Torrentz2 is a search engine for the torrent world. It is just another Google for torrent Lovers. You can search for any content here, It will show results from more than 25 torrent websites. But you can consider it as anime torrenting sites, You can search Anime content here and get results from famous torrent sites. The outer World is worth buying, and it is better to play than New Vegas and Fallout 3. More than 20 Lakhs copies have been purchased in the entire World. This game is a spiritual continuation to Fallout, having a similar character playing system. Fallout play function on the Outer World is an image of gameplay. Rifts® World Book 13: Lone Star™ The Coalition State of Lone Star is a wild and woolly place of contrast and conflict. The Coalition States claims the entire State of Texas as their own, but they only control a small corner of it. Torrenting news website TorrentFreak added this was a noticeable after-effect of a major torrent site shutdown, with people hoping to either keep lost files accessible or otherwise corner the. This happens with the help of specialized software that takes tiny parts of the file from multiple computers (from all over the world) and assembles them into a complete version. It is pertinent to note that this acts as a loophole for the authorities to trace a single-serve used for cyber-crime or that has violated any legal principles.

1. In This Corner Of The World Download Torrent Free
2. In This Corner Of The World Download Torrent Download
3. In This Corner Of The World Download Torrent Pc

György Dán

ACCESS Linnaeus Centre

KTH, Royal Institute of Technology

Stockholm, Sweden

Niklas Carlsson

University of Calgary

Calgary, Canada

Abstract:

BitTorrent is a popular peer-to-peer file-sharing protocol that has been shown to scale well to very large peer populations [9].With BitTorrent, content (e.g., a set of files) is split into many small pieces, each of which may be downloaded from different peers. The content and the set of peers distributing itis usually called a torrent.A peer that only uploads content is called a seed, while a peer that uploads and downloads at the same time is called a leecher.The connected set of peers participating in the piece exchanges of a torrent is referred to as a swarm.

A client that wants to download a file can learn about other peers that share the same content by contacting a tracker at its announce URL. Each tracker maintains a list with state informationof known peers that currently are downloading and/or uploading pieces of the file, and provides a peer with a subset of the known peers upon request. Upon requesting the list of peers, the peer has to provide the tracker with information about its download progress. Additionally, the peers must inform the tracker about changes in their status (i.e., when they join, leave, or finish downloading the file). To avoid overloading trackers, the BitTorrent protocol only allows a peer to associate with one tracker per file that it is downloading (unless the tracker is no longer available and a new tracker must be contacted). Naturally, torrents may therefore have multiple parallel swarms.

While BitTorrent allows peers to effectively share pieces of popular torrentswith many peers in a swarm, the performance of smalltorrents and swarms is sensitive to fluctuations in peer participation.Measurement data suggests that peers in small swarms achieve lower throughputon average (e.g., Figure 9 in [9]). Most swarms are unfortunately small; several sources confirm that the popularity distribution of p2p content follows a power-law form, with a ``long tail' of moderately popular files (see Figure 1(a) and, e.g., [1,3,4,5]).At the same time, the measurement data we present in this paper shows that many torrents consist of several swarms (see Figure 1(b)).Potentially, if one could dynamically re-allocate peers amongthe trackers such that multiple small swarms of a torrent are merged into a single swarm,then one could improve the file sharing performance of the peers belonging to these torrents.

Motivated by these observations, the goal of our work is to evaluate the feasibility and the potential gains of dynamic swarm management for BitTorrent trackers. To support our evaluation, we performed measurements of trackers, which collectively maintain state information of a combined total of million unique torrents.We propose a light-weight distributed swarm management algorithm, called DISM,that also ensures load fairness among the trackers. Based on our measurement data and using DISM weargue that dynamic swarm balancing could lead to a significant performance improvementin terms of peer throughput. We also briefly discuss alternatives for swarm management that could lead to similar performance improvements.

The remainder of the paper is organized as follows. Section 2 describes our design objectivesand a light-weight distributed swarm management algorithm. Section 3 presents validation and performance results. Related work is discussed in Section 4. Finally, Section 5 concludes the paper.

