Evaluation of traffic dispersion methods for synchronous distributed

Int. J. Applied Systemic Studies, Vol. 3, No. 1, 2010

Evaluation of traffic dispersion methods for synchronous distributed multimedia data transmission on multiple links for group of mobile hosts Yoshia Saito* Faculty of Software and Information Science, Iwate Prefectural University, 152-52 Sugo, Takizawa, Iwate 020-0193, Japan E-mail: [email protected] *Corresponding author

Susumu Ishihara Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka 432-8011, Japan E-mail: [email protected]

Hiroshi Mineno Faculty of Informatics, Department of Computer Science, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka 432-8011, Japan E-mail: [email protected]

Tadanori Mizuno Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka 432-8011, Japan E-mail: [email protected]

Takashi Watanabe Graduate School of Science and Technology, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu, Shizuoka 432-8011, Japan E-mail: [email protected]

Copyright © 2010 Inderscience Enterprises Ltd.

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Y. Saito et al. Abstract: In this paper, we propose a multimedia data transmission method, Multitrack, that achieves high-speed and efficient data transmission between multiple mobile hosts and the internet. Users of the hosts in a cluster can see a high quality streaming video by sharing the received multimedia data cooperatively with the other hosts. In Multitrack, it is important to disperse traffic according to the quality of multiple links in order to offer a stable multimedia data transmission. We also propose an effective traffic dispersion method for this environment, and evaluate this method and other basic methods by simulations. Keywords: multimedia communications; mobile computing; multiple links; traffic dispersion. Reference to this paper should be made as follows: Saito, Y., Ishihara, S., Mineno, H., Mizuno, T. and Watanabe, T. (2010) ‘Evaluation of traffic dispersion methods for synchronous distributed multimedia data transmission on multiple links for group of mobile hosts’, Int. J. Applied Systemic Studies, Vol. 3, No. 1, pp.89–101. Biographical notes: Yoshia Saito received his PhD Degrees from Shizuoka University, Japan, in 2006. He had been an expert researcher of National Institute of Information and Communications Technology (NICT) from 2004 to 2007, Yokosuka, Japan. He is currently a Lecturer at Iwate Prefectural University since October 2007. His research interests include internet broadcasting and mobile computing. He is a member of IEICE, IPSJ, IEEE, and ACM. Susumu Ishihara received his BE, ME and PhD in Electronics Engineering from Nagoya University in 1994 and 1999. From 1998 to 1999, he was a JSPS Special Researcher. Since 1999, he has been with Shizuoka University, where he is an Associate Professor of the Graduate School of Science and Technology. He was a Visiting Researcher at the University of California, Irvine in 2008. His research interests are in the areas of mobile TCP/IP networking, mobile ad hoc networking and sensor networks. He is a member of IEEE, IEEE Communication Society, IEEE Computer Society, ACM SIGMOBILE, ACM SIGCOMM, IEICE and IPSJ. Hiroshi Mineno received his BE and ME Degrees from Shizuoka University, Japan in 1997 and 1999, respectively. In 2006, he received the PhD Degree in Information Science and Electrical Engineering from Kyushu University, Japan. Between 1999 and 2002, he was a Researcher in the NTT Service Integration Laboratories. In 2002, he joined the Department of Computer Science of Shizuoka University as an Assistant Professor. His research interests include sensor networks as well as heterogeneous network convergence. He is a member of IEEE, ACM, IEICE and IPSJ. Tadanori Mizuno received the BE Degree in Industrial Engineering from the Nagoya Institute of Technology in 1968 and received the PhD Degree in Engineering from Kyushu University, Japan, in 1987. In 1968, he joined Mitsubishi Electric Corporation. Since 1993, he is a Professor of Shizuoka University, Japan. Now, he is a Professor of Graduate School of Science and Technology of Shizuoka University. His research interests include mobile computing, distributed computing, computer networks, broadcast communication and computing, and protocol engineering. He is a member of Information Processing Society of Japan, IEICE, the IEEE Computer Society, ACM and Informatics Society.

