How to Measure the Speed of a Network Service Quality

Transcription

1 QoS Specification and Monitoring for Multimedia Delivery in TeleLearning Irene Cheng, Anup Basu and Lei Chen Department of Computing Science University of Alberta 615 General Services Building Edmonton, AB T6G 2H ABSTRACT Historically, the Internet has been viewed as a provider of a single level of service, that of "best effort", where all data packets are given the same priority in the network. However, service providers are increasingly realizing that the Internet in reality offers varying levels of service; some areas of the network may have high levels of congestion resulting in poor quality while other areas may exhibit consistently high quality. The goal of this project is to monitor and predict bandwidth availability for clients sites using a telelearning facilty and use this estimate to provide Quality of Service (QoS) based delivery of multimedia course material over the internet. In order to provide QoS based delivery a prioritization method has been designed to order information according to the preferences of a user. Java applets and CGI programming tools are used to provide an interactive web retrieval environment. At present, JPEG quality, resolution and delay are used as parameters to determine the QoS. We are extending the work to include audio and video delivery with QoS in the future. KEYWORDS Multimedia on the Web, Web Applications, Dynamic Web-content, TeleLearning, QoS 1.0 Introduction The internet has traditionally offered a single level of service, that of " best effort," where all data packets are treated with equity in the network. However, we are finding that the internet itself does not offer a single level of service quality, and some areas of the network exhibit high levels of congestion and consequently poor quality, while other areas display consistent levels of high quality service. As well, the bandwidth currently available changes dynamically with time. Customers are thus voicing a requirement to define a consistent service quality they wish to be provided, and network service providers are seeking ways in which to implement such a requirement [3]. This effort is happening within the umbrella called "Quality of Service" (QoS). Of course, this is now a phrase which has become overly used, often in vague, non-definitive references. QoS discussions currently embrace abstract concepts, varying ideologies, and moreover, lacks a unified definition on what QoS actually is and how it might be implemented. Subsequently, expectations regarding QoS have not been appropriately managed within the internet community at-large on how QoS technologies might be realistically deployed on a global scale. A more important question is whether ubiquitous end-to-end QoS is even realistic in the internet, given the fact that the decentralized nature of the internet does not lend itself to homogenous mechanisms to differentiate traffic. Meeting quality of service (QoS) guarantees in distributed multimedia system is fundamentally an end-to-end issue. Consider, for example, the remote playout of a sequence of audio and video: in the distributed system platform, quality of service assurances should apply to the complete flow of media, from the remote server across the network to the point(s) of delivery. This generally requires end-to-end admission testing and resource reservation in the first instance, followed by careful co-ordination of disk and thread scheduling in the end-system, packet/cell scheduling and flow control in the network, and finally active monitoring and maintenance of the delivered quality of service [9]. A key observation is that for applications relying on the transfer of multimedia, and in particular continuous media flows, it is essential that quality of service is configurable, predictable and maintainable system wide, including the end-system devices, communications subsystem and networks. Furthermore, it is also important that all end-to-end elements of distributed systems architecture work in unison to achieve the desired application level behaviour. In this paper we describe a project whose goal is to monitor and predict bandwidth availability for clients sites using a telelearning facilty and use this estimate to provide Quality of Service (QoS) based delivery of multimedia course material over the Internet. In order to provide QoS based delivery a prioritization method has been designed to order information according to the preferences of a user. The current implementation considers JPEG quality, resolution and delay as the only parameters in the QoS specification and subsequent QoS based retrieval process. Recently work has been reported using multi-layer storage of multimedia [14]. However, this paper does not consider bandwidth monitoring and adjusting quality depending on the currently available bandwidth. Also, no means is provided for users to interactively specify their quality of service requirements and priorities when the specified quality of service cannot be