Ignoring the seed-to-leecher ratio, small swarms typicallyperform worse than larger swarms. However, for load sharingand reliability purposes it may be advantageous to split theresponsibility of maintaining per-peer state informationacross multiple trackers, i.e., to allow several swarms to coexist.

Consequently, dynamic swarm management should make it possible to (i) merge swarmsbelonging to a torrent if they become too ``small', and to (ii) split a swarm or to re-balance swarms if the swarms become sufficiently ``large'.In general, it is hard to define when a swarm should be considered ``small' or ``large',but for simplicity, we assume that a swarm can beconsidered ``large' if it has at least participating peers, for some threshold .``Large' swarms are likely to have high piece diversity and aremore resilient to fluctuations in peer participation.

Apart from these two properties, one would like to minimize the effect of swarm balancing on the traffic load of the trackers by (iii) avoiding a significant increase in the number of peers for any individual tracker (load conservation), and by (iv) minimizing the number of peers that are shifted from one tracker to another (minimum overhead, especially shifts associated with torrents being re-balanced).

The distributed swarm management (DISM) algorithm we describe in thefollowing was designed with these four properties in mind.Our algorithm allows swarms to be merged and to be split: swarms are merged whenever a swarm has less than participating peers, and a swarm is split (or re-balanced) over multiple trackers only if splitting the peers does not cause any swarm to drop below peers. The algorithm ensures that no tracker will see an increase of morethan peers in total (across all torrents) and typically much less,and it does not balance swarms that have at least peers each.

The DISM algorithm is composed of two algorithms.It relies on a distributed negotiation algorithm to determine the order in which trackers should perform pairwise load balancing. On a pairwise basis, trackers then exchange information about the number of active peers associated with each torrent they have in common (e.g., by performing a scrape), and determine which tracker should be responsible for which peers.

2.1 System Model

Table 1 defines our notation.In the following, we consider a system with a set of trackers , witha combined set of torrents .We will denote by the set oftrackers that track torrent , and by the set of torrents that are tracked by tracker .Every torrent is tracked by at least one tracker(i.e., ), but thesubsets are not necessarily pairwise disjoint.Finally, let us denote the number of peers tracked by tracker for torrent by , the total number of peers tracked by tracker by , and the total number of peers associated with torrent by .

2.2 Distributed Negotiation

The distributed negotiation algorithm assumes that each tracker knows the set of torrents that has in common with each other tracker for which the trackers' torrents are not disjoint (i.e., for which).Note that this information should be availablethrough the torrent file, which should have been uploaded to the tracker when the torrent was registered with the tracker.

The algorithm works as follows. Tracker invites for pairwise balancing the trackers for which the overlap in tracked torrents, is maximal among thetrackers with which it has not yet performed the pairwise balancing. A tracker accepts the invitation if its overlap with tracker is maximal. Otherwise, tracker asks tracker to wait until their overlap becomes maximal for as well.The distributed algorithm guarantees that all pairs of trackerswith an overlap in torrents will perform a pairwise balancing once and only once during the execution of the algorithm.If tracker accepts the invitation, tracker queries for from , executes the pairwise balancing algorithm described below, and finally tells the results to tracker .

2.3 Pairwise Approximation Algorithm

Rather than applying optimization techniques, we propose a much simpler three-step greedy-style algorithm. First, peers are tentatively shifted based only on information local to each individual torrent. For all torrents that require merging (i.e., for which ),all peers are tentatively shifted to thetracker that already maintains information about more peers for that torrent.For all torrents that should be re-balanced (i.e., for which and ), the minimum number of peers () needed to ensurethat both trackers have at least peers aretentatively shifted to the tracker with fewer peers.

Second, towards achieving load conservation of , the peer responsibility of some torrents may have to be adjusted. Using a greedy algorithm, the tentative allocations are shifted from the tracker that saw an increase in peer responsibility (if any) towards the tracker that saw a decrease in overall peer responsibility.To avoid increasing the number of peers associated with partial shifts, priority is given to shifting responsibilities of the torrents that are being merged. Among these torrents, the algorithm selects the torrent that results in the largest load adjustment and that does not cause the imbalance in overall load to shift to the other tracker. By sorting torrents based on their relative shift, this step can be completed in steps.