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Takashi Watanabe received his BE, ME and PhD Degrees in Communications Engineering from Osaka University in 1982, 1984 and 1987, respectively. In 1987, he joined Faculty of Engineering, Tokushima University as an Assistant Professor. In 1990, he moved to Faculty of Engineering, Shizuoka University. He was a Visiting Researcher at University of California, Irvine from 1995 through 1996. He is currently a Professor of Graduate School of Science and Technology, Shizuoka University. He is a translator of Japanese version of 802.11 Wireless Networks: The Definitive Guide (published by O’REILLY, 2003). His current research interests include computer networks, mobile networking, ubiquitous networks, ad hoc networks and sensor networks. He is a member of IEEE, IEEE Communications Society, IEEE Computer Society, ACM SIGMOBILE and IPSJ.

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Introduction

In recent years, third Generation (3G) mobile cellular technologies have enabled high speed data transmission of more than 100 kbps in the mobile computer environment. This opens the way for high quality multimedia data distribution for many mobile users. The transmission speed on wireless links is, however, potentially slower than wired links. Even if a user uses a mobile host with a high-speed link, it is difficult to transmit more high quality multimedia contents when the server is not capable of handling the heavy load of the streaming service. Because using multiple unicasting to distribute multimedia data to multiple users imposes a heavy load on the server, it is more effective to use multicasting instead of multiple unicastings to mitigate the load on the server. Unfortunately, it does not mitigate the traffic of the last hop from the server to the client. To reduce the traffic on narrow wireless links, but still provide a service to mobile clients without decreasing the quality of the multimedia data, the traffic has to be dispersed to multiple links. In this paper, we propose a multimedia data transmission method that offers high-speed data transmission to the clients without increasing the server load by dispersing traffic to multiple wireless links. To achieve high-speed and efficient multimedia data transmission for multiple mobile hosts, we focused on a situation where multiple users of mobile terminals watch the same streaming video in place. For example, there are situations in which some users watch the same streaming video news programme using their mobile computers in a bus, or several friends gather and watch the TV programme at each mobile computer, etc. Mineno et al. proposed SHAKE (SHAring multiple paths procedure for cluster networK Environment) (Mineno et al., 1999). In this procedure, mobile hosts are temporarily connected each other using fast wireless links to form a network (we call this network a cluster-type network) in which the traffic is dispersed to multiple wireless links between the cluster and the internet. This system can achieve high-speed communication through long distance wireless links. Furthermore, by broadcasting the multimedia data transmitted by SHAKE to the terminals in the cluster, more efficient data distribution is achieved because the server does not need to send the data to each host. We call this proposed method ‘Multitrack’ (MULTImedia data TRAnsmission for Cluster networK).

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Multitrack offers a high quality streaming video and audio transmission to the clients on low-speed links without increasing the server load. Data transmission on multiple links tends to be unstable due to differences in delay and bandwidth. Therefore, methods for traffic dispersion influence the efficiency of multimedia data transmission. We also propose an effective data dispersion method to achieve stable data transmission and compare it with other methods by simulations. The remainder of this paper is organised as follows. In Section 2, we outline SHAKE. In Section 3, we describe the details of Multitrack and propose an efficient packet distribution method. In Section 4, we give the simulation results, comparing the proposed method with other methods. In Section 5, we summarise our results and briefly describe the future directions of our research.

