Upload Bandwidth Research Articles

Relative fixed Internet connection speed experiences as antecedents of customer satisfaction and loyalty: An empirical analysis of consumers in Germany

Abstract So far, management practitioners and scholars alike assume that raises in the contractual absolute maximum bandwidth of Internet connections increase the attitudinal bonds of customers with their access provider. The present study broadens this perspective: In a sample of up to 752 German-speaking Internet users with a wireline Internet access at home, it explores two relative speed characteristics of broadband connections as potential determinants of customer affinity with an access provider. The two reference-dependent aspects comprise customer quality experiences in terms of delivered transmission speeds (1) relative to the maximum down- and upload bandwidths as per customer contract, and (2) relative to speeds emphasized in advertisements of a consumer’s access provider. Furthermore, correlations are analyzed between the frequency with which customers use speed tests to check the quality of their wireline Internet connection and their attitudinal bonds with their access provider. Attitudinal bonding is broken down into customer satisfaction with the performance of the current wireline Internet access at home and propensity to churn at the earliest opportunity. Multivariate regression analysis reveals that the impression that advertised wireline access bandwidths are exaggerated compared to the speeds actually delivered and – to a lesser extent – the perception that the contractual maximum speed is reached only in a low share of a customer’s Internet sessions significantly decrease customer satisfaction and significantly increase customer inclination to churn. The effects of the two speed assessments are not independent of one another: Perceived speed exaggerations in a provider’s ads impair customer bonds less (more) strongly if the experienced share of Internet sessions with speeds below the contractual maximum bandwidth is high (low). More frequent speed testing reduces customer bonding, especially in case that advertised speeds of a customer’s provider are experienced as overblown. The results suggest that wireline Internet access providers can increase the attitudinal bonds with their customers by abstaining from emphasizing maximum speeds in their advertising, which customers rarely reach under standard everyday conditions.

Implementation of Bandwidth Management in Cafe Billing Systems Using QR Code Authentication

Seeing the high demand for internet, this research will design an implementation of bandwidth management using a user manager, which will be applied to the billing system in public places with internet services. This aims to regulate the bandwidth or internet bandwidth that will be used by each customer in accordance with the total payment. This implementation utilizes a QR Code which will be used as authentication. This QR Code will be printed on the customer's payment receipt / receipt and will be scanned through the application installed on the android mobile phone. The application will be connected to a server which will later connect the customer's smartphone to connect to the internet service. After making the application, it is tested to check the accuracy of the bandwidth received by the customer. From the bandwidth accuracy testing, the results show that the delivery of bandwidth to the customer has an error with an average error in the download bandwidth of 6.5% and the upload bandwidth of 17%. The occurrence of this error is due to the ping and jitter values ??during data transmission, with an average ping of 78.7 ms and a jitter of 82.7 ms. Whereas in reading the QR Code, it was found that when using a camera with a 5MP resolution the QR Code can be read up to a distance of 90 cm and when using a camera with a resolution of 20.7MP, the QR Code can be read up to a distance of 110 cm.

Optimal Replica Distribution in Edge-Node-Assisted Cloud-P2P Platforms for Real-Time Streaming

In recent years, there have been a number of large-scale deployments of P2P real-time streaming platforms in cloud storage systems due to their fast and convenient storage services for platform designers. Note that providing real-time streaming services for a platform is costly because of heavy bandwidth consumption from the platform to end-users. To cope with the above issue, our idea is to exploit the upload bandwidth of volunteers, namely, edge or fog nodes, to form an edge-node-assisted real-time streaming P2P platform. Specifically, according to designed strategies, the cloud distributes chunk replicas to edge nodes that can respond to end-user requests, and the public cloud supplements end-user demands only when the edge nodes cannot accommodate them. One of the important issues while introducing edge nodes into the system is how to utilize their resources efficiently. However, mainly due to the heterogeneous upload capacity of edge nodes and uneven request rates for different streaming chunks, the replica utilization at the edge nodes is diverse for different replica distribution strategies. Likewise bandwidth consumption in the public cloud varies correspondingly. Thus, this paper describes a mathematical model of the proposed replica distribution and designs an optimal algorithm for distributing chunk replicas. The results validate the effectiveness of the proposal.