Finally, if overall load conservation is not yet fully achieved,additional load adjustments can be achieved by flipping the peer responsibilitiesof the pair of torrents that (if flipped) would result in the load split closest toachieving perfect load conservation (across all torrents), with ties broken in favorof choices that minimize the total shift of peers.Of course, considering all possible combinations can scale as. However, by noticing that only the torrents with the smallest shift for each load shiftare candidate solutions, many combinations can be pruned. By sorting the torrents appropriately, our current implementationachieves whenever is finite.

We have also considered an alternative version in which priority (in step two)is given to allocations of the torrents that are being re-balanced. For these torrents, we (greedily) balance the load of the torrent with the largest imbalance first, until load conservation has been achieved or all such torrents have reached their balancing point. Results using this algorithm are very similar to those ofour baseline algorithm and are hence omitted.

2.4 Implementation and Overhead

The proposed DISM algorithm could be implemented with a minorextension to the BitTorrent protocol. The only new protocol message requiredis a tracker_redirect message that could be used by a tracker to signal to a peer that the peer should contact an alternative trackerfor the torrent. The message would be used by a tracker for a torrent for which decreases due to the execution of DISM.Peers that recieve the message shouldcontact another tracker they know about from the tracker file.

The communication overhead of DISM is dominated by the exchangeof torrent popularity information between the trackers and by the redirection messages sent to the peers. Distributed negotiatation involves one tracker scrape before every pairwise balancing, and the correspondingexchange of the results of the balancing. The amount of data exchanged betweentrackers and is hence .The amount of redirection messages is proportional to the number of peers shifted between swarms, and is bounded by .

3.1 Empirical Data Set

We used two kinds of measurements to obtain our data set.First, we performed a screen-scrape of the torrent search engine www.mininova.org.In addition to claiming to be the largest torrent search engine, mininova was the most popular torrent search engine according to www.alexa.com during our measurement period (Alexa-rank of , August , ). From the screen-scrapeswe obtained the sizes of about files shared using BitTorrent, and the addresses of trackers.

Second, we scraped all the trackers for peer and download information of all the torrents they maintain. (Apart from interacting and helping peers, a tracker also answers scrape requests at its scrape URL.) For the tracker-scrapes we developed a Java application that scrapes the scrape URL of each tracker. By not specifying any infohash, the tracker returns the scrape information for all torrents that it tracks. This allowed us to efficiently obtain the number of leechers, seeds, and completed downloads as seen by all trackers that we determined via the screen-scrape of mininova.

We performed the tracker-scrapes daily from October , , to October , . All scrapes were performed at pm GMT.We removed redundant tracker information for trackers that share information about the same swarms of peers, and identified independent trackers. Table 2 summarizes the data set obtained on October 10, 2008.

Figure 1 summarizes some of the main characteristics of the world of trackers and torrents captured by our measurements. Figure 1(a) shows the size of torrents and swarms against their rank. Similar to previous content popularity studies (e.g., [1,3,4,5]),we find that the torrent rank distribution is heavy tailed, with Zipf-like characteristics,and a substantial number of torrents have moderate popularity. Based on our screen-scrapes,we found no correlation between content size and torrent popularity(the correlation coefficient is approximately ).

Figure 1(b) shows the number of torrents with a given number of unique swarms (after removing duplicates). Clearly, there are a substantial number of torrents that are served independently by multiple trackers. To assess the fraction of small swarms that would benefit from being merged, Figure 1(c) shows the normalized coefficient of variation (CoV) of the swarm sizes versus the torrent size, of each observed torrent. A value of ofthe normalized CoV shows that swarm sizes are equal; a value of shows that all swarms are empty except for one.For small torrents (e.g., smaller than 200 peers) we would like the normalized CoV to be close to one. While the normalized CoV only can take a finite number of values (hence the distinguishable patterns), this figures show that there aremany small torrents that could benefit from being merged.

