TOBAB: A Trend-Oriented Bandwidth Adaptive Buffering in Peer-to-Peer Streaming System *

Transcription

1 TOBAB: A Trend-Oriented Bandwidth Adaptive Buffering in Peer-to-Peer Streaming System * Sirui Yang, Hai Jin, Xuping Tu Cluster and Grid Computing Lab Huazhong University of Science and Technology, Wuhan, , China hjin@hust.edu.cn Abstract. Multimedia streaming application is increasingly popular. P2P mode makes it much more suitable for large-scale users to participate into one single application. However, most effort is spent on issues such as overlay construction and content delivery. As a foundational aspect in P2P-based multimedia systems, buffer management needs more exploration and traditional measures should be refined. In this paper, a bandwidth adaptive buffer exchange strategy is proposed, which is similar to the slow start process in TCP protocol. A novel algorithm is applied in a live media streaming system, called Anysee, and has been proven resilient to the bandwidth fluctuation in P2P networks. 1 Introduction Streaming applications have become increasingly popular. As the centralization architecture always faces the problems of single-failure-point and server deployment cost, the streaming applications gradually appear to be Peer-to-Peer (P2P) fashion. Previous studies consider two key components supporting streaming applications in P2P networks: overlay construction and content delivery [1]. Due to the uncertain arrival time of each packet in P2P media streaming systems, designing a dynamic and adaptive buffer management policy is more important than ever before. In multimedia applications, buffer management is a guarantee for better Quality of Service (QoS). Due to the fluctuant bandwidth and the induced uncertainty of data packets arrival, a well designed strategy concerning on buffer management is necessary for a robust multimedia architecture. As no feasible solutions are able to measure connection metrics among peers, buffering algorithms with parameters such as bandwidth can not be scientifically executed. The dynamic characteristic of P2P network, in large geographic coverage and with peers joining and leaving the network frequently, enlarges the difficulty of getting a comparatively exact bandwidth between peer pairs. Besides, to collect media data required by media players, mesh structure in P2P media streaming * This paper is supported by National Science Foundation of China under grant , and CNGI projects under grant CNGI A and CNGI D.

2 systems is proposed and multi-source policy is adopted first in CoolStreaming [12]. Multi-source solution imports a new problem: how to assign requests to neighboring peers with consideration of balance between local demand of media data and bandwidth among several peers. In this paper, we propose an algorithm resembling the TCP slow-start process, called TOBAB (Trend-Oriented Bandwidth Adaptive Buffering). It adjusts the request quantity sent to each source peer. The algorithm does not need the accurate bandwidth value between two peers. However, it sends requests which can be adaptive (tread on the heels of the bandwidth trend) and greedy (take full advantage of available bandwidth). The rest of this paper is organized as below: in section 2, related works on buffer management is discussed. In section 3, the TOBAB strategy and buffer management in our application, Anysee, are presented. In section 4, performance of the algorithm is analyzed and evaluated. We conclude this paper in the last section. 2 Related Work 2.1 Bandwidth Measure There has been much work on developing techniques for estimating the capacity and the available bandwidth of network paths based on end-point measurements [3]. Many of the proposed capacity estimation schemes are based on the packet-pair principle [6]. Some mathematical methods like a simple stochastic analysis of the bandwidth estimation in the context of a single congested node with asymptotically accurate bandwidth estimators are proposed. In packet-pair principle, a generic queuing model of an Internet router is developed and the estimation problem assuming renewal cross traffic at the bottleneck link is solved [5]. A link bandwidth measurement method using a deterministic model of packet delay is proposed [11]. It derives both the packet-pair property of FIFO queuing networks and a new technique, called packet tailgating, for actively measuring link bandwidths. Some tools designed to estimate metrics are fully compared and analyzed [7][8][9][10]. Existing bandwidth estimation tools measure one or more of three related metrics: capacity, available bandwidth, and bulk transfer capacity (BTC). These tools implement tactics in previous studies. In summary, IP networks do not provide explicit feedback to end hosts regarding the load or capacity of the network. Instead hosts use active end-to-end measurements in an attempt to estimate the bandwidth characteristics of paths they use [4]. However, these studies do not actually find a method to get the physical bandwidth value in network links. Some of them concern on the capacity of the slowest link of the path, while others mainly discuss the smallest average unused bandwidth among the routers of an end-to-end path. It is hard for application layer to get an accurate and meaningful bandwidth situation for a node in the network.