Distinguishing bandwidth and latency in households’ willingness-to-pay for broadband internet speed

We measure households’ willingness-to-pay for changes in key home broadband Internet connection features using data from two nationally administered, discrete choice surveys. Both surveys include price, data caps, and download and upload bandwidth, but only one includes latency. Together, these surveys allow us to measure tradeoffs between bandwidth and other connectivity features such as price and data caps, and perhaps most notably, provide the only empirical evidence to date of tradeoffs between bandwidth and latency. We find that households’ valuation of bandwidth is highly concave, with relatively little added value beyond 100 Mbps. For example, households are willing to pay about $2.34 per Mbps ($14 total) monthly to increase bandwidth from 4 Mbps to 10 Mbps, $1.57 per Mbps ($24) to increase from 10 to 25 Mbps, and only $0.02 per Mbps ($19) for an increase from 100 Mbps to 1000 Mbps. We also find households willing to pay about $8.66 per month to reduce latency from levels obtained with satellite Internet service to levels more common to wired service. Household valuation of increased data caps is also concave as caps increase from 300 GB to 1000 GB, although consumers place a significant premium on unlimited service. Our findings provide the first relative valuation of bandwidth and latency and suggest that current U.S. policy may be over-penalizing latency relative to reductions in bandwidth and data caps. For example, we find that in its CAF Phase II Auction, the FCC is imposing a bidding penalty for latency that is about five times higher than what our WTP estimates suggest it should be relative to bandwidth offered.

Distinguishing Bandwidth and Latency in Householdss Willingness-to-Pay for Broadband Internet Speed

We measure households’ willingness-to-pay for changes in key home broadband Internet connection features using data from two nationally administered, discrete choice surveys. Both surveys include price, data caps, and download and upload bandwidth, but only one includes latency. Together, these surveys allow us to measure tradeoffs between bandwidth and other connectivity features such as price and data caps, and perhaps most notably, provide the only empirical evidence to date of tradeoffs between bandwidth and latency. We find that households’ valuation of bandwidth is highly concave, with relatively little added value beyond 100 Mbps. For example, households are willing to pay about $2.34 per Mbps ($14 total) monthly to increase bandwidth from 4 Mbps to 10 Mbps, $1.57 per Mbps ($24) to increase from 10 to 25 Mbps, and only $0.02 per Mbps ($19) for an increase from 100 Mbps to 1000 Mbps. We also find households willing to pay about $8.66 per month to reduce latency from levels obtained with satellite Internet service to levels more common to wired service. Household valuation of increased data caps is also concave as caps increase from 300 GB to 1000 GB, although consumers place a significant premium on unlimited service. Our findings provide the first relative valuation of bandwidth and latency and suggest that current U.S. policy may be over-penalizing latency relative to reductions in bandwidth and data caps. For example, we find that in its CAF Phase II Auction, the FCC is imposing a bidding penalty for latency that is about five times higher than what our WTP estimates suggest it should be relative to bandwidth offered.

DIFFERENT SECURE DATA DEDUPLICATION APPROACHES FOR CLOUD STORAGE: A REVIEW

With the fast growth of the current era, world are moving to digital storage for accomplishment purposes, there is a rising demand for systems that can deliver safe data storage in effective manner. As more corporate and private users outsource their data to cloud storage providers, new data breach incidents make end-to-end encryption progressively prominent obligation. With the growing awareness of data privacy, more and more cloud users choose to encrypt their sensitive data before outsourcing them to the cloud storage. Data deduplication is single instance data storage widely used in cloud storage system to reduce space and upload bandwidth. Duplication-less storage system which de-duplicates the data using file level deduplication and block level deduplication. In this paper we gives an overview of the state of data Deduplication approachesand describes the various application to produce specification according need of current generation.