3.2 Protocol Convergence/Complexity

Before analyzing the performance benefits of dynamic swarm management, we note that DISMexecutes quickly, with low overhead for the trackers.Using the data obtained on October , 2008, there are trackersthat have to participate in the algorithm (the rest do not have overlap in torrents).The average overlap between these trackers is torrents.In total, pairwise balancings were performed; i.e., slightly more than two pertracker on average. We argue that this overhead is negligible compared to theload due to scraping and announce traffic caused by the peers.

3.3 Peer Allocation using DISM

To analyze the performance benefits of dynamic swarm management,we used the DISM algorithm to re-allocatethe peers between swarms belonging to the same torrent.Figure 2 shows the average normalized CoV as a function of the number of peers for October 10, 2008. Figure 2(a) shows results in which the full torrent size is considered. Figures 2(b) and (c) show the same results, but for leechers and seeds, respectively. All figures show results for both the original peer allocation, as well as for the distributed algorithm using three different threshold values (i.e., ). We note that the distributed algorithm pushes the CoV for torrents smaller than the threshold values (and even a bit beyond) to roughly one. As previously discussed, this is exactly what we want.

Finally, to further analyze the re-balancing achieved of torrents, Figure 3 shows the normalized CoV for all torrents. Comparing Figure 3 with Figure 1(c) we note that the distributed algorithm significantly reduces the number of torrents operating in the lower left corner. While there still are some torrents that do, we note that Figure 2 suggests that this number is very small.

3.4 Throughput Analysis

Thus far our analysis has been based on the assumption that small torrents are more likely to perform poorly. To validate this assumption and allow us to obtain estimates of the potential performance gains small torrents can obtain using our distributed algorithm we need to estimate the throughput of different sized swarms.

To estimate the throughput of any particular swarm we measured the number of seeds, leechers, and cumulative number of downloads for two consecutive days. Using these values we estimated the throughput per leecher as the file size divided by estimated download (service) time , which using Little's law can be expressed as , where is the averagenumber of leechers currently being served in the system and the current arrival rate, which due to flow conservation can be estimated as the overall throughput (equal to the number of download completions during that day, times the file size , and divided by the time between two consecutive measurements). To summarize, we have an estimated throughput of . Finally, throughput estimates for any particular swarm type wereobtained by taking the average over all swarms of that particular size.

Figure 4 shows our throughput estimation results. Here, swarms are classified based on the total number of peers in the swarm () and the seed-to-leecher ratio ; 60 bins are used for the swarm sizes and three curves are used for the different seed-to-leecher ratios.We note that the results for swarms up to just over peers areconsistent with intuition and previous measurement results [9]. For larger swarm sizes we believe that the above estimation is less accurate. Estimation errors may be due to inaccurate estimation of the average number of leechers over the measurement period, for example. Furthermore, we note that the popularity of these type of workloads typically follows adiurnal pattern and our measurements are done at peak hours. Therefore, the throughput estimations are pessimistic.

Using these throughput estimations we can estimate the speedup achievedby the distributed algorithm. Figure 5 shows the relative throughput improvement for leechers as a function of the torrent size. The results show a good match with our objectives: the smallest torrents experience throughput gains up to percent on average, while torrents above approximately peers are not affected by our algorithm.

Figure 6 shows therelative estimated throughput improvement for leechers in torrents smaller than 300 peers over a week. The figure shows the estimated increase of the throughput per leecher averaged over the torrents for three different values of the threshold . The curvesmarked with show results weighted with the torrent size; the curves without markers show the non-weighted gain. The throughput gains are rather steady in both cases, and show that dynamic swarm managenemt could improve peer throughput significantly.

