Download Time Research Articles

FQ-SAT: A fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization

In the 5G and beyond era, multipath transport protocols, including MPQUIC, are necessary in various use cases. In MPQUIC, one of the most critical issues is efficiently scheduling the upcoming transmission packets on several paths considering path dynamicity. To this end, this paper introduces FQ-SAT - a novel Fuzzy Q-learning-based MPQUIC scheduler for data transmission optimization, including download time, in heterogeneous wireless networks. Different from previous works, FQ-SAT combines Q-learning and Fuzzy logic in an MPQUIC scheduler to determine optimal transmission on heterogeneous paths. FQ-SAT leverages the self-learning ability of reinforcement learning (i.e., in a Q-learning model) to deal with heterogeneity. Moreover, FQ-SAT facilitates Fuzzy logic to dynamically adjust the proposed Q-learning model’s hyper-parameters along with the networks’ rapid changes. We evaluate FQ-SAT extensively in various scenarios in both simulated and actual networks. The results show that FQ-SAT reduces the single-file download time by 3.2%–13.5% in simulation and by 4.1%–13.8% in actual network, reduces the download time of all resources up to 20.4% in web browsing evaluation, and reaches percentage of on-time segments up to 97.5% in video streaming, compared to state-of-the-art MPQUIC schedulers.

Planned but ever published? A retrospective analysis of clinical prediction model studies registered on clinicaltrials.gov since 2000

ObjectivesTo describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000. Study Design and SettingObservational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts. ResultsNine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data. ConclusionPublished clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.

Secure pharmaceutical supply chain using blockchain in IoT cloud systems

Supply Chain Management (SCM) systems require time sequencing, coordination and tracking the movement of goods and processes. Internet of Things (IoT) and Blockchain technologies are useful to develop a secure automated SCM. IoT devices with in-built sensors and actuators help to keep track of the state, location, temperature or other parameters of an entity, and control the automation of routine as well as hazardous tasks. Blockchain technology supports time-stamping, authentication, process coordination, non-repudiation, commercial transactions, and also provides security for transactions and storage. The pharmaceutical SCM demands accurate, immediate and secure control system. Additionally, the supply chain process data from IoTs is stored and processed in Cloud by Analytics applications, for business planning and improvement. An efficient and secure IoT-Cloud-Blockchain based system for both SCM automation and analytics has been proposed in this work. It leverages a hierarchical IoT, Mist, Edge, Fog, Cloud computing (IMEFC) architecture to enhance Communication-Response-Compute-Security-Storage (CRCSS) in the system. Blockchain technology provides security for the SCM transactions and data. The efficiency of the Blockchain is measured in terms of upload time, download time and transaction fees for Bitcoin, Ethereum and Filecoin platforms. The Filecoin blockchain platform is quicker and cost-effective for larger file sizes, compared to Ethereum and Bitcoin, making it suitable for Pharmaceutical SCM systems.

Reducing the Memory Footprint of 3D Gaussian Splatting

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a x27 reduction in overall size on disk on the standard datasets we tested, along with a x1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device (see Fig. 1).

Joint In-Orbit Computation and Communication for Minimizing Download Time From LEO Satellites

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

Improving Security on Election Results Data Transmission via Cloud Using Hybrid Homomorphic Encryption

Elections in recent years have become topical issues and characterized by violence and conflicts leading to loss of lives and properties. The integrity and confidentiality of the declared collated results have been questioned by individuals and organisations with keen interest in the outcomes of every election. Different countries have adopted different Election Results Management Systems (RMS) to help present a credible, fair and transparent election results. These systems adopted are not without criticisms and suspicions. This research paper has presented various factors that need to be considered when selecting a Results Management System (RMS) for elections. A cloud-based using a Hybrid homomorphic encryption approach is proposed in managing the election results data security and transmission. The proposed scheme has demonstrated effectiveness in handling data integrity, data confidentiality, data privacy and access control. The proposed scheme presented has enhanced the security of election results data against Chosen Ciphetext Attacks (CCA) and Denial of Service (DoS) as well as other cyber related attacks. The time required for the entire election data encryption, transmission, decryption, upload time and download time has been greatly enhanced with the proposed system. The proposed system workflow algorithm, key generation algorithm, encryption algorithm and decryption have been presented in this research paper. The outcome of the research work indicates that only 0.00078 seconds is required to generate keys for about 100 users. About 0.705 seconds and 0.863 seconds is required for the encryption and decryption of a 500MB election results data. It was observed that the overall election results data transmission time is about 51.779 seconds which is less than one minute (60 seconds) for about 500MB data size of the election results data. This paper makes a case for the adoption and implementation of the proposed system since it performs better in terms of securing the election results data and transmission time in the cloud environment.

