Study and Analysis of Bandwidth Flow Estimation Techniques for Wired/Wireless Networks

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1 Study and Analysis of Bandwidth Flow Estimation Techniques for Wired/Wireless Networks Pallavi Sharma Research Scholar NIMS University, Jaipur Abstract: In this topic, an analysis will be made on bandwidth flow estimation technique which comes under networking domain. Correct bandwidth constrained applications and tools are required for proper bandwidth estimation. A proper monitoring of available bandwidth is required during execution to avoid degradation in performance. A several measurement tools have been proposed in the last few years. After the implementation of e Wireless Sensor Networks are capable to provide good level of QoS but research works are not much for improving performance of bandwidth constraint applications by checking sufficiency of bandwidth available in transmission route. In this topic we will do the analysis of bandwidth flow estimation technique for wired/wireless networks and we will do comparisons of existing estimation tools. Keywords- Bandwidth, Estimation, Technique, Wired/Wireless Networks Introduction: The Internet is going through the rapid changes and one of the consequences of this change is a growing speed of QoS. The high quality of service is the prerequisite of Internet today. Furthermore, an accurate and extensive measurement for bandwidth estimation is required to improve the higher quality of services. In this research, an analysis will be made on the existing techniques for bandwidth estimation. Moreover, we will do some comparisons between existing tools and techniques. Following are the tools and techniques: 1. Tools 1.1 Spruce, ProbeGap & Idlegap : A Large number of multimedia files are transmitted over Internet at a rapid rate. The rate of the transmitted multimedia streams should change expeditiously during bandwidth deviations. Spruce [1] is based on Probe Gap Model. ProbeGap [2], is the mainly used bandwidth estimation technique, which is dependent on probing time and the volume of the packets for probing. ProbeGap generates good estimates at low cross-traffic rates (regardless of the cross-traffic packet size); however, it significantly overestimates available bandwidth when the cross-traffic is high. Moreover, influence by cross-traffic on probe packet sequences causes probe packets in sequences to be split up or even lost. IdleGap, is estimated for a real-time system in a wireless network and in Multimedia Stream server Internet. 1.2 Stream Service Based (STB): Many of the wireless and wired networks are joined using STB (Set Top Box). STB is used as an interface between wireless and wired networks. 130

2 Pallavi Sharma et al,int.j.computer Techology & Applications,Vol 3 (1), Kreatel, a Swedish manufacturer of IPTV STBs. For carrier networks and the digital home, this combination makes for a triple play solution integrating broadband video, voice, and data access into a single device. The medium of delivery, the Internet, has also shown itself to be capable of delivering quality video and entertainment. As a result, the digital home consumer market has rapidly grown, and both Motorola and Cisco were aware of how the STB would play a key role in the digital home consumer market. 1.3 Stream Service Based on Set Top Box and : In this figure, an Internet-based Set Top Box (STB) is the interface between a wired network and a wireless network. Even though wired networks can provide high and stable bandwidths, fragile wireless networks may not support it. Therefore, for[39] Layered streaming services, it is very critical for the STB to know the available wireless network bandwidth. 1.4 Set Top Box: An STB is a device combining the functionality of analog cable converter boxes such as tuning and descrambling and computers such as navigation, interaction, and display. Today s STBs have four major components: [13]. Network interface MPEG decoder Graphics overlay Presentation engine The STB designers are being asked to support an array of new audio, video, and image formats as their products evolve into more open, networked devices. IPTV STBs may be enabled with the functions of personal video recorders (PVRs), digital media adapters (DMAs), voice over IP (VoIP), videophones, and more [14]. Due to the heterogeneous nature of home-based networked devices, each new device with additional functionality layers on different requirements. IPTV and VoD depend on streaming media over a wide area network (WAN) while media applications such as PVR and DMA add a media source in a home LAN environment. For IP video transmitted using the UDP protocol, packet loss can cause significant QoS reduction. A simple video stream can be severely degraded with low levels of packet losses, due to error propagation effects. Video quality is often represented in terms of peak signal-to-noise ratio (PSNR), which is a measure of the root mean square (RMS) error between the original and reconstructed video sequences. Recent successful DSL in Europe and telecommunication enter the market for Although all of the issues highlighted require a solution, it is a critical importance for the ability of the STB to adapt to the limited available bandwidth. Telchemy, a leader in VoIP and IPTV performance managements offers a lightweight software agent called VQmon/SAVM that can be integrated into STBs [15]. VQmon/SA-VM transmits metrics back to service providers during video transmissions. Last year Cisco acquired an STB manufacturer Scientific Atlanta (SA). Recently, another STB manufacturer, Motorola, agreed to purchase Stream service based on the STB and does not depend on cross-traffic. The available bandwidth is estimated via the ratio of free time in the wireless links. In a wireless network in STBs on the Internet deployments of IPTV over Asia have proven that companies can successfully television services. 131

