ACNS: Adaptive Complementary Neighbor Selection in BitTorrent-like Applications

Transcription

1 ACNS: Adaptive Copleentary Neighbor Selection in BitTorrent-like Applications Zhenbao Zhou 1, 2, Zhenyu Li 1, 2, Gaogang Xie 1 1 Institute of Coputing Technology, Chinese Acadey of Sciences, Beijing , China 2 Graduate School of Chinese Acadey of Sciences, Beijing , China {zhouzhenbao, zyli, xie}@ict.ac.cn Abstract BitTorrent, one of the ost popular Peer-to-Peer file sharing applications, accounts for a large proportion of the total Internet traffic. While its appearance benefits the content distributors and users, the traffic injected into the network backbone has becoe a great challenge for the ISPs. In this paper, we study traffic shaping in BitTorrent-like applications to iprove traffic locality and enable fast data delivery. To this end, a piece copleentary index is introduced based on the piece deand between peer nodes. Then, we propose an efficient and adaptive neighbor selection schee (ACNS). According to ACNS, each node self-adaptively chooses the ost copleentary peers to connect with and download file pieces, rather than having a fixed nuber of outside neighbors. Our schee can be integrated with the BitTorrent protocol by slight odifications, and requires no additional infrastructure provided by ISPs. Experiental results based on extensive siulations have shown the effectiveness of ACNS. Copared with the fixed biased neighbor selection schee, ANCS cuts down the cross-isp traffic by ore than 31% and iproves the download rate by about 15%. I. INTRODUCTION The past few years have witnessed the continuous growth of the P2P (Peer-to-Peer) file sharing applications, such as Gnutella, emule, BitTorrent [3] and etc. A recent report [13] by Ipoque has shown that P2P file sharing applications account for about 49% to 83% of Internet traffic worldwide, while BitTorrent is the ost popular application which represents as large as 73% of P2P traffic in Australia, and ore than 60% in Gerany, China and soe other countries. For space liitation, we oit the BitTorrent protocol specifications since is quite well-known [10]. A. Proble Stateent and Related Works BitTorrent has gained great success and has been considered as one of the ost popular and efficient applications for file content distribution. However, it brings a great challenge for the Internet Service Providers (ISPs) due to the large aount of cross-isp traffic, which not only significantly increases ISPs cost, but also consues too uch backbone s bandwidth, adversely affecting the QoS of other applications, such as WWW, Eail and etc. In BitTorrent, the neighbors of a node are chosen randoly. The randoized neighbor selection enables the even distribution of file pieces during file downloading. But, it also greatly increases the probability that a node downloads a piece fro a reote neighbor (e.g. belonging to different ISPs), although the piece is also owned by a physically nearby neighbor (e.g. belonging to the sae ISP). Thus, the cross-isp traffic is considerable. Actually, in an N-node syste, for the ISP that has n nodes in the syste, a file piece traverses into the ISP n(1 n/ N) n ties, on average [11]. Currently, ISPs always resort to rate throttling to control the traffic generated by BitTorrent. The coplaints raised by the subscribers and how to accurately identify BitTorrent traffic are two ajor concerns. Deploying caches at the ISPs gateways is another choice [6][8]. However, it requires a large storage space and violates the legality of the content. Bindal et. al in [10] present a fixed biased neighbor selection algorith to iprove the traffic locality of BitTorrent. Each node only builds a sall nuber (e.g. 1) of links with external peers that locate at other ISPs. Liiting and fixing the nuber of links with external peers adversely affect the evenly distribution of file pieces, which in turn prolongs the download tie. Moreover, the external neighbors of a node are fixed all the tie, which akes it very likely the file pieces that are very coon within local network are still fetched fro other ISPs. Xie et. al propose P4P [6], an architecture for cooperative traffic control. Each ISP provides a server naed itracker to keep track of the network state, topology and policy of the