2 met on the internet. In the past we [15,16] introduced the use of foveated representation and retrieval of image and video for real-time as well as internet based applications. This concept is different from hierarchical approaches in that it allows variations in detail within the same level of representation of an image or video, and not between levels. Foveation can therefore allow efficient compression of images and videos where there are distinct regions of interest in a scene. The main difference of our work from these related publications is providing QoS specification options to users and adjusting system response according to the parameters input by users. Also, we actually measure, monitor, and predict bandwidth between a server and a client, and this bandwidth measurement is continually used in the QoS based delivery; other approaches (such as VBR) often assume delivery based on variable link speeds, however, the link speeds are considered to be fixed during an application and not varying dynamically depending on congestion. In this paper we give an algorithm for optimally retrieving images satisfying QoS specifications; as well, a dynamic web content updating method is implemented. The remainder of this paper is organized as follows: Section 2 describes the method used for monitoring bandwidth between a client site and a server site, the bandwidth monitoring device is assumed to reside on the server in this application. Section 3 describes a means for specifying the QoS parameters and their priorities in the context of the current implementation; this section also discusses an algorithm for the optimal retrieval based on the QoS specification and a method for maintaining dynamic web content. Section 4 shows the outline of the implementation of the bandwidth monitoring software, before the work is concluded in Section Bandwidth monitoring In our implementation, we designed a program to monitor the bandwidth usage on the Internet. The are two different approaches in our implementation: One is to monitor the transmission speed for a point-to-point connection, then to determine the level of QoS; another one is to test the output speed of a web site to determine the level of QoS. We deploy SNMP (Simple Network Management Protocol) to implement our program. SNMP manager was designed to get MIB (Message Information Block) information from SNMP agent. With SNMP facilities integrated into the WWW server it is possible to manage the server from a remote station. This enables the use of advanced management platforms that conform with SNMP to monitor and control the operational status of the various WWW servers under a management domain, for example, the various WWW servers of an organisation. As the use of the Internet grows and diversifies, means to achieve better efficiency and faster downloads are being sought. For the consumer, fast web page loading time means more effective surfing and ultimately less time spent on the net waiting and looking at half full pages. For many applications, the quality of the network connection immensely affects performance and presentation; one such application is TeleLearning. Network connections are chiefly characterized by the bandwidth available to users using the connection, and in general the higher the bandwidth the faster the document transfer and loading time. Quality of Service (QoS) on the Internet is a blooming, new and important concept of the information highway. The term "Quality of Service" is yet to be defined concisely; currently it embraces abstract concepts and varying ideologies. In our point of view, QoS deals with providing certain levels of Quality to Internet users no matter how busy the connection is. To properly understand the goal of the project, we need to define what we mean by: 1. Delay 2. Bandwidth 3. Quality with respect to speed of transfer 4. Quality with respect to resolution By delay, we mean the elapsed time for data packets to reach the final destination over the Internet. Delay is caused by increased amount of data held "in transit" through the Internet. Higher delay times make the Internet seem insensitive and unresponsive yet in reality it is just trying to cope with great stress imposed on the transport protocol by a large volume of data traffic. By bandwidth we mean the maximal data transfer rate that can be sustained between two end points. Data transfer rate is measured in bits per seconds and is limited by the amount of traffic that exists between the end-to-end path. In terms of quality, the speed of transfer deals with the amount of time it takes to receive the requested data over the Internet. The higher the traffic on the Internet, the higher the delay and the lower the bandwidth, consequently the slower the transfer of data is. On the other hand, the quality of resolution deals with the clarity of pictures and movies (video) and sound (audio) over the Internet. Intuitively, better resolution entails higher data overhead, which requires a large bandwidth and low delay for efficient transfer. To achieve QoS in the realm of our project, we propose to have a hierarchy of multimedia resolutions that are transmitted to the receiver according to the available bandwidth of the path between a sender and a receiver. Stemming from the nature of the project, the problem right now is dynamically determining the path bandwidth of the

