Waiting Time versus Utility to Download Images

Transcription

1 Waiting Time versus Utility to Download Images Kazutomo NOMURA, Kyoko YAMORI, Eiji TAKAHASHI, Takumi MIYOSHI, and Yoshiaki TANAKA Graduate School of Science and Engineering, Waseda University Global Information and Telecommunication Institute, Waseda University Faculty of Systems Engineering, Shibaura Institute of Technology Okinawa Research Center, Telecommunications Advancement Organization of Japan Nishi-Waseda, Shinjuku-ku, Tokyo, Japan Abstract The utility of a contents delivery network depends on the waiting time. This paper deals with the priority control of contents delivery networks from the viewpoint of utility. First, a subjective evaluation experiment on image downloading is carried out to obtain the relationship between the waiting time and the utility. From the results, a function of the utility versus the waiting time is estimated. Then, this function is applied to the optimization of the priority control of an image delivery system. This system has two classes, and it is analyzed by computer simulation. The ratio of high priority packets to low priority packets is changed as a parameter, and the total utility of all users is maximized. The total utility of a two-class system is shown to be higher than that of a one-class system. 1. Introduction A user s utility in a contents delivery network (CDN) depends on the waiting time until the start of the delivery service. For example, a shorter waiting time results in a higher utility, while a longer waiting time results in a lower utility. Considering the user s utility in such a service, the total utility of all users changes with the priority control [1][2]. If we exchange the rate of a higher priority class with that of a lower class, the optimum ratio of the higher and lower classes in terms of the traffic load gives the maximum utility. In this paper, from the viewpoint of the utility, we discuss the relationship between the waiting time and the utility, and represent the effect of priority control from the results obtained. Chapter 2 introduces a method of measuring the utility, and a subjective evaluation experiment. Chapter 3 gives an outline of this experiment in terms of the utility versus the waiting time. Chapter 4 shows results of this experiment. Chapter 5 estimates a utility measure function from the results of the experiment. Chapter 6 describes a simple traffic model of packet switching to evaluate the waiting time for this service. Chapter 7 shows that utility with priority control is better than without priority control. Finally, Chapter 8 summarizes the obtained results and gives concluding remarks. 2. Subjective Evaluation Experiment In this paper, we describe the relationship between the waiting time and the utility from a subjective evaluation experiment, while considering a simple contents delivery system involving the downloading of images, such as pictures. The subjective evaluation experiment allows assessors to actually experience objects and evaluate them based on their subjectivity. It is difficult to know one s utility (satisfaction) clearly, but not so difficult to know one s utility relative to one s preference. Namely, we quantitatively analyze a factor that influences a user s selection from an experiment. The subjective evaluation is made under the same conditions and basis as the standardized method in spite of different circumstances. This makes the comparison easy and precise. The organization called the International Telecommunication Union Radiocommunication Sector (ITU-R), provides the methodology for the quality evaluation of television pictures [3] [6]. Our subjective evaluation experiment was carried out under the methodology. The methodology is known as Rec. ITU-R BT (Methodology for the Subjective Assessment of the Quality of Television Pictures (Recommendation ITU-R BT )). We adopted Variant II of the double-stimulus impairment scale (DSIS) method in the methodology as the presentation structure of test materials. In this method, the absolute quality Reference and the quality Test that we want to evaluate are each shown twice by turns. Reference is always shown first. The principal points we should consider when carrying out the subjective evaluation experiment written

2 in Rec. ITU-R BT are as follows. (a) Observers (BT ) At least 15 observers should be employed. They should be non-experts, in the sense that they are not directly concerned with the same quality standards as in their normal work, and are not experienced assessors. (Preliminary findings have suggested that non-expert observers can yield more critical results when exposed to higher quality transmission and display technologies.) (b) Instructions for the assessment (BT ) The assessors should be carefully introduced to the assessment method, the types of impairment or quality factors likely to occur, the grading scale, the sequence, and the timing. In the case of quality assessment, the quality can be defined as consisting of specific perceptual attributes. (c) The test session (BT ) A session should last up to half an hour. At the beginning of the first session, about five Dummy presentations should be introduced to stabilize the observers opinions. The data issued from these presentations must not be taken into account in the results of the test. We carried out the subjective evaluation experiment while being aware of the above-mentioned items. 3. Outline of This Experiment This experiment was carried out according to Variant II of the DSIS method. Figure 1 shows an example screen of this experiment. The assessors viewed the test web pages on laptop PCs. The specifications of the machines and tools that we used are shown in Table 1. They clicked links on the pages and browsed images on their browsers. Then, we called those images indicated with no waiting time after clicking the Reference condition, and those images indicated with some waiting time between the click and the indication the Test condition. The assessors evaluated, based on their subjectivity, the waiting time of the Test condition in comparison to that of the Reference condition. At this time, the length of the waiting time was hidden to the assessors to nullify any effects on their subjectivity. How the presentation was made in the actual experiment is shown in Figure 2. Each indication of the Reference condition and Test condition was continued for 20 sec, and both were done twice in turns with 5 sec intervals. The assessors compared the delay of the Reference condition and the delay of the Test condition, and noted down how they felt about the waiting time on a score sheet. This was one presentation. This Figure 1 A screen of the experiment. Table 1 Specifications of machines and tools. Video memory 2MB Monitor 10.4-inch TFT LC (SVGA) Pixels Mouse Microsoft WheelMouse optical Browser Netscape Communicator 4.7 5HIHUHQFH Figure 2 VHF VHF 7HVW &RPSDUHWKHVHLQGLFDWLRQV DQGHYDOXDWH VHF VHF VHF VHF VHF 5HIHUHQFH 7HVW WLPH The process of this experiment. Table 2 Five-grade impairment scale. 5 imperceptible 4 perceptible, but not annoying 3 slightly annoying 2 annoying 1 very annoying experiment consisted of 15 presentations including two dummies. The evaluation was based on the five-grade impairment scale shown in Table 2, and the score sheet used for the experiment is shown in Figure 3. The assessors showed their evaluations by marking a horizontal line on the graph shown in Figure 3. They marked the line