ISP. Then, the tracker calculates which neighbor should be chosen according to the inforation collected by itrackers. In [11], nodes are grouped into clusters and several nodes with higher upload speed are selected as the core nodes for the cluster. Only the core nodes get file pieces fro other clusters. Therefore, the core nodes are naturally the bottlenecks of the clusters and ore vulnerable. Other related works include easureent and odeling of BitTorrent systes. Interested readers please refer to [2] [4]for details. B. Our Contribution In this paper, we study traffic shaping in BitTorrent-like applications through adaptive bias neighbor selection. Our goal is to iprove traffic locality and enable fast data delivery. To this end, we propose an efficient and adaptive neighbor selection schee (ACNS). ACNS is otivated by the fact that the nuber of rando neighbors of a node should varies adaptively with the file download process. The file download process is spit into three phases [12]: bootstrap phase, efficient download phase and last download phase. At the bootstrap phase, since only very few nodes have file pieces, a node /09/$ IEEE

2 should establish ore links to rando nodes to fetch file pieces into the local network. At the efficient download phase, since there have been lots of file pieces in local network, the nuber of rando neighbors of a node should be reduced correspondingly. At the last download phase, since the nuber of nodes in the local network is relative sall, a node should build ore rando links to get the last pieces. We introduce a piece copleentary index (PCI) to evaluate the copleent of two nodes, based on the deand of the on each other. PCIs of neighbors vary with the evolution of the file download process and give us indications of how any and which rando neighbors that a node should have. Each peer periodically evaluates the PCIs to rando nodes and self-adaptively chooses the ost copleentary peers to connect with and download file pieces. To the best of our knowledge, this is the first work that uses adaptive neighbor selection to shape BitTorrent traffic. We perfor extensive syste siulations based on an event-driven siulator to evaluate the perforance of ACNS. The results show that our schee iproves the traffic locality and file download speed effectively. Specially, copared with the fixed biased neighbor selection schee [10], ANCS cuts down the cross-isp traffic by ore than 31% and iproves the download rate by about 15%. The rest of the paper is organized as follows. Section II details ACNS algorith, including the definition of PCI and adaptive neighbor selection. Section III presents the experient setup, perforance etrics and the results. Finally, we conclude our work in Section IV and describe the possible future works. II. ADAPTIVE COMPLEMENTARY NEIGHBOR SELECTION In this section, we detail our approach ACNS. To ease description, we define soe notations. A node y is called as node x s outside neighbor if x and y locate at different ISPs. Otherwise, y is x s inside neighbor. We first introduce Piece Copleentary Index (PCI) to reflect the copleent of two peers. If a node x s outside peer y has several file pieces which are rare on x s inside neighbors, then we call x has a deand on y. If y also has a deand on x, we call x and y are copleentary. A larger copleent of x and y indicates the necessary to build a neighbor link between the. Obviously, the copleent of two peers varies with the file download process. Thus, each node periodically evaluates the deands of it on the outside peers and, in turn, calculates the PCIs. Based on the PCIs, a node adaptively adjusts its TABLE I NOTATIONS Notation ISP-x D(x, y) P(x) M(x) S(x) R(x) K x L(i,x) PCI(x, y) Definition the nuber of overall pieces the ISP that node x belongs to the deand of node x on node y the set of pieces peer x has downloaded the set of pieces peer x isses the set of inside neighbors of peer x the set of outside neighbors of peer x the nuber of inside neighbors of peer x, equal to the cardinality of S x the nuber of nodes in S x that have piece i the piece copleentary index of node x and node y neighbor relationship by selecting several outside peers which are ore copleentary to it to connect and download file pieces. Table I lists all iportant