Enhancing Crowd-Sourced Video Sharing through P2P-Assisted HTTP Video Streaming

This paper introduces a decentralized architecture designed for the sharing and distribution of user-generated video streams. The proposed system employs HTTP Live Streaming (HLS) as the delivery method for these video streams. In the architecture, a creator who captures a video stream using a smartphone camera subsequently transcodes it into a sequence of video chunks called HLS segments. These chunks are then stored in a distributed manner across the worker network, forming the core of the proposed architecture. Despite the presence of a coordinator for bootstrapping within the worker network, the selection of worker nodes for storing generated video chunks and autonomous load balancing among worker nodes are conducted in a decentralized fashion, eliminating the need for central servers. The worker network is implemented using the Golang-based IPFS (InterPlanetary File System) client, called kubo, leveraging essential IPFS functionalities such as node identification through Kademlia-DHT and message exchange using Bitswap. Beyond merely delivering stored video streams, the worker network can also amalgamate multiple streams to create a new composite stream. This bundling of multiple video streams into a unified video stream is executed on the worker nodes, making effective use of the FFmpeg library. To enhance download efficiency, parallel downloading with multiple threads is employed for retrieving the video stream from the worker network to the requester, thereby reducing download time. The result of the experiments conducted on the prototype system indicates that those concerned with the transmission time of the requested video streams compared with a server-based system using AWS exhibit a significant advantage, particularly evident in the case of low-resolution video streams, and this advantage becomes more pronounced as the stream length increases. Furthermore, it demonstrates a clear advantage in scenarios characterized by a substantial volume of viewing requests.

Efficient content caching for 5G assisted vehicular networks

Traffic congestion is one of the major challenges faced by daily commuters in smart cities. An autonomous transportation system with a 5 G-based Cellular Vehicle-to-Everything (C-V2X) communication system is the solution to meet the traffic challenges faced in smart cities. Vehicular networks provide wireless connectivity to enable a large number of connected vehicle applications. Vehicular networks allow vehicles to share their emergency and infotainment traffic by following vehicle-to-vehicle (V2V) or by using vehicle-to-infrastructure (V2I) communication. The infrastructure of vehicular networks mainly comprises multiple Road Side Units (RSUs). Fog computing nodes are placed adjacent to these RSUs to provide quick access to vehicles. For infotainment traffic, vehicles intend to download their required content from the content provider. Caching the same contents from the nearby fog computing node significantly reduces delay with improved quality of service. As there are millions of contents with varying sizes, caching all demanded contents on these fog nodes is not possible due to their limited caching capacity. In this work, we propose an improved content caching scheme for fog nodes to satisfy vehicles and content providers for fair content placement. The proposed algorithm is based on a modified Gale–Shapley technique that considers factors such as content popularity, vehicle connectivity, and quality of the communication channel to optimize the content caching process. Simulation results show that the proposed technique caches a higher number of popular contents with lower downloading time.

Measuring congestion-induced performance imbalance in Internet load balancing at scale

Internet load balancers typically use congestion-oblivious hashing algorithms to distribute traffic across load-balanced (LB) paths, resulting in imbalanced load distribution. When congestion occurs, LB paths could experience different levels of congestion, called congestion imbalance. Congestion imbalance has been extensively studied in datacenters (DCs). It is, however, still under-explored in the Internet. In this paper, we take a first step towards measuring congestion imbalance among Internet LB paths at scale. We present Congi, our lightweight prober, that leverages support vector machine (SVM) classifiers to efficiently detect congestion imbalance using latency samples. Our experiments show that Congi is capable of detecting congestion imbalance between uncongested and congested LB paths: on average, the uncongested path has 3x greater throughput and 0.4% lower packet loss rate than the congested one. To measure congestion imbalance at scale, we then use Congi to conduct measurement campaigns from worldwide DCs to millions of/24s. We find that most DCs experience significant congestion imbalance to 36%–43% of end hosts. Lastly, we use Congi to direct web page downloads under congestion imbalance from a campus network, and show that the download time can be reduced by 44% on average for Alexa top sites for clients experiencing network congestion under congestion imbalance.

A novel fuzzy programming approach for piece selection problem in P2P content distribution network.