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SHAKE

Multitrack is based on the concept of SHAKE. Figure 1 shows an example of communication by SHAKE. In SHAKE, two or more mobile hosts are temporarily connected each other with high-speed wireless links (e.g., wireless LAN, Bluetooth etc.) to built a cluster-type network. Multiple links connected to the internet from the cluster are used simultaneously to transmit data between them. Therefore, high-speed data transmission is achieved, even if the client has only a low-speed link to the internet. Figure 1

Example of communication with SHAKE (see online version for colours)

However, only some of the hosts in the cluster benefit from using multiple links in the scenario described above. For example, when the server transmits a movie, only one user of the receiver host can watch the movie, the users of other hosts that provide their links to the receiver host cannot watch it. If terminals that lend their own links to the other hosts can receive the transferred multimedia contents, all hosts in the cluster can obtain a benefit from the cluster. Multitrack is a method to distribute multimedia data transmitted by SHAKE to all mobile hosts in a cluster.

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Multitrack

3.1 Overview of multitrack We assume the following situation in which Multitrack is used. Two or more mobile hosts are temporarily connected, as in SHAKE, and the hosts are connected to each other with high-speed wireless links (e.g., wireless LAN, Bluetooth etc.) to built a cluster-type network. High-speed multimedia communication is achieved by dispersing traffic to an application layer, as shown in Figure 2. The dispersed data is sent to each terminal and relayed to each host. By merging the data at each host, users of each mobile host can watch a high quality streaming video. Furthermore, the load on the streaming server is mitigated. Figure 2

System architecture

Multitrack can be used for watching popular sports such as baseball and soccer, and receiving up to date traffic information when people are involved in traffic congestion. It is useful in distributing multimedia contents used by several people who gather at the same place as just described. Multitrack is similar to the patching and merging methods for multimedia streaming proposed in Eager et al. (1999) and Sen et al. (1999a) at the point of merging fragmented data. However, those methods are different to the Multitrack because they aim at mitigating the load on a server and the start-up delay, and they do not aim to accelerate the data transfer. Multitrack is also similar to multicast (Floyd et al., 1997) and Contents Distribution Network (CDN) technologies such as vTrails technology (http://www. vtrails.com/), which achieve efficient distribution using a P2P network and a multimedia stream caching mechanism (Sen et al., 1999b) to attempt to achieve efficient data distribution to multiple terminals. These technologies mainly benefit the server to reduce its load. On the other hand, Multitrack aims to benefit both the server and the clients.

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3.2 Traffic dispersion methods Traffic dispersion methods strongly influence the efficiency of multimedia data transmission in Multitrack. Stable data transmission is not achieved without adequate traffic dispersion that addresses link qualities such as bandwidth and delay. To achieve stable multimedia data transmission, the following two conditions should be satisfied. The first is that packets arrive in order, because out-of-order packet arrival causes frame loss. The second condition is that the receiving buffer at the client must not be empty, because buffer depletion causes continuous frame loss. Many researchers have studied end-to-end congestion control technology to adapt to fluctuations in the network conditions (Floyd and Fall, 1998). Particularly, as research has focused on congestion control in multimedia data transmission, technologies that control the transmission rate by delay or packet loss rate (Rejaie et al., 1999; Sisalem and Schulzrinne, 1998), and quality control technologies for streaming video (Rejaie et al., 2000; McCanne et al., 1996) have been proposed. However, these works have not focused on dispersing data flows to multiple links. In Multitrack, though, packets in a data flow are dispersed to multiple links at an adequate rate. Therefore, Multitrack additionally requires traffic dispersion methods to achieve stable data transmission. At first, we evaluated the following basic packet dispersion methods to find effective ones in Multitrack. •

Method 1 (Border): Packets are cyclically sent to each link according to the bandwidth ratio of each link in a round robin manner.

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Method 2 (Drand): Packets are dispersed at random according to the inverse ratio of the delay of each link.

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Method 3 (Q): Each packet is sent to a link where waiting time of the queue is the shortest.

In Method 1, Ri packets are sent continuously to link i (i begins from 1) when the bandwidth ratio is R1 : R2 : … : Rn. Next, Ri+1 packets are sent to link i + 1, and the remaining packets are sent in the same manner. In Method 2, packets are dispersed to link i at random by the following probability Pi. When the end-end delay time of the links is D1, D2, …, Dn, Pi =

1/ Di

∑

n

1/ D j

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j =1

In Method 3, virtual transmit queues are used to predict waiting time at each link’s transmit queue. Figure 3 shows the model. The parameter i indicates the ID of each link. Here, S, Bi, Li, τi, t are fixed packet size, bandwidth, queue length, the time when a packet is being sent, was sent, and the present time, respectively. Li is recalculated using S, Bi, τi – t when a new packet is transmitted. Waiting time Wi in the transmit queue is estimated by the following equation. Figure 4 illustrates the method. Wi =

S ( Li + 1) + τ i − t. Bi

Evaluation of traffic dispersion methods Figure 3

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Model of virtual transmit queue

Each packet is sent to the link i that has the shortest Wi. However, packets may not arrive at the receiver in order in data transmission via multiple links because of the difference in delays and packet losses. It is important that packets arrive in order to achieve a stable multimedia data transmission because streaming video players need the data in order as early as possible to avoid frame losses. Next, we propose the following Method 4 to achieve it. •

Method 4 (Q+D): Each packet is sent to a link where the sum of the waiting time at transmit queue and delay time reported from the client is the shortest.

In Method 4, a packet is forwarded through a link i that is predicted to deliver the packet first to the receiver. Figure 4

Waiting time at a transmit queue and arrival prediction time (see online version for colours)

Time passing before the packet arrives is estimated by the following equation: Di = Wi + di

where di is the estimated transmission delay time, which is predicted with a feedback message by the client. Each packet is sent to the link i that has the shortest Di. Here, we discuss how these four methods estimate Bi and di. Since bandwidth Bi often changes in real situations, it is difficult to measure it precisely. Therefore, we assume the last 1-hop link has the lowest speed, and Bi is fixed. Half of the round trip delay is measured periodically for use in estimating di.

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Performance evaluation

We evaluated the four packet distribution methods by simulations.

4.1 Simulation model Figure 5 shows the simulation model. In this simulation, the server sends streaming data with b [kbps] and f [fps] to the clients, using n links. The maximum bandwidth Bi of each link and the delay at the starting time are assumed to be known. RTP is assumed to be used for the transport protocol to transmit streaming data and the packet size, including its header, S [bytes]. The following scenario is used in this simulation. First, a frame generated every 1/f [sec] at the server is stored in a packet. When the packet size reaches S [bytes], the server transmits it to a link selected by a packet dispersion method. The host also sends RTCP packets to each client at fixed intervals to allow monitoring of the data delivery. The round trip time and packet loss rate of each link is reported by clients with RTCP. Figure 5

Simulation model

The quality of each link is controlled by changing the delay, packet loss rate and amount of background traffic. Background traffic is sent, in addition to the traffic of the streaming multimedia data. By changing the amount of the background traffic, the available bandwidth of each link can be changed. The delay of each link consists of a fixed delay Dci [msec] and delay fluctuation Dvi [msec], which is generated according to Dci plus the exponential distribution of an average of Dvi [msec] every one second. When a packet reaches the client, a divided frame is reconstructed and stored in the client’s buffer. Finally, frames are extracted from the buffer one by one, in order, every 1/f [sec]. The first frame is extracted from the buffer after being buffered for P [secs]. If a frame that should be displayed at a certain point is not in the buffer, it is treated as a lost frame, and is not retransmitted. The time taken to process packet distribution and frames reconstruction is not considered in this simulation.

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4.2 Experiments and results We run simulations for four cases. In these simulations, we set the background traffic to 0 kbps, the fixed delay Dci to 300 msec, the delay fluctuation Dvi to 100 msec, and the packet loss rate to 1% as the initial value of link quality. Each link was full duplicated and symmetric. We set the packet size S to 1040 bytes, RTCP transmitting interval to l sec, and buffering time P at client buffer to 5 s. Case 1: Affect of background traffic In this case, we evaluated the affect of background traffic. CBR traffic (120 kbps, 15 fps) was sent using two links (96 kbps), or one link (192 kbps) for comparison. 20 s after beginning of the transmission, background traffic (64 kbps) was added to one of the links. Figure 6 shows the simulation results of Case 1. This graph shows the relationship between the elapsed time and the amount of data in the receiver’s buffer. Figure 6 indicates that all methods achieved stable buffering until 20 s after beginning of the transmission, due to the sufficient bandwidth. After that point, the amount of the data in the buffer was stable in only Drand and Q + D. The performance of these methods is not so different from the case of one link that has wide bandwidth. On the other hand, the amount of data in the receiving buffer decreased in Border and Q. Furthermore, many video frames were dropped out at the end. The reason for this difference is that Drand and Q + D were able to change the packet dispersion ratio using feedback from the clients when a link quality was changed. However, Border and Q were not able to change the packet dispersion ratio because these methods do not use feedback from the clients. Therefore, the transmission became unstable. These results show Drand and Q + D outperform other methods when available bandwidth changes. Figure 6

Amount of data in buffer when background traffic is changed during the data transmission

Case 2: Affect of delay In this case, we evaluated the effect of delay. CBR traffic (120 kbps, 15 fps) was sent using three links (Link 1: 128 kbps, Links 2–3: 64 kbps), or one link (256 kbps) for comparison. Buffering time was set to 1 s. 20 s after beginning of the transmission, the fixed delay of Link 1 was changed to 1500 msec. Figure 7 shows the results of Case 2.

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The graph shows the number of frames lost, and indicates that few frames were lost in Q + D. Other methods do not disperse packets, so the packets would arrive in order. Because of this, they dispersed packets to Link 1 which had a long delay, and the packets did not arrive in time when they had to be displayed. These results show that Q + D outperforms other methods when buffering time is short and the order of packet arrival is important. Case 3: Affect of loss rate In this case, we evaluated the effect of packet loss rate. CBR traffic (120 kbps, 15 fps) was sent using three links (Link 1: 128 kbps, Links 2–3: 64 kbps), or one link (256 kbps) for comparison. 20 s after beginning of the transmission, the packet loss rate of Link 1 was changed to 10%, 30%, and 50% in the simulation. Figure 8 shows the number of frame drops in Case 3. The number of frame drops in Q and Q + D increases according to the increase of the packet loss rate. In Q + D, many packets were dispersed to Link 1 that has wide bandwidth, although the loss rate was high. Therefore, we modified the Q + D method so that it may disperse the same packets to multiple links if packets are sent to a link with a high error rate (larger than 5% in this simulation). This method is named Q + D′. Figure 8 shows that Q + D′ dramatically reduces the number of frames lost. The Q + D problem of frame drops in wide bandwidth link can be solved by Q + D′. Figure 7

Number of lost frames when delay is changed during the data transmission (see online version for colours)

Figure 8

Number of frame drops when loss rate is changed during the data transmission

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Case 4: Affect of multiple links and background traffic In this case, we evaluated the effect of multiple links and background traffic. CBR traffic (240 kbps, 15 fps) was sent using five links (Link 1: 128 kbps, Links 2–3: 64 kbps, Links 4–5: 32 kbps), or one link (320 kbps) for comparison. 20 s after beginning of the transmission, background traffic (32 kbps) was added to Links 2 and 3, or 64 kbps background traffic was added in the case of using one link. Figure 9 shows the relationship between the elapsed time and the amount of data in the buffer. Figures 10 and 11 show the relationship between the elapsed time and the sequence number of a packet that reached the client in Drand and Q + D. Figure 9 indicates that Q + D keeps enough amount of data in the receiving buffer 20 s after beginning of the transmission. The Q + D method is not so different from a case of one link that has wide bandwidth. The difference between Q + D and Drand seems small in this graph. However, there were more out-of-order packet arrivals in Drand than in Q + D as shown by Figures 10 and 11. It implies that the number of lost frames increases in Drand if the buffering time is short. Therefore, Q + D outperforms other methods when multiple links are used and the available bandwidths are changed. Figure 9

Amount of data in buffer when multiple links are used and the background traffic is changed during the data transmission

Figure 10 Sequence number of drand when multiple links are used and the background traffic is changed during the data transmission (see online version for colours)

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Figure 11 Sequence number of Q + D when multiple links are used and the background traffic is changed during the data transmission (see online version for colours)

4.3 Modification of Q + D method All these simulation results of Cases 1–4 show that Q + D outperforms other methods in various situations. However, Q + D has a problem that many frames are lost when there are high-speed links with high error rates. It is, therefore, necessary to disperse packets according to the error rate of the links. The method for reducing lost frames described in Case 3, Q + D′ increased traffic, while it reduced frame losses. If the traffic volume is large, the available bandwidth becomes small. Consequently, the number of duplicate packets dispersed by the Q + D′ method should be changed according to the available bandwidth.

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Summary

In this paper, we proposed a multimedia data transmission method, Multitrack that achieves high-speed and efficient data transmission for multiple mobile hosts, and investigated its performance when four different packet dispersion methods are employed. These results clarified that the Q+D method that sends packets to a path where the arrival time of a packet is the shortest achieves the most stable multimedia data transmission, and avoids frame loss and receive buffer depletion. Although Q+D had a problem in that the number of frame drops increases when there were high-speed links with high error rates, this problem was solved by dispersing duplicate packets to other links. In future work, we will implement Multitrack at the application level and evaluate it in practical environments.

References Eager, D., Vernon, M. and Zahorjan, J. (1999) ‘Optimal and efficient merging schedules for video-on-demand servers’, Proc. ACM MULTIMEDIA, November, Florida, USA, pp.199–202. Floyd, S. and Fall, K. (1998) ‘Promoting the use of end-to-end congestion control in the internet’, IEEE/ACM Trans. Network, February, pp.458–472.

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Floyd, S., Jacobson, V., Liu, C., McCanne, S. and Zhang, V. (1997) ‘Reliable multicast framework for light-weight sessions and application level framing’, IEEE/ACM Trans. Network, December, pp.784–803. McCanne, S., Jacobson, V. and Vetterli, M. (1996) ‘Receiver-driven layered multicast’, Proc. ACM SIGCOMM, August, California, USA, pp.117–130. Mineno, H., Ishihara, S., Ohta, K., Aono, M., Ideguchi, T. and Mizuno, T. (1999) ‘Multiple paths protocol for cluster type network’, International Journal of Communication Systems, December, pp.391–403. Rejaie, R., Handley, M. and Estrin, D. (1999) ‘RAP: An end-to-end rate-based congestion control mechanism for realtime streams in the internet’, Proc. IEEE INFOCOM, July, New York, USA, pp.1337–1345. Rejaie, R., Handley, M. and Estrin, D. (2000) ‘Layered quality adaptation for internet video streaming’, IEEE Journal on Selected Areas of Communications (JSAC), Special issue on Internet QOS, December. Sen, S., Gao, L., Rexford, J. and Towsley, D. (1999a) ‘Optimal patching schemes for efficient multimedia streaming’, Proc. IEEE INFOCOM, April, New York, USA, pp.455–463. Sen, S., Rexford, J. and Towsley, D. (1999b) ‘Proxy prefix caching for multimedia streams’, Proc. IEEE INFOCOM, April, New York, USA, pp.1310–1319. Sisalem, D. and Schulzrinne, H. (1998) ‘The loss-delay based adjustment algorithm: a TCP-friendly adaptation scheme’, Proc. NOSSDAV, Cambridge, UK, July.

Website vTrails, http://www.vtrails.com/