Analysis of the probability of channel satisfactory state in P2P live streaming systems

In this paper a model based on user behaviour of P2P live streaming systems was developed in order to analyse one of the key QoS parameter of such systems, i.e. the probability of channel-satisfactory state, the impact of upload bandwidths and channels’ popularity on the probability of channel-satisfactory state was also analysed. Results obtained have shown that channels with high number of patronising peers achieve satisfactory state easily while channels with low number of peers hardly attain satisfactory state. This called for the need to design a framework or incentive scheme for effective functionality of P2P live streaming systems which will favour all channels regardless of their popularities. Keywords: P2P Systems, QoS Parameters, Satisfactory State, Video Streaming, Fluid Mode

Анализ времени распространения файладля одноранговой сети

Peer-to-peer (P2P) file sharing systems are responsible for a significant part of the Internettraffic today. File sharing is perhaps the most popular application among P2P applications. Incomparison with traditional Client/Server file distribution, P2P file sharing has some advantages,namely, scalability, bandwidth and others. In this paper we study the minimum distribution timefor getting the entire file by all of the users in the system, who need this file. This parameter isclosely associated with the mentioned bandwidth. The expression for the minimum distributiontime uses fluid-flow arguments and includes such terms as the file size, the upload rates of theseeds and the upload and download rates of the leechers. Using numerical examples and theexpression for the minimum distribution time, we show the efficiency of P2P file sharing. Weconsider the system behaviour, when there are two types of leechers in the system. These typesdiffer from each other by their upload bandwidths.

Detection of File Level and Block Level Deduplication and attacks in Cloud Computing Environment

In cloud computing, security and storage space management techniques are most important factors for improving the performance of cloud computing. Secure deduplication is a technique for eliminating duplicate copies of storage data, and provides security to them. To reduce storage space and upload bandwidth in cloud storage The basic idea in this paper is that we can eliminate duplicate copies of storage data and limit the damage of stolen data if we decrease the value of that stolen information to the attacker. Dekey new construction in which users do not need to manage any keys on their own but instead securely distribute the convergent key shares across multiple servers for insider attacker. User profiling and decoys, then, serve two purposes. First one is validating whether data access is authorized when abnormal information access is detected, and second one is that confusing the attacker with bogus information.

A new mechanism to improve video streaming in P2P networks using helper nodes

Peer-to-peer video on demand (P2P-VoD) is a solution to provide video service to a large number of users on the Internet. Due to the upload bandwidth bottleneck of VoD servers, the streaming capacity in the P2P-VoD system is limited. This paper proposes a new mechanism to improve the video streaming in P2P networks by using the idle peers upload bandwidth and storage, which are called helpers. The proposed method uses an XOR mechanism in the VoD server to encode chunks and reduce bandwidth use. The helpers receive encoded chunks from the VoD server and save them in their storage spaces and cooperate in sharing these chunks with the other peers. Simulation results using OMNET++ are provided to show the efficiency of the proposed mechanism in terms of the required upload capacity requirement of the server and deploying the storage and bandwidth of helpers. The results of simulation show that the upload bandwidth usage of the server has been reduced. On the other hand, the proposed method increases the memory usage of helpers.

A Window-Based, Server-Assisted P2P Network for VoD Services with QoE Guarantees

We describe a Peer-to-Peer (P2P) network that is designed to support Video on Demand (VoD) services. This network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also developed a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters.

Delay Insertion Based P2PTV Traffic Localization Considering Peer's Relaying Capability

Recently, P2PTV is a popular application to deliver video streaming data over Internet network. On the overlay network, P2PTV applications create the logical links between pairs of peers considering round trip time (RTT) without physical network consideration. P2PTV packets are shared over a network without localization awareness which is the serious problem for Internet Service Providers (ISPs). The delay-insertion-based traffic localization scheme was proposed for solving this problem. However, this scheme sometimes leads the newly joining peer to download streaming traffic from a local neighbor peer which has only scarce upload bandwidth. This paper proposes a novel scheme of delay-insertion-based traffic localization in which the router estimates relay capability to each relay peer candidate and leads the newly joining peer to connect to a neighbor peer with sufficient performance for relaying video data. Parameters were evaluated for the optimized condition in the relay capability estimation process. In addition, experiments conducted on a real network show that our proposed scheme can lead the newly joining peer to avoid downloading video data from insufficient relay capability peers and maintain video quality close to normal P2PTV system while efficient traffic localization in level of Autonomous System (AS) network.