Piece selection policy in dynamic P2P networks play crucial role and avoid the last piece problem. BitTorrent uses rarest-first piece selection mechanism to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. The problem of piece section is multiple objectives. A novel fuzzy programming approach is introduced in this article to solve the multiple objectives piece selection problem in P2P network, in which some of the factors are fuzzy in nature. Piece selection problem has been prepared as a fuzzy mixed integer goal programming piece selection problem that includes three primary goals such as minimizing the download cost, time, maximizing speed and useful information transmission subject to realistic constraints regarding peer's demand, capacity and dynamicity. The proposed approach has the ability to handle practical situations in a fuzzy environment and offers a better decision tool to each peer to select optimal pieces to download from other peers in dynamic P2P network. Extensive simulations are carried out to demonstrate the effectiveness of the proposed model. It is proved that proposed system outperforms existing with respect to download cost, time and meaningful exchange of useful information.

Towards Impact of Chunk-Level Characteristics on Mobile Live Streaming Performance

Today, mobile live streaming is gaining a rapid growth in use, which refers to watching the media content recorded and broadcast in real time on mobile devices. In live streaming process, each video segment must go through recording, encoding, uploading, transcoding, publishing, downloading, decoding before playback. The ingest algorithm inside the streamer decides the upload bitrate, while the adaptive bitrate (ABR) algorithm inside the player determines the download bitrate. Thanks to the chunked CMAF standard, each segment is split into smaller chunks that can be independently encoded, transferred, decoded and played. It is of great help to quantify the impact of chunk-level characteristics on mobile live streaming performance. In this paper, we establish a tandem queuing model to describe the whole streaming system. Based on the model, we respectively characterize rebuffering probability, rebuffering count, streaming latency, and average bitrate, analyzing the impact of chunk upload and download rates, upload and download time variances, startup threshold and chunk length on them. From analysis results, we propose insights and recommendations for bitrate adaptation in mobile live streaming and design simple heuristic ingest and ABR algorithms leveraging them. Extensive simulations verify the insights as well as effectiveness of designed algorithms.

Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models

As cloud computing rises in popularity across diverse industries, the necessity to compare and select the most appropriate cloud provider for specific use cases becomes imperative. This research conducts an in-depth comparative analysis of two prominent cloud platforms, Microsoft Azure and Amazon Web Services (AWS), with a specific focus on their suitability for deploying object-detection algorithms. The analysis covers both quantitative metrics—encompassing upload and download times, throughput, and inference time—and qualitative assessments like cost effectiveness, machine learning resource availability, deployment ease, and service-level agreement (SLA). Through the deployment of the YOLOv8 object-detection model, this study measures these metrics on both platforms, providing empirical evidence for platform evaluation. Furthermore, this research examines general platform availability and information accessibility to highlight differences in qualitative aspects. This paper concludes that Azure excels in download time (average 0.49 s/MB), inference time (average 0.60 s/MB), and throughput (1145.78 MB/s), and AWS excels in upload time (average 1.84 s/MB), cost effectiveness, ease of deployment, a wider ML service catalog, and superior SLA. However, the decision between either platform is based on the importance of their performance based on business-specific requirements. Hence, this paper ends by presenting a comprehensive comparison based on business-specific requirements, aiding stakeholders in making informed decisions when selecting a cloud platform for their machine learning projects.

Enhancing Data Security in Healthcare IoT: An Innovative Blockchain-based Solution

The Internet of Things (IoT) has revolutionized the healthcare industry by enabling the seamless integration of medical devices, sensors, and data-driven applications. However, the large influx of sensitive healthcare data and the proliferation of linked devices have caused grave worries about data security and privacy. Traditional centralized security systems are unable to handle the changing threats and problems in the IoT healthcare setting. This study suggests a novel strategy for boosting data security in the healthcare industry that makes use of blockchain technology. The main goal of this research is to develop and deploy a trustworthy framework that safeguards private healthcare information in IoT networks. Blockchain, as a distributed and decentralized ledger, offers inherent security features such as immutability, transparency, and cryptographic mechanisms. In this research, it is suggested that healthcare data be gathered via the IoT and stored in the Interplanetary File System (IPFS) using Ethereum-based blockchain technology for data security. The suggested method creates a reliable environment for managing healthcare data by exploiting the special features of blockchain. The json and jpeg files are utilized five times on a distributed database housed on IPFS and a centralized database hosted on Firebase, and the upload and download times are recorded. For IoT-based healthcare systems, we have also investigated the cost and length of time required to implement smart contracts on blockchain platforms like Rinkeby, Binance, and Matic. This research suggests implementing the Blockchain platform in an IoT-based healthcare system to provide data confidentiality, integrity, and accessibility.