paraeters and notations used in this paper. A. Piece Copleentary Index We first describe how to evaluate the deand of a node x on an outside peer y, D( xy., ) Intuitively, x has a deand on y if y has soe file pieces that x issed. However, our goal is to liit the traffic injected into the ISPs. Thus, we call x has a deand on y if and only if y has several file pieces that are rare on x s inside neighbors. Thus, we define D( xy, ) to reflect the extent of x s deand on y as follows. Dxy (, ) = i= 0 ( e ( i)( K L( i, x)) xy i= 0 x ( e ( i) K ) xy x where exy () i is a indicator which indicates whether file piece i is owned by y but issed by x, and is calculated as follows. Other notations are listed in Table I. 1 if i P( y) and i M ( x) exy () i = (2) 0 otherwise Obviously, 0 Dxy (, ) 1. Note that D( xy, ) is not zero even x s inside neighbors have the pieces that x isses. Recall that another goal is to enable fast file distribution. If only very few (e.g. 1) inside neighbors have the file piece that x isses, then, since a node only supports 5 concurrent uploads, x ay wait for a long tie before it gets that piece. Thus, if the file pieces that x isses are rarely replicated on its inside neighbors, x still has a deand on the outside neighbor y which has those pieces. However, the extent of x s deand on an outside peer decreases with the growth of the nuber of inside neighbors which have the file pieces that x isses. Another thing worth pointing out is that the deand of x on outside peer y also reflects the deand of ISP-x on peer y. The deand of node x on its outside peer can be used to guide the neighbor relationship establishent between the: the higher the deand is, the higher probability that a link is built between the. However, recall that BitTorrent uses incentive schee Tit-for-Tat (TFT). Thus, if x s outside peer y has little interests on the pieces that x has downloaded, then it would take a long tie before x gets a file piece fro y, even x has a large deand on y. On the other hand, if y also has a large deand on x, then it has a great chance that the file piece transferring starts quickly between the and the transferring lasts for a long tie. Note that, since each peer leverages rare-first strategy to select which piece to download, the file pieces exchanged between x and y ust be the ones that ISP-x and ISP-y need eagerly. Motivated by this fact, we define Piece Copleentary Index (PCI) to reflect the copleent of x and y as follows. PCI( x, y) = D( x, y) D( y, x) (3) Now, we directly leverage PCI( x, y ) to guide the neighbor relationship establishent. A larger copleent of x and y indicates the ore necessary to build a neighbor link between the. Fig. 1 gives an exaple of how to copute PCI between (1)

3 two peers. The shared file is divided into 3 pieces. Since node y only has piece c, thus Dxy (, ) = ( (3 2))/3= 1/3. Since node y and all its inside neighbors iss piece a, thus Dyx= (, ) ((3 0) ) / 3 = 1. Finally, PCI( x, y ) = 1/3. Fig. 1 Exaple: calculation of PCI( x, y ) B. Adaptive Neighbor Manageent Each node x periodically requests the tracker for a peer list. In ACNS, the tracker records which ISP each peer belongs to. When receiving a request fro x for peer list, the tracker responds x with a k-peer list, of which k/2 peers are randoly selected fro x s inside peers and other k/2 peers are randoly selected fro x s outside peers. Node x aintains two peer lists: one for inside peers and one for outside peers. These two lists are used to record the peer inforation received fro the tracker. Node x coputes PCIs with each peer in the outside peer list according forula (3). To save the tie and space cost, we liit the length of each list to 35. Least Recently Used (LRU) echanis is used when the lists are full. For each node x, suppose that q outside peers are kept in the outside peer list, we sort the by their PCIs with x, which yields PCI ( x, y1) PCI ( x, y2)... PCI( x, y q ). Now, we select the first several peers with higher PCIs as x s outside neighbor. The question is how any peers we should select. A siple answer is that each node fixed selects k peers. Then, the question is transfored to how to deterine the value of k. A larger k causes too uch cross-isp traffic whereas a saller k slows down the download rate. Instead, in