Total Download Time Research Articles

Mode-Suppression: A Simple, Stable and Scalable Chunk-Sharing Algorithm for P2P Networks

The ability of a P2P network to scale its throughput up in proportion to the arrival rate of peers has recently been shown to be crucially dependent on the chunk sharing policy employed. Some policies can result in low frequencies of a particular chunk, known as the missing chunk syndrome, which can dramatically reduce throughput and lead to instability of the system. For instance, commonly used policies that nominally "boost" the sharing of infrequent chunks such as the well known rarest-first algorithm have been shown to be unstable. Recent efforts have largely focused on the careful design of boosting policies to mitigate this issue. We take a complementary viewpoint, and instead consider a policy that simply prevents the sharing of the most frequent chunk(s). Following terminology from statistics wherein the most frequent value in a data set is called the mode, we refer to this policy as mode-suppression. We also consider a more general version that suppresses the mode only if the mode frequency is larger than the lowest frequency by a fixed threshold. We prove the stability of mode-suppression using Lyapunov techniques, and use a Kingman bound argument to show that the total download time does not increase with peer arrival rate. We then design versions of mode-suppression that sample a small number of peers at each time, and construct noisy mode estimates by aggregating these samples over time. We show numerically that the variants of mode-suppression yield near-optimal download times, and outperform all other recently proposed chunk sharing algorithms.

A NOVEL MULTIMEDIA FILE SPLITTING TECHNIQUE FOR MEDICAL DATA GRID STORAGE

Nowadays, videos and images dimensions are improved from 1D and 2D images into 3D or 4D (3 spatial dimension + time) images / videos. The improvement for these video data file were also increased the size of the data. Therefore, single and small storage is not enough for storing them. To overcome the problem, grid-based file storage service is introduced. However, the quality-of-service is very important for this applications and service. Therefore, several of quality-of-service requirements such as delay for data transmission, average CPU time per chunk used, and the total download time for a complete video file are need to be investigate. In this paper, our grid-based file storage test bed architecture, and the previous existing multimedia file splitting techniques will be discussed. Besides, our new multimedia file splitting technique – “Exponential-And-Uniform-based Splitting Technique” is also being proposed.

CREATION OF LARGE CATALOGUES BY USING OF VIRTUAL OBSERVATORIES

We developed an application program to search images in the registers and databases of Virtual Observatories and to download them to local computer. The program has the ability to process XML file in VO Table format to generate links to images, as well as to work directly with the astronomical servers. To improve the efficiency for downloading of large number of images, we used multi-threaded mode. The program runs under the Windows operating system. Using the program in 2014 year, we found and downloaded more than 145 thousand of images of open clusters, having total volume of about 300 GB. Total download time was about 7 days. To process the downloaded images, we created and configured a complex of 10 virtual machines on two PCs for parallel image processing by using Astrometrica program. Total processing time was about 14 days. An application program was also created to analyse the obtained results, which were used to create four catalogues of stellar coordinates at the average epoch of 1953 to 1998. The total number of stars in the catalogues is more than 35 million. The standard error is 0.04 to 0.07, and the average number of observations is 4 to 6. The catalogs are used to improve proper motions of stars in and around of open clusters.

Implement Efficient Data Integrity for Cloud Distributed File System Using Merkle Hash Tree

There are various cloud distributed file systems have been proposed in recent years. The users are able to obtain the vast cloud storage spaces through Internet access even if the essence of storage space in portable devices and handsets are restricted and insufficient. In this work, we implement a distributed file system based on an open source named Apache Hadoop, and the original Hadoop file system is replaced with the Tahoe least-authority file system. Furthermore, we investigate the data block size of Merkle hash tree in Tahoe file system, and experiment the total transmission rate and download time with two different file sizes. The experiment result shows that the file system provides a secure and reliable cloud storage service even if parts of storage nodes are broken, and the suggestible block size is found.