3 order to get the ratio of idle time, the information from network management at the low layer is used. Using this technique a fast and efficient method for estimating the available bandwidth is achieved. A lot of tools have been suggested to get this idle time since the introduction of Cprobe [3]. Following are the main tools under this category. Cprobe uses Internet Control Message Protocol (ICMP) packet trains, which estimates the current congestion along a path. Cprobe produces a short stream of echo packets of a target server and records the time between the receipt of the first packet and the receipt of the last packet. Available bandwidth is measured by dividing the number of bytes sent by this time. In order to tolerate packet drops and possible reordering of packets, Cprobe uses results of four separate 10-packet streams when calculating the available bandwidth. Spruce and IGI are both based on the ProbeGap model [5], which assumes a single bottleneck. Spruce [1] and IGI [4], the Cprobe s successors, use the interval of consecutive probe packets, since the interval or gap between probe packets increases in heavy cross-traffic. Spruce calculates the number of bytes received at the queue between two probes for the interprobe spacing at the receiver. Further, it computes the available bandwidth as the difference between the arrival rate at the receiver bottleneck and the path capacity. The IGI [4] algorithm sends a sequence of packet trains with an increasing initial gap, from the source to the destination host. It monitors the difference between the average source (initial) and destination (output) gap and when it becomes zero, it terminates. Topp [6] and Pathload [7] are incoming packets based tools. The difference of the incoming rate from the sender side to the outgoing rate at the receiver side displays that the incoming rate to be less than or equal to the available bandwidth of the probing link. In ProbeGap [5], the link s idle time is the milestone for bandwidth estimation of a wireless network; however, ProbeGap also must send several probe packets over a specific interval. However, all the methods mentioned above introduce additional traffic into the link. These methods require a probing sequence time to send and process the probing packets. To keep track for lost probes, additional probes are sent filtering out of bad estimates and requiring more processing. It may be possible that most of these methods may not be applicable to certain applications, which requires instant bandwidth estimates. Moreover, if the link is congested, many probes may not reach the destination. Specifically, strict time bounds required of multimedia applications impose upper limits on delay and jitter in addition to the usual performance metrics of throughput and packet loss. 2. Role of Cross-Layer Feedback: Cross-layer feedback allows an interaction between a layer and any other layers in the protocol stack. Cross-layer feedback is performed by a mobile device accessing its own protocol stack layers that contain information from the transmitted packets for efficient mobile device communication and interaction. This packet information retrieval across the protocol stack layers also called cross-layering provides important information about mobile devices in a wireless network [8, 9]. Davis [10] suggested an management method that processes the captured frame to obtain the available bandwidth. The method suggested by Davis describes a WLAN traffic probe that operates at the MAC layer and is capable of producing real-time information on resource usage on a per-station basis. Moreover, a different priority at the MAC layer may be assigned for a QoS-sensitive application based on the applications [11]. Carter and Crovella used bandwidth probing to measure congestion and bandwidth at the application level. All these given methods check the ability to collect, compute, and share useful information for bandwidth estimation across the OSI layers. Eberle et al. [12] has given a cross-layer based model for energy-efficient transmission. To manage the transmission a quality of energy manager (QoEM) is inserted into the network 132