ACNS, we adopt an adaptive selection based on the PCIs. After sorting the PCIs of all available outside peers of x, we have the following question: in( k ), subjected to PCI( x, y1) + PCI ( x, y2) PCI( x, yk ) RT, where RT is short for (Required Threshold) and reflects how large the copleent we want to have. Given the value of RT, we can get the k outside peers for peer x to connect with. The default value of RT is 1. However, we evaluate its effect on the perforance in the siulations (Section III). Suppose the selected k outside peers for node x copose set T( x. ) Node x runs the routine adjust_neighbor as described in Fig. 2. After the execution of routine adjust_neighbor, if the total nuber of connections that node x initializes is below 20, as the regular BitTorrent, x contacts the tracker to obtain a new peer list and connects with the inside peers. Peers in ACNS periodically adjust their neighborhood relationships. The period, which is referred as Rechoose Neighbor Interval (RNI), should be chosen carefully. Since TFT strategy x.adjust_neighbor( T( x ) ) // the notations used here are all listed in Table 1, if not otherwise specified. 1. for each node y R( x) do 2. if y T( x) then 3. x disconnects the connection to y 4. T( x) = T( x) { y} 5. end if 6. end for 7. t = 35 S( x) R( x) /* A node at ost can initialize 35 connections actively, S( x ) and R( x ) are the cardinality of the set S( x ) and R( x ), respectively. */ 8. while t > 0 and T( x ) is not epty do 9. x randoly selects a node z in T( x ) to establish a connection 10. t = t T( x) = T( x) { z} 12. end while 13. if t > 0 then 14. x randoly selects t peers fro the inside peer list it stores to connect with 15. end if Fig. 2 Algorith for neighbor adjustent is used, a node only unchokes its neighbor fro which its download rate is higher. Therefore, newly neighbors can only get unchoked by optiistic unchoke strategy. This eans that the RNI should be bigger than the optiistic unchoke interval, which is 30 seconds. If we only consider the control overhead, RNI should be larger. However, after the piece exchanges for a while, the copleent of two nodes shrinks quickly. When the copleent of two nodes is very low, we should disconnect the links and build new links between ore copleentary nodes. Thus, the interval should not be too long. In current design, the default value of RNI is 120 seconds. However, we also evaluate the ipact of RNI on the perforance in siulations (Section III). Under two special circustances, ACNS is not used. First, since the seeds do not need any file piece, they randoly choose their neighbors as in the regular BitTorrent protocol to evenly distribute the file pieces. Second, for a new peer that has just joined and does not have any blocks, ACNS is not applicable. The new peers also select their initial neighbors randoly. Note that we do not eploy ACNS when selecting inside neighbors. This is because, after joining the torrent for a while, a node always has ore links to inside neighbors and the bandwidths of these links are always very large. Thus, the file pieces are distributed very quickly within the ISPs even without ACNS. C. The Ipleentation of ACNS By slightly odifying the tracker and the client, ANCS can be ipleented easily. A function is added in the tracker to record the ISPs that the peers belong to. The challenge for tracker is how to get the inforation of ISP for each client. Many tools such as iplane [14] periodically update their databases for IP-to-AS apping. Based on that inforation, tracker can easily know that which ISP the peers belong to. As for the client, the function of periodic neighbor relationship