Much research has been done to understand BitTorrent andBitTorrent-like [6,11] systems. Whilemost of these efforts have been towards understanding the performanceof single torrent systems, there have been somerecent papers that consider multi-torrent environments [7,10].Guo et al. [7] provide measurements results thatshow that more than 85% of torrent users simultaneouslyparticipate in multiple torrents. The authors also illustrate that the``life-time' of torrents can be extended if a node thatacts as a downloader in one torrent also acts as a seed in another torrent.Yang et al. [10] propose incentives thatmotivate users to act as seeds in such systems.In particular,Yang et al. propose a cross-torrent tit-for-tatscheme in which unchocking is done based on the aggregatedownload rates of a peer across all torrents (instead ofonly based on the download rate for a single torrent).

Rather than increasing seed capacity through cooperationamong peers downloading different files, this paper focuseson how multiple swarms of the same file can be mergedto improve the performance of small swarms. To thebest of our knowledge, no prior work has considered theperformance benefits from adaptively merging and/or re-distributingpeer information among trackers.

Other related works have considered the effectiveness of BitTorrent's tit-for-tat and rarest-first mechanism [2,8]. BitTorrent-like systems have also been considered for streaming [11]. Finally, we note that long-tailed popularity distributions have been observed in many other contexts, including Web workloads [1,3] and user generated content [4,5].

Using an extensive measurement study we have observed that there exist many moderately popular torrents that could significantly benefit from the re-balancing of peer information available on the trackers. In this paper, we consider a light-weight distributed swarm management algorithm that manages the peer information at the trackers while ensuring load conservation among the trackers. The algorithm is relatively simple and achieves much of its performance improvements by identifying and merging peer information of small swarms. Apart from improving the average throughput, merging small swarmsalso facilitates locality-aware peer selection.

In This Corner Of The World Download Torrent Free

Finally, we note that there are at least two alternatives to DISM to avoid forming small swarms.One alternative is for peers to perform a scrape of all responsibletrackers before joining a torrent, and to pick the largest swarm.The disadvantage of this solution is that it does not allow load balancingfor large torrents.The other alternative is based on random tracker hoppingand the peer exchange protocol (PEX). A peer would change tracker (i.e., swarm)with a probability proportional to , and would usePEX to distribute the addresses of the peers it knows about from theprevious swarm. The disadvantage of this alternative is thatnot all BitTorrent clients support PEX.

While DISM is not the only way to improve the throughput of small swarms,the potential benefits of the other solutions would be similar to those of DISM,discussed in this paper.Future work will compare alternative approaches.

The authors are grateful to the anonymous reviewers,Aniket Mahanti, and Carey Williamson for their constructive suggestions, which helped improve the clarity of the paper. Niklas Carlsson was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Informatics Circle of Research Excellence (iCORE) in the Province of Alberta.György Dán was visiting the Swedish Institute of Computer Science while most of this work was performed.

Bibliography

1

M. Arlitt and C. Williamson.
Internet Web Servers: Workload Characterization and Performance Implications.
IEEE/ACM Trans. on Networking, 5(5):631-645, Oct. 1997.

2

A. R. Bharambe, C. Herley, and V. N. Padmanabhan.
Analyzing and Improving a BitTorrent Networks Performance Mechanisms.
In Proc. IEEE INFOCOM, Barcelona, Spain, Apr. 2006.

3

L. Breslau, P. Cao, L. Fan, G. Phillips, and S. Shenker.
Web Caching and Zipf-like Distributions: Evidence and Implications.
In Proc. IEEE INFOCOM, New York, NY, Mar. 1999.

4

M. Cha, H. Kwak, P. Rodriguez, Y. Ahn, and S. Moon.
I Tube, You Tube, Everybody Tubes: Analyzing the World's Largest User Generated Content Video System.
In Proc. ACM IMC, San Diego, CA, Oct. 2007.

5

P. Gill, M. Arlitt, Z. Li, and A. Mahanti.
YouTube Traffic Characterization: A View from the Edge.
In Proc. ACM IMC, San Deigo, CA, Oct. 2007.

6

C. Gkantsidis and P. R. Rodriguez.
Network Coding for Large Scale Content Distribution.
In Proc. IEEE INFOCOM, Miami, FL, Mar. 2005.