4 protocol stack. The following is one of the feedback metrics. 4. IDLEGAP USING NETWORK ALLOCATION VECTOR 3. Video Service Transmissions Quality (VSTQ) Score, Providing Data on Video Transmission Quality. Video quality score (VQS) method provides a unique solution for the management of a service provider to STB transmissions. However, it does not provide a solution for an STB to end-user link management. This approach works as follow: An STB receives a request from a client. STB retrieves the requested multimedia data stream from the associated server. STB forwards the retrieved multimedia data stream to the multimedia terminal. A wireless channel is shared during this process. The STB can hold portions of the stream in its cache and forward the cached stream data to multimedia terminals through this shared wireless channel. In order to reduce negative effects of network traffic such as late packets, the STB holds and forwards the streaming data between two different networks, the wired and wireless networks. The more shared wireless channel resources assigned to handle the streams, the less jitter the terminal will experience within the network. The wireless channel is a limited shared resource available for servicing heterogeneous multimedia streams. Thus, it is critical to use a simple and effective allocation strategy for the wireless channel so that the quality of the video streams delivered through the STB and the wireless network can be improved. Moreover, currently our research focuses on how to estimate the available resources for heterogeneous streaming services in this environment. In general, the streaming services with high quality may require more resources than the ones with low quality. Bandwidth estimation is basic problem for realtime applications in wireless networks because of the two main factors. First, as in the wired networks, traditional FIFO is not used to schedule the bandwidth among connections in wireless networks. Nodes are arranged in a distributed manner, to avoid collisions in wireless networks. This arrangement causes bandwidth estimation methods in wired networks using intervals [1, 4] or rates [ 6] inapplicable for bandwidth estimation in wireless networks. Second, for time-sensitive multimedia streaming services, the probing time for the available bandwidth should be short. Let C be the capacity of the wireless network.1 Idle rate indicates the rate at which the link is idle. Then the available bandwidth (AB) can be obtained by the following product: AB = C Idle rate. However, previous methods [5, 10] using this formula cause too much overhead to be used in a real-time system for the estimation of the available bandwidth. 4.1 Network Allocation Vector Network allocation vector uses a network allocation vector (NAV) in a situation when two nodes in a wireless network share the same access point (AP) but cannot hear each other. In this case one node will not be able to know about the fact that is there any other node, which is already using the same shared resource(the wireless channel). Thus the network allocation vector (NAV) is used by each node for addressing this hidden node problem, that shows how long other nodes allocate the link in the IEEE DCF MAC protocol. By checking its NAV, the node can know whether another node is already using the shared wireless channel even though a node is located at a place where it cannot reach other active nodes. A node that is reachable from sender updates its NAV whenever the sender sends RTS (request to send) 133

5 Pallavi Sharma et al,int.j.computer Techology & Applications,Vol 3 (1), to the receiver s Access point. However, if it is not reachable from sender, other node does not update NAV. When the receiver sends CTS (clear to send), Other-2 node updates it s NAV. The idle time in the wireless network can then be estimated from the NAV information. 5. Measurement Techniques Several active end-to-end measurement tools and method have been proposed. The main motive of these measurement tools is to find the available bandwidth of a network path, which is inferred by sending a few packets and analyzing the effects on the probe frames of intermediate nodes and cross-traffic. They tools differ from each other on the basis of the size and temporal structure of probe streams, and in the way the available bandwidth is derived from the received packets. Pathload [16], IGI/PTR [17], Abing [18], Spruce [19], pathchirp [20], DietTOPP [21], Yaz [22], and ASSOLO [23] are some examples of probing tools which have emerged in recent years. The difference of these commonly used methods is given as follow. 5.1 Spruce [19] uses tens of packet pairs having an input rate chosen to be roughly around to the capacity of the path, which is assumed to be known. Moreover, packets are spaced with exponential intervals in order to emulate a poissonian sampling process. 5.2 IGI [17] uses a sequence of about 60 unevenly spaced packets to probe the network. The gap between two consecutive packets is increased until the average output and initial gaps match. 5.3 Similarly, PTR relies on unevenly spaced packets but the background traffic is detected through a comparison of the time intervals at the source with those found on the destination side. 5.4 Abing [18] relies on packet pair dispersion technique. The closely spaced probes for example 8 to 16 are sent to one destination as a train. The evaluation of the observed packet pairs delays and the estimation of the available bandwidth are based on a technical analysis of the problems that the frames could meet in the routers or other network devices. 5.5 Pathload [16] and DietTOPP [21] make use of constant bit-rate streams and modifies the sending rate in each turn. The only difference between these two tools is that the DietTOPP increases linearly the sending rate in successive streams while Pathload varies the probing rate using a binary search scheme. 5.6 Yaz [B83] is derived from Pathload. However, it should report results more fast and with increased accuracy with respect to its predecessor. 5.7 PathChirp [20] sends a variable bit-rate stream and it consists of exponentially spaced packets. The actual unused capacity is inferred from the rate responsible for increasing delays at the receiver side. ASSOLO [B84] is a tool which is quite similar to PathChirp, but it uses a filter to improve the accuracy and stability of results and has a different probing traffic profile. To improve the QoS several tools and techniques including AB-Shoot [24], S-chirp [25], FEAT [26], BART [27] or MRBART [28] have also been proposed. However, the source codes of these tools were never released publicly and the methods were implemented only in simulations. A detailed analysis of the existing estimation techniques is outside the scope of this paper a proposed taxonomy has been developed by [19]. The results of the measurements on the Planet Lab and RON testsbeds can be summarized as follows:[38] 1. Almost 70% of Spruce's measurements had a relative Error smaller than 30%. Pathload and IGI experienced larger errors. 2. Pathload consistently over- or underestimated the avail-able bandwidth, whereas IGI did not respond properly to injected cross traffic and overestimated available Bandwidth on some paths. 134

