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Design And Implementation Of Download Wizard For Simultaneous Download

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Description

CHAPTER ONE

INTRODUCTIONT

1.1 Background Of Study
In computer networks, to download (abbreviation DL) is to receive data from a remote system, typically a server[1] such as a web server, an FTP server, an email server, or other similar systems. This contrasts with uploading, where data is sent to a remote server.
A download is a file offered for downloading or that has been downloaded, or the process of receiving such a file.
Downloading generally transfers entire files for local storage and later use, as contrasted with streaming, where the data is used nearly immediately, while the transmission is still in progress, and which may not be stored long-term. Websites that offer streaming media or media displayed in-browser, such as YouTube, increasingly place restrictions on the ability of users to save these materials to their computers after they have been received.
Downloading is not the same as data transfer; moving or copying data between two storage devices would be data transfer, but receiving data from the Internet is downloading.

Extensive research over the past several decades has explored many techniques to improve data transfer speed and efficiency. Despite this effort, bulk data transfers often remain slow for a variety of reasons. First, of course, receivers may be bandwidth-limited. Second, the source or sources may be unable to saturate the receiver’s bandwidth. Third, congestion or failures in the “middle” of the network may slow the transfer.
Downloading data from multiple sources simultaneously is a popular technique to speed transfers when the receiver is not the bottleneck. Many peer-to-peer content distribution systems use this idea, for example. These systems employ two basic strategies to locate the sources of data: per-file and per-chunk.
In a per-file system, receivers locate other sources of the exact file they are downloading in O(1) lookups. These systems, exemplified by BitTorrent (B. Cohen, 2003), Gnutella, 2000 and ChunkCast, 2006, typically use a global location service. Unfortunately, the performance of file transfers using these systems is often unacceptably slow, with users requiring hours or even days to download content.
In a per-chunk system, receivers locate sources for individual pieces, or chunks, of the desired file. Since any given chunk in a file might appear in several other files, finding sources for each chunk can yield a much larger number of similar sources. The cost, however, is performing O(N) lookups, one for each of the N chunks in the file the receiver is trying to download. Moreover, such systems also require a mapping between every unique chunk in the identical and similar files and their corresponding sources, i.e., O(N) mappings per object. Examples of per-chunk systems are CFS, 2001 and Shark, 2005.
In this paper, we present Similarity-Enhanced Transfer (SET)—a hybrid system that provides the best of both approaches, locating both identical andsimilar sources for data chunks using O(1) lookups and by inserting O(1) mappings per file. We justify this approach by demonstrating that (a) cross-file similarity exists in real Internet workloads (i.e., files that people are actually downloading on today’s file-sharing networks); (b) we can find similar sources in O(1) lookups; (c) the extra overhead of locating these sources does not out-weigh the benefit of using them to help saturate the recipient’s available bandwidth. Indeed, exploiting similar sources can significantly improve download time.
The three contributions of this paper are centered around these points. First, we present a detailed similarity analysis of 1.7 TB of data fetched from several active file-sharing networks. These data represent a variety of file types, with an emphasis on multimedia files often ignored by previous similarity studies. Our results show that significant cross-file similarity exists in the files that are frequently transferred on these networks. By exploiting this similarity, receivers can locate several times the number of potential sources.
The second contribution of this proposed system is a technique to locate similar sources to the file being downloaded using only O(1) lookups. This technique, which we term handprinting, is a novel use of deterministic sampling. Sources insert a fixed number of hashes into a global database; receivers look up their own set of hashes in this database to find sources of similar files. System-wide parameters determine the amount of similarity receivers can detect (e.g., all files with x% similarity to the target file) and with what probability they can detect it.
Third, to demonstrate the benefit of this approach to multi-source downloads, we built a prototype system that uses handprinting to locate sources of similar files. Our results show that the overhead of our approach is acceptable (roughly 0.5% per similar file). Without using similar sources, the prototype meets or exceeds BitTorrent’s performance. When we enable downloads from similar sources, the system finds and uses these sources to greatly improve transfer speeds.
1.2 Statement Of The Study
Many contemporary approaches for speeding up large file transfers attempt to download chunks of a data object from multiple sources. Systems such as BitTorrent quickly locate sources that have an exact copy of the desired object, but they are unable to use sources that serve similar but non-identical objects. Other systems automatically exploit cross-file similarity by identifying sources for each chunk of the object. These systems, however, require a number of lookups proportional to the number of chunks in the object and a mapping for each unique chunk in every identical and similar object to its corresponding sources. Thus, the lookups and mappings in such a system can be quite large, limiting its scalability.
This paper presents a hybrid system that provides the best of both approaches, locating identical and similar sources for data objects using a constant number of lookups and inserting a constant number of mappings per object. We first demonstrate through extensive data analysis that similarity does exist among objects of popular file types, and that making use of it can sometimes substantially improve download times. Next, we describe handprinting, a technique that allows clients to locate similar sources using a constant number of lookups and mappings. Finally, we describe the design, implementation and evaluation of Similarity-Enhanced Transfer (SET), a system that uses this technique to download objects. Our experimental evaluation shows that by using sources of similar objects, SET is able to significantly out-perform an equivalently configured BitTorrent.
1.3 Aim Of The Study
The aim of this study is to design and implementation of download wizard for simultaneous download of file for individual use.
1.4 Objective of The Study
The main objective of this proposed system is:
1. To develop a desktop application for online downloading of file
2. To easy the stress and time of download file one after the order
3. To reduce problem of file to download
1.5 Significance Of The Study
Projects provide a flexible framework for engaging students in exploring curricular topics and developing important 21st century skills, such as communication, teamwork, and technology skills. In addition, students are motivated by the fun and creative format and the opportunity to make new friends around the world. For download wizard for simultaneous download enables quick and easy download and management file.
1.6 Scope Of Study
The research will center on the design and implementation of download wizard for simultaneous download of file for individual use
1.7 Limitation Of Study
Usually, every work has some limitations and this study is not exempted.
The two major limitations of this study are the high programming technique as well as financial constraints. The high programming technique constraint in VB.net and Microsoft Access prevents the researcher to have an in depth study and analysis on the subject matter. While the issue of financial constraint limits the frequency of investigation to/from the institution toward gathering the necessary information relevant for the study.

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