3 LPSHUFHSWLEOH SHUFHSWLEOHEXWQRWDQQR\LQJ VOLJKWO\DQQR\LQJ DQQR\LQJ OHQJWK PP 8WLOLW\ 8 Ž Œ Š 8 $ W N $ N :DLWLQJ WLPH W 026YDOXH 8 $H NW $ N YHU\DQQR\LQJ Figure 4 MOS value and utility measure function. Figure 3 Score sheet. at a point corresponding to their estimation, which was anywhere on the graph. In a sense, this scale can be regarded as a continuous scale. The length of the graph on the score sheet was 100mm. The evaluated value was the length between the bottom of the graph and the point where the assessor marked the line. Then, we normalized the evaluated value, that is, the top of the graph was 1. The subjective evaluation experiment was based on the above regulation, and the Mean Opinion Score (MOS) of the utility corresponding to the waiting time was obtained. It took about 27 minutes to complete all the evaluations in this experiment. The average image data size was about kbytes. 4. Results of This Experiment The number of assessors was 19 (15 males and four females). The MOS scores were obtained from their evaluated values excluding the maximum and minimum values. When the number of assessors is n and the evaluated value of assessor i is a i, the MOS value is given by MOS = 1 n n a i. (1) i=1 Each MOS is shown by in Figure 4 as obtained from the experiment. We recognized the MOS value to be the user s utility. In this graph, t is the waiting time for the indication of an image and U is the utility corresponding to the waiting time. In Figure 4, a MOS value showing a longer waiting time results in a lower utility. Considering the decrease of the utility corresponding to the increase of the waiting time, the utility decreases more when t is small than when t is big. For example, the difference in the utility between when t is 1 sec and when t is 3 sec is larger than that between when t is 10 sec and when t is 12 sec though the difference in the waiting time is 2 sec for both cases. That is, a slight increase in the waiting time has a large influence on the utility when the waiting time is short, but it has a small influence when the waiting time is long. 5. Utility Measure Let us obtain the relationship between the waiting time for the indication of an image and the utility, from the results of the experiment. In Chapter 4, the decrease of the utility corresponding to the increase of the waiting time is larger when the waiting time is short than when it is long. Denoting the waiting time by t, the utility by U, and the logarithm of U by u, we have u = log e U. (2) The coefficient of correlation between u and t is 0.975, indicating a very strong positive correlation. Then, we get a relative equation between u and t by the least squares method as follows. u = 0.216t (3) From Equations (2) and (3), we can estimate the measure function for U =0.965e 0.216t. (4) The proportion of this approximation curve is 0.951, and the observed significance level of the test is The proportion shows the validity of the regression equation, and is also called the coefficient of