6 Pallavi Sharma et al,int.j.computer Techology & Applications,Vol 3 (1), Pathload generated between 2.5 and 10 MB of probe traffic per measurement. In contrast, the average per-measurement probe traffic generated by IGI is 130 KB and that generated by Spruce is 300 KB. 6. Comparison of different tools proposed over time Works proposed in the past have proposed the use of existing bandwidth estimation techniques in WMNs, without quantifying their performance [39]. Generally, most of tools proponents usually compare the performance of their solution against that of others researchers. For example, in [22] Sommers et al. showed a comparison between the performance of Yaz and Pathload & Spruce under a controlled environment, On the other hand, over the scores of real Internet paths an investigation on the performances of Spruce against IGI was done by Strauss and his colleagues [19]. Ribeiro et al. [20], through emulation technique tested pathchirp against Pathload and TOPP. However, the tools compared above are not enough in number and the investigation done on these tools is also limited. Furthermore, Shriram et al. [29] evaluated different tools depending on their performance. The tools compared by him were Abing, pathchirp, Pathload and Spruce on a high-speed testbed and on real world GigE paths. Similarly, over a real Internet path of the French national monitoring and measurement platform Metropolis, Labit et al. [30], evaluated Abing, Spruce, Netest/Pipechar, pathchirp and IGI. Angrisani et al. [31] compared IGI, pathchirp and Pathload in a testbed equipped with a proper measurement station. Moreover, in [32] the authors presented a comparison of DietTOPP, Pathload and pathchirp in a mobile transport network, which have been generated only from simulations using ns2. Shriram and Kaur [33] to evaluate the performance of Pathload, pathchirp, Spruce, IGI and Cprobe under different network conditions using ns2 network simulation. Finally, Guerrero and Labrador [34] presented a low cost and flexible testbed and in presence of different cross-traffic loads, they evaluated Pathload, IGI, and Spruce in a common environment. The performance of probe-based tools for estimating Av-Bw in wired networks has been widely evaluated [35]. In the above largest comparison of available bandwidth estimation tools the performances of different software programs are examined in terms of accuracy, time and intrusiveness. Almost all these tools have been ported to the recent operating system, and the changes required to make older software work on a newer system have been publicly released [36]. CONCLUSION This research approaches under networking realm. Here we assess the complexities came across through bandwidth controlled applications. Applications whose recital based completely on bandwidth requires to be taken care for accessibility of necessary bandwidth on the transmission path to shun degradation in performance while implementation. A more comprehensive evaluation has been performed. Although great efforts have been made to compare the existing estimation methods, all past works considered only part of the existing measurement tools. The above-mentioned experiments have also been performed considering different scenarios and testbed configurations, thus making the various results not easily comparable. We advocate the need for a unified, flexible and low-cost platform for independent evaluations of measurements tools, and we propose in this paper a testbed solution based on free GPL-licensed software alternative to the one described in. Our study also takes one step further with respect to previous works, since it proposes the largest comparison of available bandwidth estimation tools the performances of 9 software programs are examined in terms of accuracy, time and intrusiveness. All the tools have been ported to a recent operating system, and the changes required to make older software work on a newer system have been publicly released. 135

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