3 2.2 Buffer Management Strategy in P2P Streaming Traditional multimedia applications with prefixed and linear buffer management policy do not adapt to dynamic P2P environment [17]. In structured P2P multimedia streaming systems [13][16], where multicast trees are constructed mostly, peers receive media data from one supplying peer (called father), and send the data to one or more consuming peers (called children). In first version of [13], the buffer size is set to 40 (be able to store 40-second data). When half of it is fulfilled, the application starts to transfer the media data to media player [15]. In [12][14], where mesh architecture and multi-source are introduced, buffer design needs to take request assignation and bandwidth cooperation into account. A buffer exchange algorithm for peers is discussed [12] to transmit media data pieces effectively in P2P streaming systems. First, an 80-bit Slot Map (SM) is generated on each peer, with each bit representing corresponding buffer block. A SM-like packet is constructed periodically where each bit denotes the corresponding block is ready or not. By exchanging and comparing SM-like packet, application determines which blocks need to be requested from one peer, and which from another. The algorithm mainly depends on bandwidth and the deadline of a block as it should be sent to media player in time. The algorithm becomes a garnish if bandwidth value is not veracious. It does not consider the conflicts among requests in peers, which possibly makes an urgent block request to a supplying peer with insufficient bandwidth. 3 Bandwidth Adaptive Buffer Management Bandwidth plays a pivotal role in P2P media streaming systems. It affects packet transmission and most algorithms execution is influenced by bandwidth. Basically, unfulfilled requests decrease program efficiency and QoS to some extent. Thus, fewer requests for media data should be sent out if current bandwidth is awful, and vice versa. As bandwidth is hard to measure exactly, algorithms should be buffer-adaptive. 3.1 Buffer Value Unnecessary in P2P Streaming Practically, instant bandwidth value (such as 500Kb/s at 7:10am) is not much instructive in P2P environment. In common case, a host may run several P2P applications (or other network programs) at one time. The bandwidth (especially the in-bound bandwidth) is shared by these applications. In most P2P topologies (like unstructured meshes), a peer connects several neighbors [19]. Obviously, neighbors share the bandwidth (generally the out-bound bandwidth) of the peer. To measure an instant bandwidth between two peers is not significant, as it varies frequently. 3.2 Buffer Exchange Algorithm

4 In CoolStreaming [12], the Buffer-Map exchange algorithm works well in fixedbandwidth environment, or the bandwidth value in peer pairs is assumed foreseen. This is always unreal for most P2P applications. We extend it adaptive to bandwidth fluctuation (not prearrange an assumption value any more) in our research. In our buffer management, we plot a buffer composed by slots. One slot represents media content in one second. It is built-up by several packets. We prescribe this kind of policy because data in one second is superfluous to be packed and transmitted. Each packet is set to be the maximal size of a UDP packet (S udp ), unless it is the last packet of a slot, which may be less than S udp. Compared to CoolStreaming, the buffer map is replaced by Slot Map. Correspondingly, each bit represents not a packet but a slot. When generating a new packet, the timestamp is checked whether it belongs to a new slot. Therefore, the last generated slot is called fulfilled or ready with its corresponding buffer-map bit set to 1. Otherwise, the bit is 0, as default. With preparation done and the time point (for buffer exchange) coming, by checking SM, the program starts to count slots which are not ready. Then it calculates its neighbors SM by counting the SM-like packets received from neighbors. According to the result, it sends requests to each neighbor. Before the deadline T deadline elapsed, neighboring peers return the requested data. Buffer manager records the transmission status, with which the bandwidth situation is analyzed. 3.3 TOBAB Algorithm The exchange algorithm resembles CoolStreaming but we adjust the bandwidth part. In brief, as requests are sent and responded, bandwidth situation is recorded according to response status. In the buffer request algorithm, bandwidth is first set to a default value based on the landmark difference of the local and remote peers. A linear increase, multiplicative decrease standard is used. This standard is chosen because the adaptive and greedy algorithm is expected. However, pure TCP-like protocol does not fit the dynamic environment in P2P networks. It should be further mended to be stable and effective. Linear increment is adopted for greed purpose and it causes little jitter. As available bandwidth lessens, the multiplicative decrement starts being applied. Meanwhile we adjust the increment not constant (but still linear) and more effective. The increment is direct proportion of the difference between requested and responded packets. TOBAB algorithm is represented below. The number of requested data packets is initialized as default (e.g. 20 packets are requested for N neighbor peers). When T deadline passes, the program counts the number of completed packets. If all requested packets are brought to success (i.e. 20 packets or more), the bandwidth is considered to be underestimated. Thus the bandwidth value is raised linearly (e.g. increasing 2 packets each time). Otherwise, not all requested packets are transferred punctually (e.g. only 12 arrived). The bandwidth, in this case, is assumed to be overestimated. The bandwidth is thus reduced partially (e.g. mediacy of the requested and the responded, 16 packets). We also record the finishing time of the process of transmission (T tran ). In the condition that T tran is much less than T deadline (e.g. less than