Time-Efficient Data Download for Emergency UAV: Joint Optimization of On-Board Computation and Communication Under Energy Constraint

Reducing data download time helps in improving the time efficiency of unmanned aerial vehicle (UAV) for emergency services. While download time can be shortened either by increasing the data download rate, or by reducing the data size via on-board computation, the optimal trade-off between the two parts is still an open question. In this letter, the joint optimization of on-board computation and communication is studied to minimize the download time under realistic energy constraint. To decouple computation from communication, we first derive the exact expressions for the optimal computation frequency and transmit power based on a given computation strategy. As the resulting univariate optimization problem is an implicit one, we develop a Dinkelbach-like algorithm that iterates between solving a simplified explicit parametric problem to find a candidate strategy, and updating the parameter to approach the optima. The proposed scheme reduces at least 33.3% download time compared with conventional strategies, and the advantage becomes more significant under stringent energy constraint.

Sustainable Cross-Regional Transmission Control for the Industrial Augmented Intelligence of Things

Sustainable connectivity between an Augmented Intelligence of Things (AIoT) system and terminal devices can provide a strong guarantee for an enterprise's continuity of production. To ensure sustainable data interaction in emergency situations such as damage to communication infrastructure facilities in a postdisaster period, a pyramidal air–space–ground network model is proposed in this paper, and its transmission performance is investigated. The space network, constructed of geostationary (GEO) satellites and medium and low Earth orbit (MEO and LEO) satellites, is heterogeneously fused with the terrestrial AIoT network to ensure around-the-clock network service for enterprise production. Then, bandwidth–delay product (BDP) analysis is performed for this heterogeneous network. The proposed scheme improves the data transmission efficiency of the network model by mitigating the impact of a long round-trip time (RTT) and improving the congestion window optimization in the case of data loss. The proposed scheme increases the transmission rate in the network model by a factor of 3.72 compared to the traditional transmission scheme in the case of a 5-hop terrestrial AIoT network and has a 3.94-fold advantage in download time.

Communicating the External Beam Radiotherapy Experience (CEBRE) Discussion Guides: Real World Implementation and Evaluation

Communicating about the radiotherapy treatment process can be difficult for patients and healthcare professionals. The "Communicating the External Beam Radiotherapy Experience" (CEBRE) and Communicating the Gynecologic Brachytherapy Experience (CoGBE) discussion guides use graphic narrative to explain external beam radiotherapy and gynecologic high-dose rate brachytherapy, respectively. The CEBRE, CoGBE, and CEBRE en Español guides have been available for free download since 10/2018, 6/2020, and 8/2021, respectively. This study aims to characterize how the guides are being implemented and used in practice. Global downloads of the CEBRE, CoGBE, and CEBRE en Español guides were analyzed. Email addresses submitted at the time of download were used to contact study subjects. Duplicate emails, fake addresses, and expired emails were removed resulting in 500 individuals being invited to participate in the survey. A data collection web application survey was distributed on 1/27/23. Two reminders were sent prior to the survey closing on 2/13/23. The survey addressed how physicians or other radiotherapy team members use the CEBRE guides, where they are used, and how the guides can be improved. Likert-type data (1 = strongly disagree, 5 = strongly agree) are reported as median [interquartile range]. As of 10/16/22, the CEBRE guides (English) had been downloaded 602 times, CoGBE 86 times, and CEBRE en Español 63 times. 42/500 individuals responded to the survey for an 8% response rate. 40/42 (95%) of participants were healthcare providers with 35/40 (87.5%) radiation oncologists. 16/40 (40%) have used the CEBRE guides in some way. 6/16 (37.5%) "always use," 2/16 (12.5%) "often use," and 3/16 (18.8%) "sometimes use" the guides during consultation. 8/16 (50%) report using the guides for education of medical students, residents, and fellows. The 16 respondents that use the guides felt the guides were useful overall (5[5-5]), felt "very confident" using the guides (4.5[4-5]), and felt the guides "help patients prepare for their treatment" (5[4-5]). 40 respondents provided suggestions on additional language translations with 19/40 (48%) recommending translation into Mandarin, 6/40 (15%) into Cantonese, Portuguese, French and Arabic, and 5/40 (12.5%) into Haitian, Hindi, and Vietnamese. The majority of respondents did not use the CEBRE guides which may be due to difficulty with implementation. However, respondents who use the guides are confident using them and rate them as useful for patients. These data, although limited by the low response rate, suggest that the CEBRE guides have accomplished the goal of facilitating patient-physician conversations. Moreover, there is an interest in translating the guides into additional languages other than Spanish. Due to the low response rate, definitive conclusions cannot be made from these data. Therefore, further research such as interviews and focus groups may be beneficial.