3 end-to-end connection between a sender and a receiver. The difficulty arises from the fact that the Internet is a collection of routers and transmission links that collectively work to provide a "best-effort" network service. Routers receive incoming data packets and send them through appropriate transmission hops to their intended destination. Knowing that different routes exists between two end points, and knowing that data packets are sent to their destination via these routes and assembled at the destination, it is very hard to lock on to a specific route, to determine its bandwidth and to send all the packets through it. Also, during transit, the bandwidth will dynamically change depending on the congestion of the connection hence the resolution of the image to be transmitted will have to change and adapt to the current bandwidth. Our implementation is based on several assumptions. Firstly, we assume that the packets we send through the connection are not frequently reordered by the routing algorithms. Secondly, we also assume that the paths established by the routers for the packets are stable, meaning that the paths that the packets will take will not change in the next few seconds. This is a reasonable assumption when dealing with the connection on www servers because routing table updates are infrequent on the Internet. Robert L. Carter and Mark E Crovella describe the major idea behind the implementation in Measuring Bottleneck Link Speed in Packet-Switched Networks. In essence, the bottleneck link speed is measured by sending a pair of data packets from the source, through the network to the target and back through the network to the source. When the packets arrive at the source, we measure the inter-packet gap. Through the bottleneck the inter packet gap is increased and this increase reflects the capacity of the bottleneck link. That is, the size of the gap varies inversely with the capacity of the link. It must be noted that the inter packet gap is stretched only on the trip going through the bottleneck link from the source to the target, and not from the target to the source. When the packets return to the source, the inter arrival time (gap between the arrival of the two packets) is measured and reflects the speed of the bottleneck link. The idea is better explained using the following diagram. Figure 1: Illustration of packet flow through a bottleneck link. From this diagram we see that if two packets are queued together at the entrance to the bottleneck, with no packets in between them, the time between the arrival of the two packets (inter packet spacing time) reflects the processing time for the router s algorithm to process the second packet of the pair. The trailing edge of the first of a pair of packets marks the time when the router started processing the second packet of the pair and the trailing edge of the second packet records the time when the router finished processing that packet. Therefore given a packet of size P and the inter arrival time in seconds, the bottleneck link speed or bandwidth can be estimated as follows: Many problems are inherent in measuring the bandwidth in practice. These problems include: Queuing failure 1. Competing traffic 2. Probe packet drops 3. Downstream congestion 4. These problems are solved by using multiple ICMP (Internet Control Message Protocol) ECHO packets of varying sizes and by a careful filtering process, which discards inaccurate measurements.

4 In conclusion, we have reliably overcome the problems inherent in the Internet architecture and confidently measured the bandwidth of the Internet path between end-to-end connections. This information, coupled with the hierarchy of images of varying sizes will serve to provide a better QoS to the consumer and bring us a step closer to our goal, which is to provide QoS telelearning methods over the Internet. 3.0 QoS specification Images form one of the important elements in multimedia applications. When an image is presented to the viewer, it is expected that the objects in the image represent accurately and precisely the details of the objects as perceived by human eyes in real life. This means that very high resolution images, in other words, images of relative big size, are required. Big size images pose two problems: storage and speed. With the increased capacity on modern computers in terms of memory and disk space, image storage becomes insignificant when processing images. However, the image loading time continues to be a challenge to researchers, especially when images are loaded on the Internet. In addition to E-Commerce, there are other web-based applications, such as TeleLearning, which require image retrieval on the Internet. One common characteristic among these applications is that not every single image is required to be displayed in high resolution. For example, there can be one hundred cars advertised by a second-hand car dealer, but a potential customer may be interested in those from $5000 to $8000. A customer may not be interested in cars with unpopular colors. Eventually, he/she may choose to display 10 high resolution images. This paper applies QoS (Quality of Service) delivery to help eliminate unwanted images and display the selected ones according to the viewer s preference. The QoS specification options provided here include resolution, JPEG compression quality, bandwidth and other parameters which will affect the quality of service when images are delivered to the viewer. This paper makes use of a n-layered methodology to organize and store the images in the system, and dynamic web-content to facilitate the update and retrieval of these images. The viewer is able to specify the desired QoS and a priority list. In case the specified QoS cannot be met, the priority list will be used to relax one or more parameters in order to retrieve the next best image requested by the viewer. The following two diagrams explain how the priority list is used: Figure 2: Screenshot of image request based on QoS. When a viewer is interested in a particular thumbnail and wants to have a closer look at the image, he/she can choose a higher resolution and a higher JPEG quality. If "no restriction" is specified for maximum delay, the image of the requested resolution

