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Download MP3 of 3 Finer's Top Songs: Abacate, Calcinha, and More

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Many contemporary approaches for speeding up large file transfersattempt to download chunks of a data object from multiple sources.Systems such as BitTorrent quickly locate sources that have an exactcopy of the desired object, but they are unable to use sources thatserve similar but non-identical objects. Other systems automaticallyexploit cross-file similarity by identifying sources for each chunk ofthe object. These systems, however, require a number of lookupsproportional to the number of chunks in the object and a mapping foreach unique chunk in every identical and similar object to itscorresponding sources. Thus, the lookups and mappings in such a systemcan be quite large, limiting its scalability.

This paper presents a hybrid system that provides the best of bothapproaches, locating identical and similar sources for dataobjects using a constant number of lookups and inserting a constantnumber of mappings per object. We firstdemonstrate through extensive data analysis that similarity does existamong objects of popular file types, and that making use of it cansometimes substantially improve download times. Next, we describehandprinting, a technique that allows clients to locate similarsources using a constant number of lookups and mappings. Finally, wedescribe the design, implementation and evaluation ofSimilarity-Enhanced Transfer (SET), a system that uses this techniqueto download objects. Our experimental evaluation shows that by usingsources of similar objects, SET is able to significantlyout-perform an equivalently configured BitTorrent.

In a per-file system, receivers locate other sources ofthe exact file they are downloading in O(1) lookups. These systems, exemplifiedby BitTorrent [7], Gnutella [2] andChunkCast [6], typically use a global location service.Unfortunately, as we explore further in Section 2,the performance of file transfers using these systems is oftenunacceptably slow, with users requiring hours or even days to downloadcontent.

In a per-chunk system, receivers locate sources for individual pieces,or chunks, of the desired file. Since any given chunk in a file mightappear in several other files, finding sources for each chunk canyield a much larger number of similar sources. The cost,however, is performing O(N) lookups, one for each of the N chunksin the file the receiver is trying to download. Moreover, such systemsalso require a mapping between every unique chunk in the identical andsimilar files and their corresponding sources, i.e., O(N) mappingsper object. Examples of per-chunk systems areCFS [9]and Shark [3].

The second contribution of this paper is a technique to locate similarsources to the file being downloaded using only O(1) lookups. Thistechnique, which we term handprinting, is a novel use of deterministicsampling. Sources insert a fixed number of hashes into a globaldatabase; receivers look up their own set of hashes in this database tofind sources of similar files. System-wide parameters determine theamount of similarity receivers can detect (e.g., all files with x%similarity to the target file) and with what probability they can detectit (Section 4).

Third, to demonstrate the benefit of this approach to multi-sourcedownloads, we built a prototype system that uses handprinting to locatesources of similar files (Section 5). Our results show that the overhead of ourapproach is acceptable (roughly 0.5% per similar file).Without using similar sources, the prototype meets or exceeds BitTorrent'sperformance. When we enable downloads from similar sources, thesystem finds and uses these sources to greatly improve transfer speeds (Section 6).

With fast, asymmetric broadband connections, receivers arefrequently unable to saturate their available bandwidth duringdownloads, even when using existing multi-source peer-to-peer downloadprograms. For example,in 2003, Gummadi et al. found that 66.2% ofdownloads failed, and that the median transfer time inKazaa for files over 100 MB was over oneday [13].In 2002, 75% of Gnutella peers had under 1 megabit of upstream capacity [28].

Unfortunately, our experience suggests that this situation has notimproved in the intervening years. Figure 1 shows theCDF of throughput achieved while downloading 6208 large files frompopular file-sharing networks (the details of our measurement studyappear in Section 3.1). The median transferachieved under 10 Kbit/s of average throughput, and the 90th percentile only managed50 Kbit/s, despite running these experiments from an extremelywell-connected academic network.

We care about exploitable similarity that can be used to speedtransfers. In most multi-source systems, including the one we proposehere, the smallest unit of data transfer is a chunk. Chunks can have astatic or variable size. To be able to detect similarity betweenmis-aligned objects, we define the chunk boundaries using Rabinfingerprinting.1 Exploitablesimilarity means that another file shares entire chunks with thetarget file that the receiver is trying to download. We thereforedefine similarity as the fraction of chunks that are shared betweentwo files.2

Note that in all of these cases, the modified files (and theirhashes) differ from the originals; traditional, per-file systemsthat look only for identical files cannot find and use the modifiedversions as additional sources for the original (desired) object.Furthermore, note that our similarity studymeasures similarity among files exactly as they are offered online fordownload (e.g., we do not uncompress, recode, etc. media files).

In therest of this section, we explore the feasibility of using theexploitable similarity among files to improve the performance ofmulti-source transfers. We propose two metrics to capture the benefitof using similar files. We apply these metrics to a large volume ofdata downloaded from file-sharing networks to show the effectiveincrease in the number of sources that a client of one of thesenetworks might experience.

We examined several large software collections as well as1.7 TB of files downloaded from popular file-sharing networks.The results from analyzing the software archives were similar tothose in previous studies. Due to space constraints, we chose not toinclude them here, but to instead focus on the more novel study ofmultimedia files.

Selecting files to download.We could only download a fraction of the files availableon these file-sharing networks, so the choice of what to download wasimportant. We intentionally sampled a subset of files that werelikely to be similar to each other and that we expected people to be interested in downloading.

The basic question we would like to answer is how much doesexploiting the observed similarity among files speed up a datatransfer. We address this question in two stages. The first step,described in this section, is analytical. We define twoparallelism metrics and apply them to the data we downloaded.The metrics quantify, on real data, the similarity in the data and theresulting speedup in a transfer from using that similarity. Thesecond step, described in the following sections, is to understand thepracticality of using similar sources through the design,implementation, and evaluation of a system that finds and uses suchsources for faster multi-source downloads.

The optimistic metric assumesthat the client can download from all sources in parallel with anoptimal choice of which chunk to download from which source. Underthis optimal assignment of chunks to sources, eachsource serves Cs chunks out of the totalnumber of chunks C. Thetime to download the file, therefore, is determined only by the maximum Cs,the largest number of chunks the receiver must download from any single source.The parallelism, then, is C/max Cs.

The conservative metric assumes limited parallelism. Chunks are downloaded one-at-a-time, but any given chunk is downloadedfrom all available sources in parallel.In the example above with one complete source and one source with halfof the chunks, the chunk download time would beC/C/2 1/2 + C/2 1/1 =4/3.If instead these two sources each had a complete copy,the parallelism would be C/(C1/2)= 2.


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