduced the network queue delay by 63.36% and increased the network throughput by 48.6% as compared to the results generated by the original design. In L4S/ECN-enabled mode, the network queue delay is reduced by 16.17% while the network throughput increased by 93%.

The Role of Telehealth in Promoting Equitable Abortion Access in the United States: Spatial Analysis.

Even preceding the Supreme Court's 2022 Dobbs v. Jackson Women's Health Organization decision, patients in the United States faced exceptional barriers to reach abortion providers. Abortion restrictions disproportionately limited abortion access among people of color, young people, and those living on low incomes. Presently, clinics in states where abortion remains legal are experiencing an influx of out-of-state patients and wait times for in-person appointments are increasing. Direct-to-patient telehealth for abortion care has expanded since its introduction in the United States in 2020. However, the role of this telehealth model in addressing geographic barriers to and inequities in abortion access remains unclear. We sought to examine the amount of travel that patients averted by using telehealth for abortion care, and the role of telehealth in mitigating inequities in abortion access by race or ethnicity, age, pregnancy duration, socioeconomic status, rural residence, and distance to a facility. We used geospatial analyses and data from patients in the California Home Abortion by Telehealth Study, residing in 31 states and Washington DC, who obtained telehealth abortion care at 1 of 3 virtual abortion clinics. We used patients' residential ZIP code data and data from US abortion facility locations to document the round-trip driving distance in miles, driving time, and public transit time to the nearest abortion facility that patients averted by using telehealth abortion services from April 2021 to January 2022, before the Dobbs decision. We used binomial regression to assess whether patients reported that telehealth was more likely to make it possible to access a timely abortion among patients of color, those experiencing food insecurity, younger patients, those with longer pregnancy durations, rural patients, and those residing further from their closest abortion facility. The 6027 patients averted a median of 10 (IQR 5-26) miles and 25 (IQR 14-46) minutes of round-trip driving, and 1 hour 25 minutes (IQR 46 minutes to 2 hours 30 minutes) of round-trip public transit time. Among a subsample of 1586 patients surveyed, 43% (n=683) reported that telehealth made it possible to obtain timely abortion care. Telehealth was most likely to make it possible to have a timely abortion for younger patients (prevalence ratio [PR] 1.4, 95% CI 1.2-1.6) for patients younger than 25 years of age compared to those 35 years of age or older), rural patients (PR 1.4, 95% CI 1.2-1.6), those experiencing food insecurity (PR 1.3, 95% CI 1.1-1.4), and those who averted over 100 miles of driving to their closest abortion facility (PR 1.6, 95% CI 1.3-1.9). These findings support the role of telehealth in reducing abortion-related travel barriers in states where abortion remains legal, especially among patient populations who already face structural barriers to abortion care. Restrictions on telehealth abortion threaten health equity.

In-depth Study on RTT-HC-MTT Relationship for Passenger Demand beyond Elevator Contract Capacity by Simulation

The traditional elevator system design practice is to calculate the round trip time (RTT) and associated parameters of pure incoming traffic during up-peak, followed by real-time computer simulation. Recent studies indicated that the normal traffic is much more complicated, consisting of a mixture of incoming, outgoing and interfloor patterns. The Universal RTT, under such complicated traffic patterns, was analytically developed eight years ago based on the concept of an appropriate origindestination matrix describing the passenger transit probability, and verified by Monte Carlo simulation. That model is based on the assumption that the total number of passengers demanding service within one round trip is limited to the elevator contract capacity, which is in line with the traditional up-peak incoming RTT formula. The idea of extending the consideration to beyond the contract capacity was initiated two years ago. In this article, an in-depth study on such consideration is carried out so that the performance such as RTT, handling capacity (HC) and mean transit time (MTT) etc. under different traffic patterns is evaluated and analyzed with the help of Monte Carlo simulation. This article may help designers optimally size an elevator system during the RTT calculation stage without oversizing it if the prevalent traffic patterns of the building are known.