Spatial Internet Traffic Load Forecasting with Using Estimation Method

Internet traffic is one of the most unpredictable and fluctuating phenomenon to forecast, although accurate prediction is difficult challenge. Many research about measurement experiments are dedicated to predict the performance of Internet network. Especially, during last years this issue is important, when growing demand on reliable access to the Internet is desired by users. In this paper spatial (temporal-area) Internet traffic load forecasting is proposed. Data are obtained from conducted active measurement experiment. Period of time from which is contained database amounts three weeks of October 2013 and each day at the same time at: 06:00 am, 12:00 pm, 06:00 pm and 12:00 am the data were collected. This experiment relies on download a copy of the same resource from servers located in Europe by Wrocław agent. One of the most interesting variable obtained from this experiment is total download time of indicated resource. On basis of this experiment, the Internet traffic forecasts with one week ahead are performed. Spatial forecasting is made by using geostatistical estimation method - ordinary kriging. Paper contains description of ordinary kriging method and preliminary measurement data analysis. Next, model of forecast with discussion of results are given. The final view of performance considered the Internet network in Europe ending the paper.

ISEFF: An ID-Based Scalable and EFficient Distributed File Sharing Technique in Vehicular Ad Hoc Networks

Using new wireless capabilities in vehicles, various applications have appeared in Vehicular Ad Hoc Networks (VANETs). Multimedia data sharing is one of these applications, which has recently been given so much attention. Most of presented schemes in this field have focused on direct opportunistic meetings between vehicles or request/response broadcast in the whole network, which are inefficient and un-scalable due to VANET characteristics. In this paper, we propose an ID-based Scalable and EFficient distributed File sharing technique that increases efficiency and scalability via taking advantages of BitTorrent protocol and consistent hashing technique. The results of simulations show that our approach increases the received file ratio, reduces total download time of files and message overhead. Consequently, it can enhance efficiency and performance.

Semi-Fluid content distribution model for fast data dissemination in heterogeneous overlay networks

Overlay networks and peer-to-peer networking have emerged as alternative solutions to solve many problems related to massive information distribution and processing tasks by providing enhanced services in the application layer. For instance, overlay networks can improve data dissemination in P2P file sharing overlays and Content Distribution Networks (CDNs). In such overlay networks, delay is considered as a critical performance metric. A viable content distribution model could considerably minimize the completion time and would lead to efficient utilization of resources such as network bandwidth. The main objective of this paper is to design a new flexible content distribution model for fast data dissemination in overlay networks with heterogeneous nodes. The proposed “Semi-Fluid” content distribution model is a hybrid model that enables fast data dissemination in overlay networks by combining Chunk and Fluid content distribution models. In this new content distribution model, the chunk content is distributed into heterogeneous overlay nodes in a fluid manner. The proposed hybrid content distribution model eliminates both a backpressure problem caused by the Fluid content distribution model, as well as a chunk transition delay caused by the Chunk content distribution model. The performance of the proposed Semi-Fluid content distribution model has been evaluated by mathematical analysis as well as by real implementation over the “PlanetLab” platform, and the obtained results show that the Semi-Fluid content distribution model efficiently reduces the total download time of the clients. Hence, the Semi-Fluid content distribution model can be deployed as a promising solution for fast data dissemination in heterogeneous overlay networks.

Robotic data mules for collecting data over sparse sensor fields

AbstractWe present a robotic system for collecting data from wireless devices dispersed across a large environment. In such applications, deploying a network of stationary wireless sensors may be infeasible because many relay nodes must be deployed to ensure connectivity. Instead, our system utilizes robots that act as data mules and gather the data from wireless sensor network nodes. We address the problem of planning paths of multiple robots so as to collect the data from all sensors in the shortest time. In this new routing problem, which we call the data gathering problem (DGP), the total download time depends on not only the robots' travel time but also the time to download data from a sensor and the number of sensors assigned to the robot. We start with a special case of DGP in which the robots' motion is restricted to a curve that contains the base station at one end. For this version, we present an optimal algorithm. Next, we study the two‐dimensional version and present a constant factor approximation algorithm for DGP on the plane. Finally, we present field experiments in which an autonomous robotic data mule collects data from the nodes of a wireless sensor network deployed over a large field. © 2011 Wiley Periodicals, Inc.