4 anageent based on PCIs should be added. We believe the slightly odifications on tracker and client are acceptable, and it greatly benefits both service provider and end-user. III. EVALUATIONS A. Experient Setup We developed a discrete-event siulator to evaluate the perforance of BitTorrent protocol with ACNS. This siulator odels peer activities (e.g. joins, departures) as well as other BitTorrent echaniss (e.g. TFT, etc.) in detail. We ipleent ACNS, original BitTorrent with no changes (called as Native BitTorrent) and biased neighbor selection [11] schee in which a node only has 1 fixed outside neighbor. All nodes in the network have asyetric upload/download bandwidths. The network (i.e. torrent) consists of 1,000 peers. Since ost of the current users are ADSL subscribers, the upload bandwidth of each peer is set to 512 Kbps while the download bandwidth is 2,048 Kbps. It is assued that the file to distribute is a very popular one, so all the peers join in the syste as a flash crowd [7]. The peer leaves the network as soon as it copletes downloading. There is one original seed in the syste. The original seed does not depart until all the nodes finish downloading. The default upload bandwidth of the seed is 5Mbps. 1,000 nodes are assigned to 10 ISPs, each with roughly 100 nodes. All the ISPs are copletely connected with each other. The downlink bandwidth of each ISP is 200Mbps, while the uplink bandwidth for BitTorrent application is liited, considering the traffic-shaping devices deployed by ISPs. The default uplink bandwidth of each ISP is 30Mbps. However, we explore the ipact of bandwidth throttling. The default file size is 100MB. The file is divided into pieces, each with 256KB. We ainly focus on three perforance etrics. M1. Proportion of cross-isp traffic. Let T intra-isp denotes the traffic generated within ISPs to distribute the file to all the nodes, T cross-isp denote the traffic between ISPs. Then the proportion of cross-isp traffic (P_Cross) is defined as follow, where n is the nuber of nodes. Tcross ISP Tcross ISP P_ Cross = = 100% Tcross ISP + Tint ra ISP n sizeof ( file) M2. Maxiu download tie: It is defined as the axiu value of nodes download coplete tie. M3. Mean download tie: It is defined as the ean value of nodes download coplete tie. Obviously, the first etric deonstrates the efficacy of our schee in ters of traffic shaping, while the last two tell us the download rate perforance. B. Perforance Results Experiental results are reported below in ters of 3 perforance etrics. We copare our schee ACNS-enabled BitTorrent perforance with the native BitTorrent and bias neighbor selection based BitTorrent. The siulations are run ultiple ties and the differences between the are neglectable. Hence, we only present the result of one run. 1) Relative ACNS Perforance First, we evaluate the perforances of different schees (a) Proportion of the cross-isp traffic (b) CDF of coplete tie Fig. 3 Relative perforance of ACNS enabled BitTorrent protocols. Fig. 3 depicts the results. Fig. 3(a) copares the proportion of cross-isp traffic. The cross-isp traffic only contributes 15.19% in ACNS, while it contributes 55.18% and 22.18% in native and biased BitTorrent, respectively. Thus, copared with native and biased BitTorrent, ACNS-enabled BitTorrent reduces the cross-isp traffic by about 72.5% and 31.5%, respectively. Fig. 3(b) shows the cuulative distribution of download coplete tie. It is found that, in ACNS-enabled BitTorrent, 95.4% percent of the total nodes coplete the download within 2,000 seconds, while only 17.0% percent and 59.1% percent within 2,000 seconds in native BitTorrent and bias neighbor selection based BitTorrent, respectively. We also copute the ean download tie. The results show that the ean download tie is 2,376 seconds for native BitTorrent, 1,945 seconds for bias neighbor selection based BitTorrent, and 1,660 seconds for ACNS-enabled BitTorrent. Hence, copared with native and biased BitTorrent, ACNS-enabled BitTorrent iproves the download rate by about 30.1% and 14.6%, respectively. 2) Ipact of Rechoose Neighbor Interval (RNI) Next, we explore the ipact of RNI on the perforance of ACNS. Recall that RNI should be bigger than 30 seconds. Fig. 4 plots the results when RNI varies fro 60 seconds to 600 seconds. The download tie is relative large when RNI is 60 seconds. This is because the interval is so sall that an outside neighbor ay have no chance to be optiistic unchoked before the connection is disconnected. However, once the RNI grows to 120 seconds, the download tie dwindles sharply. As to the cross-isp traffic, it grows fro 13.9% to 28.3% as RNI increases fro 60 seconds to 600 seconds. This stes fro the fact that, for a larger interval, it takes a long tie for the nuber of outside neighbors of a node drops to a sall value, which in turn benefits the piece exchanges aong ISPs. We choose 120 seconds for the default value of the interval.