A cloud‐assisted publish/subscribe service for time‐critical dissemination of bulk content

SummaryCharacterized by the increasing arrival rate of live content, emergency applications pose a great challenge: how to disseminate data with diverse sizes to interested users in a real‐time manner. Most file sharing applications focus on the dissemination of bulk content with less consideration of users' interests. On the other hand, existing publish/subscribes are designed for notifying interested users with small‐sized content. To bridge this gap, we propose CAPS, a cloud‐assisted publish/subscribe service for time‐critical bulk content dissemination. In CAPS, a hybrid 2‐layer architecture is proposed to knit servers in the cloud and clients in the internet. Through dividing each event into attribute‐value pairs and the data content, CAPS provides both event matching service and data distribution in a parallel manner. To improve the upload bandwidth of data distribution, we propose a helper‐based content distribution protocol, where the servers not only guide the clients with similar interests to exchange their received data blocks but also contribute their own upload capacities to clients. Moreover, a volume‐aware helper renting scheme is proposed to adaptively adjust the scale of servers according to the churn of data volume, leading to a high‐performance price ratio. So as to evaluate the performance of CAPS, about 1000 virtual machines are deployed in our Cloud‐Stack testbed. Extensive experiments confirm that CAPS can linearly reduce the download completion time with the growing number of servers, adaptively adjust the upload capacity in tens of seconds according to the change of the workloads, and ensure reliable data dissemination even if a large number of nodes frequently churn or instantaneously fail. Compared with the state‐of‐the‐art approaches, CAPS demonstrates better performance under various parameter settings.

Obtaining Good Heuristics for Big Data Broadcasting Problem Using Novel Pipeline Approach.

Today big data computing is a critical challenge faced by many industries .Everyday up to terabytes to petabytes of the data is generated in the cloud computing paradigm. Thus there is a drastic demand for building a service to distribute, manage and process massive data sets that has risen tremendously. Engineers and researchers are dealing with these large dataset. In this paper, we investigate the big data broadcasting problem for single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time. These nodes may be present in the same data center or across geo distributed data centers. We model the big data broadcasting problem into a LOCKSTEP BROADCAST TREE (LSBT).The main idea of this model is to define a basic unit of upload bandwidth r, such that a node with capacity c broadcast a data to a set of nodes at the rate r. We divide the broadcast data into m chunks. These data chunks can then be broadcasted using a pipeline approach.

A High-Bandwidth Live Streaming Model in Mesh-Based Peer-to-Peer Networks

Previous works concerning modeling and analysis of mesh-based P2P live streaming mainly focus on the case that the upload bandwidth of ordinary peers is equal to the streaming rate of the streaming server. In this letter, we propose a high-bandwidth P2P live streaming model with two extended streaming schemes. We derive streaming formulas which incorporate multi-peer selection, multi-chunk selection, and heterogeneous streaming modes. We validate our model through simulation, and give in-depth analysis of key parameters.

Capacity of P2P On-Demand Streaming With Simple, Robust, and Decentralized Control