Accessing data from many servers simultaneously and adaptively in data grids

In data grid environments, data replication services increase the reliability and the availability of the data sets. Using replicas, a data set can be downloaded from many servers simultaneously in order to improve the performance of the data transfer. However, the servers selected may become slow or congested. Therefore, it should be replaced dynamically. In this paper, we propose a method to improve the efficiency of downloading by reevaluating the conditions of all servers that can supply the dataset during the download progress. Current servers used for downloading, if perform unsatisfactorily, will be replaced by others that meet the performance criteria. In all previous schemes in parallel downloading, the load of an ill performing server will be just transferred to a more powerful server. However, intuitively if there are idle servers around that also have this data, why not use them? Therefore, our method will monitor all servers that can supply the data even if a server is not involved in the download process initially. Loads may be transferred from a working server to a non-working server, not just transferred to another working server. Furthermore, to decide the suitability of a server, we consider not only the connection bandwidth but also the status within a server such as CPU and memory usage. We implement our method in a real grid environment. Our method decreases the completion time by 1.63%–13.45% in the real grid environment and by 6.28%–30.56% in the grid environment with other injected load when compared to the recursive co-allocation scheme. It shows that the proposed scheme adapts to the dynamic environment nicely and decreases the total download time effectively.

Data replication optimisation in grid delivery network

In this paper, we examine the data replication problem in a particular Grid Delivery Network (GDN) which is a system that provides video services, among which is Video On Demand (VOD). In this system, the data are divided into fixed size blocks which must be replicated on hosts to decrease the total download time. We propose a probabilistic model to optimise the average download time of requests based on the host's availability and the document size distribution. The objective function induced by this model is a non-linear integer problem. It can be solved in real values by Lagrangian optimisation. We prove that in a particular case, this problem can be reduced to a knapsack problem. We propose approximation algorithms and validate them using simulations with varying characteristics.

Non-real-time content scheduling algorithms for wireless data networks

A substantial portion of the emerging wireless data service consists of non-real-time applications such as content download. The existing mechanisms based on per-packet performance guarantees used mainly for voice and streaming media do not suffice for the elastic nature of non-real-time traffic. For a non-real-time user data services, the key performance measure of interest is the total download time. In this paper, we propose a novel scheduling framework for wireless content service. Specifically, we present a two-layer scheduling architecture that combines content-aware scheduling with opportunistic scheduling. In terms of content-awareness, the proposed scheduling policy provides guarantees on the download time of content. In the second stage, the instantaneous channel conditions of different users are exploited in an opportunistic manner so as to maximize the throughput of the system. We define service differentiation in two modes - differential and guaranteed - and provide polynomial time algorithms for both that manipulate the stretch ratio but within allowable limits. Extensive simulations are conducted that verify the efficiency of the proposed schemes and provide insights into the behavior of the scheduling algorithms for non-real-time data.

System Delay and Navigational Structure for Web Applications and the Importance of Measuring Workload

Myriad research suggests that delays associated with downloading of pages from server to client can have serious and deleterious consequences for usability. This has understandably led to design guidance suggesting that page download times be kept to a minimum in order to assure an optimum user experience. We argue that there may be some caveats to this rule, particularly when designing long applications, such as those used for data collection by customer service representatives. A series of three studies was conducted to test this assumption. Within each study, download pattern, navigation pattern, and total download time were differentially manipulated. Overall, it was found that an application downloaded in its entirety, rendered as a single page and navigated by scrolling, was subjectively preferred and scored lower on certain subjective workload subscales than applications downloaded incrementally, rendered as multiple pages and navigated by clicking. These results call into question the validity of design guidance focused exclusively on system delay time.