7

L. Guo, S. Chen, Z. Xiao, E. Tan, X. Ding, and X. Zhang.
Measurement, Analysis, and Modeling of BitTorrent-like Systems.
In Proc. ACM IMC, Berkley, CA, Oct. 2005.

8

A. Legout, G. Urvoy-Keller, and P. Michiardi.
Rarest First and Choke Algorithms are Enough.
In Proc. ACM IMC, Rio de Janeiro, Brazil, Oct. 2006.

9

X. Yang and G. de Veciana.
Service Capacity of Peer-to-Peer Networks.
In Proc. IEEE INFOCOM, Hong Kong, China, Mar. 2004.

10

Y. Yang, A. L. H. Chow, and L. Golubchik.
Multi-Torrent: A Performance Study.
In Proc. MASCOTS, Baltimore, MD, Sept. 2008.

11

X. Zhang, J. Liu, B. Li, and T.-S. P. Yum.
CoolStreaming/DONet: A Datadriven Overlay Network for Peer-to-Peer Live Media Streaming.
In Proc. IEEE INFOCOM, Miami, FL, Mar. 2005.

Dynamic Swarm Management for Improved BitTorrent Performance

This document was generated using theLaTeX2HTML translator Version 2002-2-1 (1.70)

Copyright © 1993, 1994, 1995, 1996,Nikos Drakos, Computer Based Learning Unit, University of Leeds.
Copyright © 1997, 1998, 1999,Ross Moore, Mathematics Department, Macquarie University, Sydney.

The command line arguments were:
latex2html-split 0 -show_section_numbers -local_icons -no_navigation swarm-trading.tex

The translation was initiated by Gyorgy Dan on 2009-03-17

At the beginning of 2017, the world saw a new version of the legendary football simulator - Pro Evolution Soccer (PES 17). This series of games has been enjoying unprecedented popularity for over 20 years and all thanks to the realistic football world for the user. In the newly minted project, the developers paid special attention to the network modes, essentially working out each of them.

The developers also managed to create an editor for PS4 with which you can license the emblems, uniforms and names of the football team.

Description of the game

PES 2017 is a new series of football simulator developed by the famous Konami studio. This game is considered the main competitor of FIFA, but it is this kind of struggle that makes each version better than the previous one. The PES series is popular for its realism, and in the new part it is brought to near perfection, allowing players to feel the football as it could in the stadium.

Football in PES 17, download torrent which can be found on our website, has changed. Players can experience new ways to dribble, shoot on goal, cross and a variety of feints. The trajectory of the ball's flight looks attractive, which is influenced by many factors: from the angle of serving to the state of the grass on the field.

Artificial intelligence has undergone transformations. Now the athletes controlled by the system have become more unpredictable, react more sharply to the player's tactics, and predict his moves. Graphics and visualization have changed for the better, as well as the level of detail has increased, new sounds of the tribune have appeared.

What's new in gameplay in PES 17

In This Corner Of The World Download Torrent Download

The players of their favorite football simulator note that in PES 17 everything has become much more interesting. The features of the gameplay of the new series include:

• real touch;
• accuracy of the pass;
• player’s natural movement;
• goalkeeper toolkit;
• total control of the team;
• corner kick strategy;
• learning artificial intelligence.

In Game PES 17, download torrent which can be found in the sports simulators section, hundreds of new animations have been added to bring field players and goalkeepers to life. If you are a fan of virtual football, then you should definitely download PES 17 from torrent on our website and show who the winner is.

Game info

• release date: 2016
• Genre: Sports
• developer: Konami Digital Entertainment
• Publisher: Konami Digital Entertainment
• Platform: RS
• Interface language: RUS | ENG
• Tablet: Sewn

System Requirements

In This Corner Of The World Download Torrent Pc

• Operating system: Windows Vista / 7/8 / 8.1 / 10
• Processor: Intel Core2 Duo 1.8GHz / AMD Athlon X2 240 or equivalent
• RAM: 4 GB RAM
• Video card: NVIDIA GeForce GT 630 / AMD Radeon HD6570
• Free space on hard disk: 13 GB