A Secure Deduplication Technique for Data in the Cloud

The tremendous growth of digital data in cloud storage systems is a critical issue, as many duplicate data in storage systems cause extra load. Cloud Service Providers (CSPs) often employ Data Deduplication techniques to eliminate redundant data and store only one copy of data to save storage space and reduce transmission costs. Data Deduplication is mostly effective when multiple clients outsource the same data to cloud storage, but it raises security and ownership issues. This paper proposes a secure, Proof of Ownership (PoW)-based Data Deduplication scheme that has a low communication overhead and ensures that only valid cloud clients can download and decrypt ciphertext from cloud storage. The Advanced Encryption Standard (AES) is used as the encryption algorithm in the proposed scheme. It utilizes two modes of AES encryption, namely, Cipher Block Chaining (CBC) and Galois Counter Mode (GCM), with single-threading and multi-threading to upload and download ciphertext between the client and the server to measure the effect of upload and download times. We present a new approach for PoW to reduce communication overhead. PoW enables owners of the same data to prove to the cloud server that they own the data in a robust way. The comparison between CBC and GCM is implemented in a Java environment with two scenarios: single-threading and multi-threading. The simulation results show that AES-GCM with multi-threading is better during the uploading and downloading times

An Optimized AMS Based Cloud Downloading Service with Advanced Caching and Intelligent Data Distribution Mechanism

The popularity of peer-to-peer video content downloading has surged due to diverse content availability and convenient sharing among users. However, scaling systems to accommodate the growing number of users and content items poses a challenge. This research aims to optimize video content downloading in peer-to-peer systems. The objective is to improve performance by developing advanced caching mechanisms, an intelligent data distribution algorithm, and efficient bandwidth resource management. The proposed approach involves implementing innovative caching mechanisms that store frequently accessed content closer to users, reducing download time. An intelligent data distribution algorithm minimizes bottlenecks and maximizes download speeds. Efficient bandwidth resource management ensures fair allocation. Results demonstrate significant enhancements in download time and overall system performance, leading to improved user experience. This research addresses the need for an optimized video content downloading system to handle increasing user and content volumes. The findings hold the potential to enhance user experiences, facilitate seamless video sharing, and advance peer-to-peer video content downloading.

Layer Dependency-Aware Learning Scheduling Algorithms for Containers in Mobile Edge Computing

Due to the features of lightweight and easy deployment, the use of containers has emerged as a promising approach for Mobile Edge Computing (MEC). Before running the container, an image composed of several layers must exist locally. However, it has been conspicuously neglected by existing work that task scheduling at the granularity of the layer instead of the image can significantly reduce the task completion time to further meet the real-time requirement and resource efficiency in resource-limited MEC. To bridge the gap, considering the complex dependency between layers and images, a novel layer dependency-aware container scheduling algorithm is proposed to reduce the total task completion time. Specifically: 1) We model the online layer dependency-aware scheduling problem for containers in a heterogeneous MEC, considering the layer download time and task computation time. 2) A policy gradient algorithm is proposed to solve this problem, and the high-dimensional and low-dimensional relations for layer dependencies are extracted with improved action selection. 3) Experiments based on the real-world data trace show that the proposed algorithm outperforms the image-based and layer-based baseline algorithms by 54% and 19% on average, respectively.

Performance Investigation of IP Security Virtual Private Network in Ipv6 Environment using Quality of Service

Different types of data are transmitted through the internet, these data such as Email, HTTP, FTP and VoIP conversation might contain important and sensitive information which can be read or seen by other users. Many attacks such as man in the middle, packet sniffing and phishing are available and used by many attackers, as a way to access the protected information. Based on this fact, security and privacy become a necessity in the field of private network (intranet) and public network (extranet). IP sec VPN is considered to be the strongest technique for protecting packets from any kind of attack. In this paper, an analytical study is accomplished using opnet simulation software to design and analyze the proposed network. The proposed network represents four sites in different cities are communicating using the technique of IPsec VPN, different scenarios are created and studied to investigate the performance of network using IPsec VPN in IPV6 environment with QoS. The investigated metrics were packet end to end delay, jitter, packet delay variation, download and upload response time.