4 determination. If the proportion is closer to 1, the fitness of the approximate equation for the evaluated value is better. Accordingly, the observed significance level of the test is an indicator showing the statistical significance of the regression equation. The significance level of this test is These values show this approximation to be very suitable. On the other hand, the coefficient of correlation between u and T, which is defined as the logarithm of t, is also calculated. It is The measure function is given by U =1.15t (4 ) The proportion is 0.791, and the observed significance level of the test is These values show that this approximation is also suitable. In this paper, function (4) is used, but function (4 ) can be used as the utility measure function under certain circumstances. These functions are shown in Figure Evaluation Priority Model Based on the utility measure function for the waiting time estimated in Chapter 5, we analyzed the effect of the priority control by simulation. The model for the analysis is shown in Figure 5. We assumed a packet switching system with two priority classes. Packets were put into a specific queue according to their priority classes on arrival. First, a waiting queue of high priority packets was managed based on FIFO. It was assumed that higher priority packets preempted lower class packets in service if any, and the preempted remaining packets were transmitted afterward based on FIFO. In this simulation, the demand for the contents (images) was of Poisson origination. The sizes of the images showed the exponential distribution, and that average image size was 18 kbytes. The image data was divided into several packets of 1,500 Byte size, and the last data was made into a packet. The size of the packet was equal to the remaining data size. The waiting time was the length from the time the first packet of each image arrived till the last packet finished being forwarded. The utility of each image was obtained according to the utility measure function (Equation (4)) estimated in Chapter 4. The total utility of all users, U all, was used as the evaluation figure in this simulation. We define U all as follows, U all = ρu. (5) ρ is the utilization rate of the line. U, the average of the utilities, is given by dividing the total utility of all forwarded images by the total data number. The rate of the high priority packets, R, is defined by R = the volume of high priority packets. (6) the volume of all packets 7. Effect of Priority Control In this chapter, the total utility U all with the priority control and that without the priority control are compared. This comparison was performed on several kinds of ρ and R and the transmission speed. We carried out examinations when ρ was 0.95 and 0.8, and the transmission speed was 1.5Mbps and 8WLOLW\8 DOO Figure 6 0ESVZLWKSULRULW\FRQWURO 0ESVZLWKRXWSULRULW\FRQWURO ínesvzlwksulrulw\frqwuro ínesvzlwkrxwsulrulw\frqwuro 5DWLRRISULRULW\SDFNHWV5 Results of simulation (ρ=0.8). &RQWHQWV 3DFNHW +LJKSULRULW\SDFNHWV /RZSULRULW\SDFNHWV 8VHU 8WLOLW\8 DOO 0ESVZLWKSULRULW\FRQWURO 0ESVZLWKRXWSULRULW\FRQWURO NESVZLWKSULRULW\FRQWURO NESVZLWKRXWSULRULW\FRQWURO 5DWLRRISULRULW\SDFNHWV5 Figure 5 Model for the analysis of priority control. Figure 7 Results of simulation (ρ=0.95).

5 128kbps. The relationship between U all and R is shown in Figure 6 (ρ = 0.8) and Figure 7 (ρ = 0.95). If we compare Figure 6 with Figure 7, it is clear that the priority control on ρ = 0.95 is more effective than on ρ = 0.8. Next, looking at Figure 7, we can see that the priority control is more effective when the transmission speed is 128kbps than when it is 1.5Mbps. This conforms to ρ = 0.8. The priority control is also effective when R is over 0.5, that is, the volume of priority packets is more than a half of the volume of all packets. 8. Conclusion In this paper, a subjective evaluation experiment was carried out in order to clarify the relationship between the waiting time in an image delivery service and the utility. As a result, the utility of users for the waiting time showed exponential variations. Simulations were conducted in order to confirm the effect of priority control from the viewpoint of utility. As a result, it was found that priority control is more effective when the network is crowded than when it is less crowded, and also that it is more effective when the transmission speed is low than when it is high. In addition, it is more effective when the volume of high priority packets occupies over a half of all packets than when it is under a half. In this paper, the relationship between the waiting time for static images and the utility was obtained in the range of seconds. Of course, when bigger files like video are downloaded, the waiting time will be in the range of minutes or hours. In such cases, we will need to plan more appropriate subjective evaluation experiments because it will be difficult to carry out the same subjective evaluation experiment as that in this paper. It might also be useful to describe the utility corresponding to the waiting time in the range of seconds, minutes, and hours as one function. We hope to describe it as one utility measure function. Priority, IEEE International Conference on Networking, Colmal, France, pp , [2] A. Watanabe, T. Takine, M. Murata, and H. Miyahara, Which Provides Better Utility for Users, Best Effort Service or Reservation Based Service?, in Japanese, Trans. of IEICE, Vol. J83- B, No. 6, pp , June [3] Y. Kuwako, and K. Sezaki, Media Synchronization on Distributed Environment, in Japanese, Technical Report. of IEICE, SSE98 100, Sep [4] N. Narita, Comparisons Between the DSCQS Method and the SSQS Method Used in Absolute Evaluation of Picture Quality Distribution and Processing of Opinion Scores, in Japanese, Trans. of IEICE, Vol. J78-D-II, No. 12, pp , Dec [5] S. Matsumoto, I. Fukuda, H. Morino, K. Hikichi, K. Sezaki, and Y. Yasuda, The Influence of Network Issues on Haptic Collaboration in Shared Virtual Environments, Fifth PHANToM Users Group Workshop, Colorado, the U. S., Oct [6] ITU R, Recommendation ; Methodology for the subjective assessment of television pictures, ITU R Recommendations. Acknowledgements We would like to thank Prof. Kaoru Sezaki of the University of Tokyo, and his students for their cooperation in the experiment. Part of this study was from results of the project entitled Research on Pricing Controlled Highly Efficient Network at the Advanced Research Institute for Science and Engineering, Waseda University. References [1] K. Yamori, Y. Tanaka, and H. Akimaru, Price Optimization of Contents Delivery Systems with