5 T deadline /2) we get an approximate conclusion that the bandwidth is much underestimate. In this case, we accelerate the increasing pace by a ratio r. Thus increased packets is not 2 but 2 r. Compared to the mild increasing style above, this incremental way is sharp. We use the mild increment because we need the request number not to exceed actual bandwidth ability, which will lead to a failure of satisfying the request and reduce following QoS of applications. While for the VBR (Variant Bit-rate) media streaming, which contains varying size of data packets, packet number may not directly stands for bandwidth unit. To fit for this environment, simply adjusting the packet number to packet size in the algorithm will be effective and suitable. TOBAB algorithm InitReqNum: Initial number of packets to request. ReqNum: Number of requested packets. RespNum: Number of responded packets. IncreNum: Mild incremental packets. IncreRt: Ratio of sharp incremental packets. TranTime: Transmission time of last request. Deadline: Time of the period allowed for transmission. ReqNum = InitReqNum; while (buffer manager is running){ RespNum = getresponsepacketnumber(); if (ReqNum RespNum) ReqNum = (ReqNum + RespNum)/2; else{ if(trantime Deadline/2) ReqNum += IncreNum; else ReqNum += (RespNum ReqNum) IncreRt; } } 4 Performance Evaluation We now evaluate the TOBAB algorithm. In all following simulations, we set the parameters as below: Maximal slots of AbsSet: 80; IncreRt: 0.4; IncreNum; 2; InitReqNum: 20. Figure 1 shows a totally random situation that host H requests packets according to TOBAB and neighbors respond at random. In this case, bandwidth changes randomly. It can be noticed that the request curve is at response curve s heels (response curve stands for the bandwidth dynamic trend).

6 Fig. 1. Random situation (Peer s bandwidth is randomly changing) In more accordance with real network, peers have their maximal in-bound limits. Figure 2, 3 and 4 simulate the fact that a peer s response sum should not exceed the maximal in-bound bandwidth. In TOBAB, anterior neighbors may be prior to occupy available in-bound bandwidth and complete their transmission, while the posterior may get less available bandwidth. If host H is able to finish all transmissions, it becomes the random situation above. Fig. 2. Peer behavior with downstream limit (Request to 1st neighbor) Fig. 3. Peer behavior with downstream limit (Request to 2nd neighbor)

7 Fig. 4. Peer behavior with downstream limit (Request to 3rd neighbor) We simulate the real situation whose responses obey both the in-bound bandwidth limit and the neighbors in-bound limit (i.e. out-bound bandwidth limit of H). Here we assume the local peer owns 3 neighbors. The results are in Figure 5, 6 and 7. Fig. 5. Live peer s process (Request to 1st neighbor) Fig. 6. Live peer s process (Request to 2nd neighbor)

8 Fig. 7. Live peer s process (Request to 3rd neighbor) Fig. 8. Reality exponent (Peer s bandwidth obeys the exponential distribution) In large scale networks, local environment trends to be stable, and the function will sequel to a constant (a limit). To measure the speed of the trend to this limit, we assume that the response curve follows the exponential distribution. That means the bandwidth is easily available at the beginning, however, as more active peers joining and other applications running, rest bandwidth becomes much less, until it gets into a vibrancy in a tiny scope. The simulation result is shown in Figure 8. In the simulation, we assume the maximal bandwidth is able to transfer 80 packets in time. The performance result analysis is listed in Table 1. In the table, the request loop in TOBAB algorithm is executed 50 times, as shown in figures above. Overestimating times means the phenomenon that request packets is beyond the bandwidth ability, which means unsatisfactory. Tendency Speed is the time used that request is always near the reality, which describes the adaptation (allowing an offset of a 2-packet, i.e. the IncreNum). Accuracy is the total times that request is near the reality, which can describe the accuracy of the request in TOBAB. Average value is the mean value for the four situations. We simulate four situations while their randomization characteristics decrease one by one. According to the statistics in Table 1, we can find out that, overestimating seldom occurs in all simulations except Random. If real bandwidth converges to a limit, TOBAB will be adaptive to the limit very soon (e.g. in Exp. ). The result is in