5 and quality will be loaded. If the viewer wants to restrict the loading time to within 10 seconds, as above, the system will obtain the current bandwidth and estimate the required loading time for such image. In case the estimated loading time exceeds the maximum delay specified, the QoS will be reduced based on the flexible parameters, i.e. Resolution-Quality. Suppose the requested resolution is "high" and JPEG quality is "high", the sequence of checking is as follows, ResolutionJPEG Quality high high medium high medium medium low medium low low thumbnail low until the estimated loading time is within 10 seconds. The last image is the default and will be retrieved if no other image satisfies the request. Figure 3: Screenshot of image delivery based on QoS. The priority list can contain one or more of the QoS parameters in any order. The viewer can also specify "flexible on nothing." QoS Delivery enables the viewer to obtain the detail of an image if he/she wants, without being bothered by the loading of unwanted images. 3.1 n-layered methodology In order to make the retrieval more efficient, the images are categorized by layers. Each layer corresponds to a QoS parameter. In other words, if there are n parameters there are n layers. The current implementation provides 4 values in the resolution layer (high, medium, low, thumbnail), 3 values in the JPEG quality layer (high, medium, low), and 4 values in the maximum delay layer (10 sec., 20 sec., 30 sec., no restriction). Each image is associated with a value in each layer. These values are the keys (indices) used to retrieve the images. Each image in the system has 4x3 = 12 representations corresponding to different resolutions and JPEG qualities. The lowest layer of this hierarchy identifies the size of each representation. Together with the current bandwidth, the image size is used to

6 estimate the loading time. The structure below shows the data structure used in the implementation of the system described here. 3.2 Dynamic web-content Instead of hard-coding the category and image names in a HTML document, this paper applies dynamic web-content using CGI programs to update the web-content instantly. CGI-based web applications normally make use of meta-data for image retrieval. Meta-data is stored in files or in a DBMS (Data Base Management System). At the same time when an image repository is built, meta-data such as image filename, file location, image resolution, etc. are assigned to each image and stored separately from the images. This approach involves meta-data population and searching time during retrieval. On the contrary, our approach is to directly retrieve the categories and the list of images within each category from the image repository. This is achieved by incorporating the meta-data in the image filenames. By referencing the name of an image, all QoS values associated with the image are also known. As soon as the repository is modified by adding or deleting images, the updated list is immediately available for the next execution of the CGI programs. 3.3 Best-fit QoS algorithm The displayed image is the next best image which matched the requested QoS if the initial QoS cannot be satisfied. The thumbnail will be displayed if no other image satisfies the request. A protocol of the BestFitQoS Algorithm for a 3-layered implementation is given below: R = { u1, u2, u3, u4 where u1 to u4 are predefined resolution values Q = { v1, v2, v3 where v1 to v3 are predefined quality values D = { w1, w2, w3, w4 where w1 to w4 are predefined maximum delay values parmlist = { r, q, d: r R, q Q, d D

7 N = number of elements in parmlist flexlist = {p1,, pn where n 1 and n N p1 to pn are distinct values t T i.e. T= {"resolution", "quality", "delay" in any order or T = {"nothing" GetRequestQoS(imageName, parmlist, flexlist) { ); I = getimage( imagename, parmlist.getresolution, parmlist.getquality time = I.getSize()/ getbandwidth(); if (time > parmlist.getdelay) { ReduceQos( imagename, parmlist, flexlist, time ); DisplayImage(imageName, parmlist); ReduceQos ( imagename, parmlist, flexlist, time ) { parameter reduced further while (time > parmlist.getdelay) { cur = flexlist.removefirst(); // obtain the next flexible flexlist.setlast( cur ); // flexlist is a circular list if ( nomorereduction(parmlist, cur) ) { // when QoS cannot be break; if ( cur equal "resolution") { ReduceResolution(parmList); elseif (cur equal "quality") { ReduceQuality(parmList); else { ReduceDelay(parmList); parmlist.getquality ); I = getimage( imagename, parmlist.getresolution, time = I.getSize()/ getbandwidth(); // end while parmlist now contains the BestFit Qos parameters 4.0 Bandwidth monitoring implementation Our prototype is implemented on a Unix platform, involving the use of HTML, JavaScript, Perl and C Language. The CGI program getbandwidth() returns the average of the bandwidths taken from a period of reasonable length. The time estimated to load the desired image is calculated using this average bandwidth. To illustrate the concept, we make use of the gifdraw module written by Tom Boutell, Cold Spring Harbor Labs. To implement a bandwidth monitor with every average value taken in a 30 seconds interval:

8 Figure 4: Screenshot of bandwidth monitoring tool. 5.0 Conclusion In this paper we outlined a method for specifying quality of service (QoS) and using this specification for efficiently retrieving multimedia content over the Internet. An implementation following the proposed approach, for a TeleLearning application, was also described. In future work we will add audio and video to the implementation. The current bandwidth monitoring system is designed to handle communication between a single client and a server, this software has to be generalized to handle multiple clients accessing the same server at the same time interval. 6.0 References [1] Paul Ferguson, Geoff Huston, "Quality of Service - Delivering QoS on the Internet and in Corporate Networks". Weley Computer Publishing. [2] Kurose, J.F., "Open Issues and Challenges in Providing Quality of Service Guarantees in High Speed Networks", ACM Computer Communications Review, Vol 23, No 1, January [3] Vogel, A.,Bochmann, G. V., Disallow, R., Geckos, J. and B. Kerherv, "Distributed Multimedia Applications and Quality of Service: A Survey", IEEE Multimedia, [4] Robert L. Carter and Mark E. Crovella. Measuring Bottleneck Link Speed in Packet Switched Networks. TR , Boston University, CS Department, March [5] Miloucheva, I., "Quality of Service Research for Distributed Multimedia Applications", ACM Pacific Workshop on Distributed Multimedia Systems, [6] Bansal, V., Siracusa, R.J, Hearn, J. P., Ramamurthy and D. Raychaudhuri, "Adaptive QoS based API for Networking", Fifth International Workshop on Network and Operating System Support for Digital Audio and Video, Durham, New Hampshire, April, [7] Pacifici, G., and R. Stadler, "An Architecture for Performance Management of Multimedia Networks", Proc. IFIP/IEEE International Symposium on Integrated Network Management, Santa Barbara, May [8] Hutchison, D., Coulson G., Campbell, A., and G. Blair, "Quality of Service Management in Distributed Systems", M. Slomaned. (Ed.), Network and Distributed Systems Management, Addison Wesley, Chapter 11, [9] Hazard, L., Horn, F., and J. B. Stefani, "Towards the Integration of Real-Time and QoS Handling in ANSA", CNET Report CNET.RC.ARCADE.01, June 1993.

9 [10] Coulson, G., Campbell, A and P. Robin, "Design of a QoS Controlled ATM Based Communication System in Chorus", IEEE Journal of Selected Areas in Communications (JSAC), Special Issue on ATM LANs: Implementation and Experiences with Emerging Technology, May [11] Gopalakrishna, G., and G. Parulkar, "Efficient Quality of Service in Multimedia Computer Operating Systems", Department of computer science, Washington University, Technical report, August [12] TINA C., "The QoS Framework", Internal Technical Report, [13] Sluman, C., "Quality of Service in Distributed Systems", British Standards Institution, UK, October [14] Chen, L. and T. Liu, "A review of QoS", Department of Computing Science, University of Alberta, May, [15] Mohan, R. et al., "Adapting multimedia internet content for universal access", IEEE Trans. On Multimedia, March [16] Basu, A. and Wiebe, K.J., "Variable resolution teleconferencing", International Conference on Pattern Recognition, October [17] Wiebe, K.J. and Basu, A., "Improving image and video transmission over ATM with prioritization and priority dithering", International Conference on Pattern Recognition, August Vitae Irene Cheng is a Research Associate in the department of Computing Science at the University of Alberta. She completed her B.S. and M.S. in Computing Science from the University of Alberta, and held the NSERC Graduate Research Scholarship. Ms. Cheng has worked with Lloyds Bank in the past. Anup Basu, is a Professor in the department of Computing Science at the University of Alberta. He completed his Ph.D. from the University of Maryland, Department of Computer Science in Dr. Basu has also worked for the Department of BioStatistics in Strong Memorial Hospital, Rochester, NY, and has been a Guest Professor at Technical University of Graz, Austria. Lei Chen is a Ph.D. student in the department of Computing Science at the University of Alberta. His research interests include Bandwidth Monitoring on the Internet, QoS based Multimedia delivery, and Stereo Vision.