5 Fig. 4 Ipact of Rechoose Neighbor Interval Fig. 5 Effect of Required Threshold 3) Effect of Required Threshold (RT) In this set of experients, we evaluate the effect of Required Threshold. Fig. 5 reports the results. Two observations are notable. First, when RT is as sall as 0.5, the download tie is relatively large. This is because, in this case, a node always has fewer outside neighbors. The file pieces that the ISPs really need are not fetched into the ISPs in tie. However, once the threshold grows to 1, the download tie decreases sharply. Second, the proportion of cross-isp traffic grows with the growth of the threshold for that a node always has ore outside neighbors when the threshold is larger. We choose 1 as the default value of the threshold. 4) Ipact of bandwidth throttling We evaluate the ipact of bandwidth throttling on the perforance by varying the ISP uplink bandwidth. Fig. 6 reports the results. In the native BitTorrent, node neighbors are randoly selected and ost of file pieces are exchanged aong ISPs. Thus, the uplink bandwidth is the bottleneck, which is the reason why the download tie in native BitTorrent decreases with the growth of uplink bandwidth. On the other hand, when the bandwidth is liited, a node ay only get fewer file pieces fro outside neighbors, while a considerable nuber of file pieces are gotten fro inside neighbors. This is the reason why the cross-isp traffic in native BitTorrent is saller when the uplink bandwidth is only 10Mbps. It is also found that the uplink bandwidth has a relatively sall ipact on the perforances of ACNS-enabled and bias neighbor selection based BitTorrent. However, ACNS outperfors others. Proportion of cross-isp traffic IV. CONCLUSION AND FUTURE WORK In this paper, we propose ACNS, an adaptive neighbor selection algorith for BitTorrent applications to reduce cross-isp traffic and enable fast content distribution. Rather than having a fixed nuber of outside neighbors, ACNS suggests that a node should adaptively select the ost cople- Fig. 6 Ipact of bandwidth throttling entary peers in other ISPs to connect with. To this end, we introduce piece copleentary index to evaluate the copleent of two nodes locating in different ISPs and then, depict how to leverage the index to guide the adjustent of neighbor relationships. The experiental results show the efficacy of our schee in ters of two perforance etrics, naely download tie and the cross-isp traffic. Specially, copared with the fixed biased neighbor selection schee, ANCS cuts down the cross-isp traffic by ore than 31% and iproves the download rate by about 15%. As a future work, we will study the piece selection strategy in BitTorrent to iprove the traffic locality and integrate the proposed schee into the open-source BitTorrent. ACKNOWLEDGE This work is supported by National Basic Research Progra of China with grant No.2007CB310702, by National Natural Science Foundation of China with grant No and No REFERENCES [1] A. Legout, G. Urvoy-Keller, P. Michiardi, Rarest first and choke algoriths are enough, proceedings of the 6th ACM SIGCOMM conference on Internet easureent, Rio de Janeriro, Brazil, [2] A. R. Bharabe, C. Herley, and V. N. Padanabhan, Analyzing and iproving a bittorrent networks perforance echaniss, INFOCOM [3] B. Cohen. Incentives build robustness in BitTorrent, P2PEcon, [4] D. Qiu and R. Srikant. Modeling and Perforance Analysis of BitTorrent-like Peer-to-Peer Networks, SIGCOMM, Sep [5] G. Shen, Y. Wang, Y. Xiong, B.Y. Zhao, Z. Zhang HPTP: Relieving the tension between ISPs and P2P, Proceedings of IPTPS, [6] H. Xie, A. Krishnaurthy, A. Silberschatz, Y. R. Yang, P4P: Provider Portal for Applications, SIGCOMM, [7] J.A. Pouwelse, P. Garbacki, D.H.J. Epea, H.J. Sips, A Measureent Study of the BitTorrent Peer-to-Peer File-Sharing Syste, technical report PDS , May [8] K. Guadi, R. Dunn, S. Saroiu, S. Gribble, H. Levy, and J. Zahorjan. Measureent, odeling, and analysis of a peer-to-peer file-sharing workload, in Proc. of SOSP 03, Bolton Landing, Oct [9] L. Guo, S. Chen, Z. Xiao, E. Tan, Measuresnts, analysis and odeling of bittorrent-like systes, IMC, [10] R. Bindal, P. Cao, W. Chan, J. Medved, G. Suwala, T. Bates, and A. Zhang, Iproving traffic locality in bittorrent via biased neighbor selection, ICDCS, [11] T. Wang, L. Wen, W. Li, J. Tao, A. Wang, and F. Baker Traffic Shaping in BitTorrent Systes by Centralized Hierarchical Peer-node Assignent, ICC, [12] V. Rai, S. Sivasubraanian, and S. Bhulai., A ulti Phased approach for odeling and analysis of the BitTorrent protocol, Proc. of IEEE ICDCS 2007, Toronto, Canada, [13] Internet Study 2007, Available: [14] iplane. Available: