Resource Allocation for Real Time Application in Low

Advances on Wireless Sensor Networks 2013

Conference Proceedings

Resource Allocation for Real Time Application in Low Bandwidth Network Environment *

T. Balla *, Z. Gal * and A. Sz. Karsai * University of Debrecen, Debrecen, Hungary

Nowadays QoE is very important part of the multimedia streaming subsystems, high definition video

Abstract Nowadays all people get the information they need from many sources. Most of these materials come from electronic services. Users consume the information not only by reading, but also watch real time services on the Internet using their hardware and software tools. With the advanced multimedia services today we can organize meeting in a virtual room, letting and anyone to join from anywhere in the world. Watching such streaming contents is not reduced to the personal computers but smart phones, tablets are able to visualize these contents with remarkable processing and transmission bandwidth requirements, as well. Some years ago we could not imagine that, we can organize meeting in a virtual room, and anyone can join from anywhere in the world. Or as an example, we can watch our favourite team football game from our mobile device. In a Cross-Border Cooperation Programme between University of Debrecen and University of Timisoara we set the objective of bring together the two universities people. With real time applications such as IPTV system, internet telephony, videoconferencing can cooperate over a virtual tunnel in each institute in very resolution, enhancing the quality of user experience. People can use each other services, electronic resources anywhere on their own campuses on any devices, platforms, strengthening the cross-border relationship between them. We discuss the optimal solution for this integrated multimedia service.

I.

standard, these types of media utilizes much more bandwidth than standard definition video some years before. It is really hard to send these multimedia content to the end users without loss of quality, even if we have enough network resources [3] [4]. Some manufacturers have some recommendations about QoS and multicast configurations, but best solutions in certain heterogeneous environment. enough network resources (bandwidth) for instance over a site to site VPN tunnel, we can experience some disappointing results with HD streaming media. In our paper we try to find the solution to maximize the media quality and user experience in a low bandwidth environment. In the second section we give some overview about the video streaming technology. We describe the mechanisms of creating a video stream, and the delivery procedure in various environments. Combining these technologies of content delivery with QoS can increase the quality of streaming media anywhere in the LAN. In the third section issues of the streaming communication are presented. The measurement scenario is given in section four. The fifth section presents the analysis methods and results of the experienced transmitted content quality depending on the traffic flow characteristics. The last section concludes this work and lists possible continuation subjects related to the quality based multimedia transmission.

INTRODUCTION

Multimedia streaming is not a new technology. It has been introduced in the late 1990s, it delivers the multimedia content using the Internet protocol suite over a packet-switched network, instead of being delivered through traditional media. In the meantime during the development of technology and appearance of the social networks and video sharing websites, multimedia became part of everyday life. During the progress, quality of this multimedia content came to the front. The public internet is everywhere; people reach it with different devices and various type of access method which is not only wired but wireless, too. Video streams can also be distributed between organizations through VPN tunnels. It is really hard to determine how many bandwidth required for a given media stream, without loss of quality thereby maximize the quality of user experience (QoE). Different devices and various type of network connections leads to new challenges because not all type of network connections are reliable [1]. There are two different methods of content delivery over the network, unicast and multicast. Both technologies use the quality of service (QoS) control mechanisms to provide the communications reliability and quality on point-to-multipoint and multipoint-tomultipoint video delivery [2].

II. STREAMING TECHNOLOGY OVERVIEW In these days network traffic consists of wide variety of types. In addition to the normal data traffic real time traffic is significant, too. Video clips, animation, internet radio, internet telephony, videoconferencing and collaboration became very popular services on the Internet. Multimedia applications typically have to be delivered and rendered in real time. If we want to describe simply a multimedia stream, we can do it with the following characterisation: raw video and audio data are pre-processed by compression algorithms in real-time. Upon a request of client, a streaming server sends this compressed content through some application-layer QoS control module. After the adaptation, the transport protocols packetize the compressed bit-streams and send the video/audio packets to the network. This type of data has unique characteristics. Requires higher bandwidth than traditional applications, audio and video data must be played back continuously at the rate they are sampled, even the multimedia data stream is arrives in burst mode usually. If data comes too fast the buffer on the client side

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Advances on Wireless Sensor Networks 2013 can be overflowed and packets will be lost resulting poor quality. Otherwise, if data stream arrives slowly the buffers will underflow so the playing process can freeze for a while. Multicast technology became popular with the spread of IPTV systems. Unlike unicast traffic multicast uses one stream per a group of users, so it is a bandwidth efficient way of utilization of network and streaming server resources. Unfortunately it is not useable for video on demand media, but for live event broadcasting or internet television services definitely became the best solution. The multicast spreading technology has two common

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coordinate forwarding packets from source to receivers and to prevent the initial flooding of datagrams [5]. We can prioritize certain type of network traffic with quality of service technology. QoS has ability to provide different priority to different type of services and it can guarantee a certain level of performance if it is required. There are three widespread QoS mechanisms to control network traffic. Priority or Low Latency Queuing is the primary method used when dealing with sensitive traffic flows that do not react well to network latency, such as voice and video, etc. Traffic in the Priority queue will be processed and transmitted ahead of all other traffic. Policing uses a token bucket to limit the flow of traffic to the specified rate. If there are not enough tokens in the bucket, any further packets arriving are discarded. Shaping uses a token bucket and data buffer to queue traffic so it can be transmitted at a rate we specified, within the timing interval. Unlike Policing if the token bucket is full then the packets must wait in the queue until there is sufficient space to continue transmission. III.

ISSUES OF THE STREAMING COMMUNICATION

As we can see streaming media service is not an easy domain of computer networks. There exists different recommendations from manufacturers and best practise documentations, but none of them give us a right answer, that how many bandwidth is required exactly for a specific multimedia stream or group of different streams transmitted on a common and shared physical path. Needs deep knowledge of technologies mentioned in section two to determine the right QoS, multicast/unicast combination for streaming media content.

Figure 1. Multicast and unicast usage scenarios [2]

methods which are current in computer networks: dense mode (push model) and sparse mode (pull model), respectively. The dense-mode algorithm uses a source-based tree. It requires the determination of a shortest-path tree to all destinations and uses a reverse shortest-path tree rooted at a source by flooding multicast traffic domain wide, and

TABLE I. QUALITY CHARACTERISTICS IN BEST EFFORT IP TRUNK LINK Traffic Homogeneous UDP flows Homogeneous TCP flows Homogeneous voice flows (UDP) Homogeneous video VBR flows (UDP) Homogeneous video streaming flows (TCP) Heterogeneous voice (UDP) and VBR video flows (UDP)

Figure 2. Multicast dense mode versus sparse mode

then pruning back branches of the tree where no receivers are present. Packets are forwarded on the shortest-path tree according to the source address they originated from, and the group address they are addressed to. The sourcebased tree in a network provides the shortest distance and the least delay from the source to all receivers [5]. The sparse-mode algorithm uses a shared-tree. Builds unidirectional shared trees rooted at a rendezvous point (RP) per group, and optionally creates shortest-path trees per source. A rendezvous point known as the core, or root is considered a point in the network where roots of distribution subtrees are shared and the multicast data flows down to reach the receivers in the network. The sparse-mode algorithm uses a rendezvous router to

No congestion

Congestion

- High bursts - High bursts - No data loss - Data loss - Low bursts - No segment (L4) loss, but packet (L3) retransmission needed - High content - Low content quality quality - Good interactivity - No interactivity - High content quality - No interactivity

- Low content quality - No interactivity

- High content quality - Good interactivity

- Low content quality - No interactivity

None of the documentations or papers tells us about the quantitative relation between network resources and quality of experience for low bandwidth network environment. This question is hard to answer and became hot subject for the current wireless LAN and 3G/4G WAN network technologies. Another important topic is the platform independency of different multimedia services. This later topic is not part of the current paper. Congestion control (CC) algorithms of the TCP have evolved over time, usually to provide improved

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Advances on Wireless Sensor Networks 2013 performance in terms of usable throughput (or goodput) for high speed network connections. What all currently deployed TCP CC algorithms have in common is that they cause a cyclical filling and draining of the queues at the bottleneck link of the path. This in turn induces fluctuating latency and periodic packet loss to all traffic sharing the link [6]. There has been extensive research on network resource measurement techniques. One category is the active probing where special packets are injected into network to measure network resources, such as bottleneck link bandwidth, available bandwidth. Another way to measure end-to-end queuing delay is by using a pair of packets with different priorities. It was shown that path segment measurement is more suitable to multicast environment, more robust than full path measurement, and reduces error probability of estimating network resources [7]. Our idea was to make a comparison between unicast and multicast multimedia traffic in a low bandwidth environment. The experienced quality is quantified by the H, long range memory parameters of the aggregated streams on the trunk.

Conference Proceedings Our examination consisted of a lot of cases. We captured 56 traces during the research. Each trace was sampled during sec. The same multimedia content was transmitted by the servers each time. The unicast traffic was sent with TCP and the multicast traffic with UDP. The measurements were taken with various backbone capacity scaled from BwL = 256 kbps to BwH = 8 Mbps with different number of clients (U = 1,2,4,8; M = 0,1,3,5,7), whre U and M represents the number of unicast and multicast clients, respectively. Through the measurement scenario once hosts were using homogenously unicast or multicast, once they were using heterogeneously the two transmission technologies. Figure 4 represents all the measurement cases in a 3 dimensional coordinate system, where z-axis shows the bandwidth, x and y axis shows the number of certain client types.

IV. MEASURMENT SCENARIO In our measurement setup we have examined various types traffic flows in many cases. Our idea was to test unicast and multicast traffic separately and together with variable number of clients in a best effort environment. For unicast and multicast streaming service we used commercial software on powerful server hardware with Linux operating system. The servers transmitted the same video stream in the same quality for the multicast group and the unicast clients in each measurement case. On the server sides we used 1 Gbps uplink to the network, so network congestion on these sides can be negligible. On the network device level we defined a policy able to limit the amount of bandwidth on the backbone. After the limiter policy we used a best effort environment again for the clients. For the packet capture it was obvious to use the port mirroring technology. Figure 3 shows the lab environment we used for our measurements.

Figure 4. Measurement cases (56 traces)

We were interested in finding the relation between the QoE (Quality of Experience) and any numerical characteristic of the stream sensed on the hardware layers of the network. V. ANALYSIS OF THE CAPTURED DATA SERIES The data stream captured by the capture server S was processed offline. Two values were taken in consideration: interarrival time and frame size. Using transformation ON/(ON+OFF) there were calculated the channel intensity M, and channel load TGP. TGP stands for tan( ), where is the channel phase angle in radian [8]. The transformation ON/(ON+OFF) calculates periodically the channel intensity M and the channel load TGP. The channel intensity is the number of frames arrived and channel load is the rate of arrived bits and theoretical maximum number of bits transmitted on the channel with a given bandwidth. Both quantities are

Figure 5. Processing the Hurst parameters HM, HTGP

recalculated once in each time period. In our case this time period was T = 100 ms. A detailed presentation of the transformation ON/(ON+OFF) is given in the paper

Figure 3. Measurement environment

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Advances on Wireless Sensor Networks 2013 [9]. Both values given by this transformation are without unit. Several publications analyse the self-similar (SS) and long-range dependence (LRD) of the network traffic. We consider important not only the interarrival time but the amount of information transmitted on the communication channel, too [8] [9]. Using the wavelet transform for both M and TGP time series we calculated the Hurst parameter pair for each measurement case (see Figure 5).

Conference Proceedings independent on the number clients and HM = 0.63. If the bandwidth is increased, the congestion level decreasing influences the HM parameter. The slope of the lines becomes dependent on the number of destination clients. When the bandwidth is big enough to not to congest the streams, the parameter HM does not represent correct the simultaneous uncongested streams number. Analogous considerations exist for the Hurst parameter HTGP (see Figure 7). For congested cases the HTGP is approximately constant: HTGP 0.52. Linear relation exists between the logarithm of the bandwidth and HTGP. The slope of the lines is dependent on the number of parallel streams. For measurement cases without congestion the parameter HTGP does not represent well the number of uncongested parallel streams.

Figure 6. Hurst parameter of the channel intensity HM (no multicast)

When there were no multicast streams present on the trunk channel the calculated Hurst parameter HM of the aggregated stream traffic varies linearly on the logarithm of the bandwidth (see Figure 6).

Figure 7. Hurst parameter of the channel load HTGP (no multicast)

For very small bandwidth (256 kbps) when strong congestion is detected the global parameter HM is

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Figure 8. Hurst parameter of the channel intensity HM (with multicast)

For only one multicast stream the parameter HM varies linearly in function of the logarithm of the bandwidth, but decreases less than for the only one unicast client case. For 1,3,5,7 multicast clients together with 7,5,3,1 unicast clients the dependency curves of the HM starts at very different values: (0.52 slightly congested cases the linear dependency exists, but the slope of these lines is approximately the same. It means that for the heterogeneous multimedia traffic affected by congestion the slope of the HM curve differs only by the number of parallel streams. Is important to note that in our measurement cases with heterogeneous traffics the number of streams was constant (U + M = 8). The possible reason of this phenomenon is that the unicast streams sent are strongly controlled with the internal congestion control mechanism of the TCP. UDP PDU transmissions are not controlled and create congestion when the bandwidth is less than the minimum necessary amount for the multicast stream transmission. The Hurst parameter dependence of the channel load HTGM for only one multicast stream has two linear parts (see Figure 9). For bandwidth less than 2 Mbps the HTGM does not depend on the bandwidth (HTGM = 0.52, mTGP 0). For bandwidth more than 1 Mbps the slope becomes mTGP = 0.03 [log(Bw/1Mbps)-1]. A possible explanation

Advances on Wireless Sensor Networks 2013 of the inflexion point existence can be the critical bandwidth value of the multicast stream sent with UDP. Under the critical bandwidth the UDP stream suffers congestion and huge degradation of the QoE parameter is experienced by the users.

Conference Proceedings by the channel bandwidth, the number of parallel sessions and the structure of congested TCP and UDP streams. These dependence rules are included in the Table II. More analysis is needed to estimate the parameters of the linear functions f1, f 2, g1, g2. Analytical model of the measured phenomenon can prove the statements above. Online calculation of the Hurst parameter pair by the network switches can give simultaneous quantitative metric to the service provider about the QoE experienced by the multimedia network content consumers. ACKNOWLEDGMENT This work was partially supported by the HURO/1101/074/1.2.1 project. The project has been supported by the Hungary-Romania Cross-border Cooperation Programme 2007-2013. The programme is financed from the European Regional Development Fund. This work was partially supported by 4.2.2.C-11/1/KONV-2012-0001 project. The project has been supported by the European Union, co-financed by the European Social Fund. REFERENCES [1]

[2] Figure 9. Hurst parameter of the channel load HTGP (with multicast)

For unicast and multicast heterogeneous traffic the linear dependency of the HTGP is similar with the case when the bandwidth is higher than 1 Mbps for only one multicast stream. The slope mTGP is the same and is equal to 0.03. The intercept of the lines increases with the number of parallel sessions. The parameters HM and HTGP are not able to detect the number parallel sessions when the bandwidth is enough for each stream. VI.

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[4] approach to adaptive bandwidth allocation with QoS enhanced on ICUIMC '09 Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, pp. 260-264, 2009. [5] [6]

CONCLUSIONS

As a conclusion we can state that both time series, channel load and channel intensity need to be considered for proper evaluation of the congestion in homogeneous TCP or heterogeneous aggregated TCP/UDP multimedia traffic. The aggregated traffic of the congested streams has LRD characteristic. TABLE I. QUALITY CHARACTERISTICS IN BEST EFFORT IP TRUNK LINK Case Congested homogeneous unicast traffic (TCP) Partially congested homogeneous unicast traffic (TCP) Partially congested heterogeneous traffic (UDP, TCP) Uncongested homogeneous traffic (UDP, TCP) Uncongested heterogeneous traffic (UDP, TCP)

(HM,H TGP) (0.63, ~0.52) HM = f 1(log(Bw),U) HTGP = f 2(log(Bw),U) f1, f2 being linear functions HM = g1(log(Bw),M,U) HTGP=g2(log(Bw),M,U) g1, g2 being linear functions No analytical estimation, yet No analytical estimation, yet

Both HM and HTGP Hurst parameters characterize the congestion. The (HM, H TGP) parameter pair is influenced

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-Streaming in Delay CHANTS '11 Proceedings of the 6th ACM workshop on Challenged networks, pp. 67-68, 2011. A. Ganjam and H. Zhang, Proceedings of the IEEE (Volume:93 , Issue: 1 ), pp. 159 170, 2005. P. H. Trisnawan and R. Budiarto Priority-based QoS mechanism for multiple multicast IPTV streams AINTEC '09 Asian Internet Engineering Conference, pp. 19-24, 2009.

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[8] Network Traffics Based on Wavelets and Transformation Applied Computation Intelligence in Engineering and Information Technology, Springer-Verlag Berlin Heidelberg, DOI 10.1007/978-3-642-28305-5, 2012, pp. 107-122. [9] Z. VoIP LAN/MAN traffic analysis for NGN QoS management , Infocommunications Journal, Volume LXIV, ISSN:0866-5583, 2009, pp. 22-29. [10] for wireless network layer roaming problem Carpathian Journal of Electronic and Computer Engineering, vol. 5, no. 1, 2012, pp. 5-8. [11] An Overview of the Multipath Communication Technologies Advances in Wireless Sensor Networks 2013, Conference Proceedings, Debrecen University Press (www.dupress.hu), Debrecen, Hungary, ISBN: 978-963318-356-4, 2013, pp. 7-11. [12] S. Fuicu An Experimental Approach into TCP Congestion Mechanism over a WLAN Network Advances in Wireless Sensor Networks 2013, Conference Proceedings, Debrecen University Press (www.dupress.hu), Debrecen, Hungary, ISBN: 978-963-318-356-4, 2013, pp. 53-57.