The performance of large-scale peer-to-peer (P2P) video-on-demand (VoD) streaming systems can be very challenging to analyze due to sparse connectivity and complex, random dynamics. Specifically, in practical P2P VoD systems, each peer only interacts with a small number of other peers/neighbors. Furthermore, its upload capacity, downloading position, and content availability change dynamically and randomly. In this paper, we rigorously study large-scale P2P VoD systems with sparse connectivity among peers and investigate simple and decentralized P2P control strategies that can provably achieve close-to-optimal streaming capacity. We first focus on a single streaming channel. Using a simple algorithm that assigns each peer a random set of $\Theta (\log N)$ neighbors and allocates upload capacity uniformly, we show that a close-to-optimal streaming rate can be asymptotically achieved for all peers with high probability as the number of peers $N$ increases. Furthermore, the tracker does not need to obtain detailed knowledge of which chunks each peer caches, and hence incurs low overhead. We then study multiple streaming channels where peers watching one channel may help peers in another channel with insufficient upload bandwidth. We propose a simple random cache-placement strategy and show that a close-to-optimal streaming capacity region for all channels can be attained with high probability, again with only $\Theta (\log N)$ per-peer neighbors. These results provide important insights into the dynamics of large-scale P2P VoD systems, which will be useful for guiding the design of improved P2P control protocols.

The effect of sample rate reduction and audio compression on noise metric accuracy and statistical learning classifier performance

With the increasing availability of low-cost and portable noise monitors, the amount of captured data can reasonably be predicted to grow exponentially. The financial and computational expense of portable data storage, processing hardware, and wireless or cellular upload bandwidth, however, necessitate data compression techniques to ensure feasible continuous operation. Sample rate reduction causes a loss of high frequency components while compressed formats (such as mp3) remove parts of the signal that are inaudible to humans, but may be important to statistical learning algorithms. The degree to which these data reduction techniques degrade the accuracy of metrics calculated from the signals (e.g. peak level, equivalent level, sound exposure level, etc.) and the performance of statistical learning classifiers that use the metrics and/or band levels is examined. Over 20,000 recordings from noise monitors located on military installations are systematically down-sampled and compressed to use in the analysis. The root-mean-square error for each of over 40 noise metrics and the mean decrease in accuracy for a statistical learning classifier are presented.

Modeling and optimization of bandwidth supply performance for cloud-assisted video systems under flash crowd

The large-scale and sudden video content access such as flash crowds results in huge bandwidth demand, which severely influence user quality of experience and quality of service of video systems. In this paper, we firstly discuss the main reason of generation of flash crowds for video streaming services and analyze key factor for balance recovery between supply and demand of upload bandwidth. We construct two models: bandwidth supply capacity model of video systems and bandwidth demand model of users, which measures usage amount of bandwidth of the cloud. Based on the built models, we further employ a community-based cooperative caching strategy of video resources to promote supply capacity of upload bandwidth of video systems. Extensive tests show how the proposed cooperative caching strategy achieves much better performance results in comparison with original solution.

LSBT-A NEW IMPEND FOR BIG DATA BROADCASTING

In contemporary period huge amount of data will be stored on cloud server, these types of data is referred as Big-Data. Big data computing is a complicated issue for broadcasters and engineers because they are dealing with datasets of petabytes in the cloud. To manage process and broadcast these massive data has increased drastically. Here we propose an approach to broadcast the data by large chunks to group of nodes that can reduce the maximum completion time. Here we model a Lock Step Broadcast Tree (LSBT) which will define an upload bandwidth(r) and each node capacity(C) at c/r per children. After these data sets divided into chunks, and are broadcasted in a pipeline manner. In homogeneous network environment the capabilities of each node is C, and uplink rate r which gives less maximum completion time. In heterogeneous networks capabilities C1, C2...Cn, so optimal uplink rate r will be calculated by O (nlog2n) algorithm there after we construct a LSBT. Finally our results show less computational complexity and less maximum completion time over other broadcasting techniques.

Successive Omniscience

Because the exchange of information among all the users in a large network can take a long time, a successive omniscience protocol is proposed. Namely, subgroups of users first recover the information of other users in the same subgroup at an earlier stage called local omniscience. Then, the users recover the information of all other users at a later stage called global omniscience. To facilitate the information exchange, a distributed storage system is used, so that users can conveniently upload and download messages through some reliable central servers. The minimum upload bandwidth is characterized and a bandwidth-storage trade-off is discovered. The results reveal the new connections to the problem of secret key agreement and, consequently, provide meaningful interpretations of a recently proposed multivariate mutual information measure that was inspired by the secret key agreement problem.