9 less than 7 steps, i.e. 7 seconds in Anysee. Accuracy is very high in this simulation (more than 86%). From the first three simulations, TOBAB will be more exact as the random characteristics decrease. But it is highly effective in regular distributions. According to distribution analysis in [18], our algorithm plays its role very well in real networks. Besides, although the initial requesting packet number is predefined in TOBAB, casually planned parameters will not affect the efficiency much, as the algorithm converges rapidly enough. Table 1. Performance of TOBAB Random Downstream-Limit Live Exp. Avg. Overestimating Tendency Speed Accuracy Conclusion and Future Work This paper proposes a bandwidth adaptive data request algorithm, TOBAB. The algorithm fits for different situations of the P2P dynamic environment. TOBAB has been proven resilient to the bandwidth fluctuation in P2P networks. It maximizes the bandwidth utilization through making track for the dynamic available bandwidth. The algorithm is especially useful to avoid the exceeding-demand problem, i.e. data request is beyond current bandwidth ability, which will lead to service unsatisfactory and a vicious circle. The efficiency of TOBAB algorithm depends much on the parameters such as IncreRt, IncreNum and InitReqNum. In different topology, different groups of parameters will make the system most effective. Tendency Speed in our evaluation can be lower in detailed environment as algorithm improves. We leave these to our future research work. References 1. V. Agarwal and R. Rejaie, Adaptive Multi-Source Streaming in Heterogeneous Peer-to-Peer Networks, Proceedings of SPIE Conference on Multimedia, Computing and Networking, California, Jan J.-I. Namgung, S.-Y. Shin, S.-H. Park, L.-S. Lee, and D. Jeong, Self-Organizing P2P Overlay Network Applying Dynamic Landmark Mechanism for Contents Delivery Network, Proceedings of 3rd ACIS International Conference, 2005, pp K. Lakshminarayanan, V. N. Padmanabhan, and J. Padhye, Bandwidth Estimation in Broadband Access Networks, Technical Report MSRTR , May R. S. Prasad, M. Murray, C. Dovrolis, and K. Claffy, Bandwidth Estimation: Metrics, Measurement Techniques, and Tools, IEEE Network, Vol.17, No.6, Nov.-Dec S.-R. Kang, X. Liu, M. Dai, and D. Loguinov, Packet-Pair Bandwidth Estimation: Stochastic Analysis of A Single Congested Node, Proceedings of the 12th IEEE International Conference on Network Protocols, 2004, pp

10 6. N. Hu and P. Steenkiste, Estimating Available Bandwidth Using Packet Pair Probing, Tech. Rep., School of Computer Science, Carnegie Mellon University, CMU-CS , C. Dovrolis, P. Ramanathan, and D. Moore, What Do Packet Dispersion Techniques Measure? Proceedings of Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE INFOCOM), Apr. 2001, pp B. Melander, M. Bjorkman, and P. Gunningberg, A New End-to-End Probing and Analysis Method for Estimating Bandwidth Bottlenecks, Proceedings of IEEE Global Internet Symposium, V. Ribeiro, R. Riedi, R. Baraniuk, J. Navratil, and L. Cottrell, pathchirp: Efficient Available Bandwidth Estimation for Network Paths, Proceedings of Passive and Active Measurements (PAM) Workshop, Apr N. Hu and P. Steenkiste, Evaluation and Characterization of Available Bandwidth Probing Techniques, IEEE Journal on Selected Areas in Communications, K. Lai and M. Baker, Measuring Link Bandwidths Using a Deterministic Model of Packet Delay, Proceedings of the ACM SIGCOMM, X. Zhang, J. Liu, B. Li, and T.-S. P. Yum, CoolStreaming/DONet: A Data-Driven Overlay Network for Efficient Live Media Streaming, Proceedings of 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), X. Liao, H. Jin, Y. Liu, L. M. Ni, and D. Deng, Anysee: Scalable Live Streaming, Proceedings of INFOCOM 06, C. Zhang, H. Jin, D. Deng, S. Yang, Q. Yuan, and Z. Yin, Anysee: Multicast-based Peerto-Peer Media Streaming Service System, Proceedings of Asia-Pacific Conference on Communications 2005, Perth, Western Australia, Oct V. N. Padmanabhan, H. J. Wang, P. A. Chou, and K. Sripanidkulchai, Distributing Streaming Media Content Using Cooperative Networking, Proceedings of NOSSDAV 02, USA, May Y. Cai, Z. Chen, and W. Tavanapong, Video Management in Peer-to-Peer Systems, Proceedings of the 5 th IEEE International Conference on Peer-to-Peer Computing, M. E. J. Newman, The Structure and Function of Complex Networks, SIAM Review, Vol.45, No.2, 2003, pp Y. Liu, L. Xiao, X. Liu, L. M. Ni, and X. Zhang, Location Awareness in Unstructured Peer-to-Peer Systems, IEEE Transactions on Parallel and Distributed Systems, Vol.16, No.2, 2005, pp