USENIX 2001 Paper
[USENIX '01 Tech Program Index]
Measuring Thin-Client Performance Using Slow-Motion Benchmarking
S. Jae Yang, Jason Nieh, and Naomi Novik
Modern thin-client systems are designed to provide the same graphical interfaces and applications available on traditional desktop computers while centralizing administration and allowing more efficient use of computing resources. Despite the rapidly increasing popularity of these client-server systems, there are few reliable analyses of their performance. Industry standard benchmark techniques commonly used for measuring desktop system performance are ill-suited for measuring the performance of thin-client systems because these benchmarks only measure application performance on the server, not the actual user-perceived performance on the client.
To address this problem, we have developed slow-motion benchmarking, a new measurement technique for evaluating thin-client systems. In slow-motion benchmarking, performance is measured by capturing network packet traces between a thin client and its respective server during the execution of a slow-motion version of a standard application benchmark. These results can then be used either independently or in conjunction with standard benchmark results to yield an accurate and objective measure of the performance of thin-client systems.
We have demonstrated the effectiveness of slow-motion benchmarking by using this technique to measure the performance of several popular thin-client systems in various network environments on web and multimedia workloads. Our results show that slow-motion benchmarking resolves the problems with using standard benchmarks on thin-client systems and is an accurate tool for analyzing the performance of these systems.
The rising cost of support and maintenance for desktop systems has fueled a growing interest in thin-client computing. Modern thin-client systems are designed to provide the same graphical interfaces and applications available on desktop systems while centralizing computing work on powerful servers to reduce administration costs and make more efficient use of shared computing resources.
While the term "thin-client computing" has been used to refer to a variety of different client-server computing architectures, the primary feature common to most thin-client systems is that all application logic is executed on the server, not on the client. The user interacts with a lightweight client that is generally responsible only for handling user input and output, such as receiving screen display updates and sending user input back to the server over a network connection. Unlike older client-server architectures such as X , many modern thin-client systems even run the window system on the server. As a result, the client generally does not need many resources, thus requiring fewer upgrades, and can have a very simple configuration, reducing support costs. Because of the potential cost benefits of thin-client computing, a wide range of thin-client platforms has been developed. Some application service providers (ASPs) are even offering thin-client service over wide area networks such as the Internet [8, 10, 22].
The growing popularity of thin-client systems makes it important to develop techniques for analyzing their performance, to assess the general feasibility of the thin-client computing model, and to compare different thin-client platforms. However, because thin-client platforms are designed and used very differently from traditional desktop systems, quantifying and measuring their performance effectively is difficult. Standard benchmarks for desktop system performance cannot be relied upon to provide accurate results when used to measure thin-client systems. Because benchmark applications running in a thin-client system are executed on the server, these benchmarks effectively only measure the server's performance and do not accurately represent the user's experience at the client-side of the system. To make matters more difficult, many of these systems are proprietary and closed-source, making it difficult to instrument them and obtain accurate results.
To address this problem, we introduce slow-motion benchmarking, a new measurement technique for evaluating thin-client systems. In slow-motion benchmarking, performance is measured by capturing network packet traces between a thin client and its respective server during the execution of a slow-motion version of a standard application benchmark. These results can then be used either independently or in conjunction with standard benchmark results to yield an accurate and objective measure of user-perceived performance for applications running over thin-client systems.
To demonstrate the accuracy of this technique, we have used slow-motion benchmarking to measure the performance of four popular thin-client systems on both web and multimedia applications. The thin-client systems evaluated were Microsoft Terminal Services , Citrix MetaFrame , AT&T VNC , and Sun Ray . We measured the performance of these systems over various network access bandwidths, ranging from ISDN up to LAN network environments. Our results illustrate the performance differences between these thin-client systems and demonstrate the effectiveness of slow-motion benchmarking as a tool for analyzing thin-client system performance. We have also compared our results to the results obtained using standard application benchmarking approaches. These comparisons illustrate the limitations of previous thin-client benchmarking efforts based on widely-used industry standard benchmarks.
The rest of this paper is organized as follows. Section 2 describes how thin-client systems operate in further detail and then explains the difficulties inherent in measuring the performance of thin-client platforms with standard benchmarks. Section 3 presents the slow-motion benchmarking technique and discusses how it can be used to measure thin-client performance. Section 4 presents examples of benchmarks that we have modified for slow-motion benchmarking and describes how these may be used to evaluate thin-client systems. Section 5 compares slow-motion benchmarking to standard benchmarking by presenting experimental results that quantify the performance of popular thin-client systems on web and multimedia application workloads over different network bandwidths. Section 6 discusses related work. Finally, we summarize our conclusions and discuss opportunities for future work.
2 Measuring Thin-Client Performance
To provide sufficient background for discussing the issues in measuring thin-client performance, we first describe in further detail how thin-client systems operate. In this paper, we focus on thin-client systems in which a user's complete desktop computing environment, including both application logic and the windowing system, is entirely run on the server. This is the architecture underlying most modern systems referred to as thin-clients, such as Citrix MetaFrame and Microsoft Terminal Services. One of its primary advantages is that existing applications for standalone systems can be used in such systems without modification.
In this type of architecture, the two main components are a client application that executes on a user's local desktop machine and a server application that executes on a remote system. The end user's machine can be a hardware device designed specifically to run the client application or simply a low-end personal computer. The remote server machine typically runs a standard server operating system, and the client and server communicate across a network connection between the desktop and server. The client sends input data across the network to the server, and the server, after executing application logic based on the user input, returns display updates encoded using a platform-specific remote display protocol. The updates may be encoded as high-level graphics drawing commands, or simply compressed pixel data. For instance, Citrix MetaFrame encodes display updates as graphics drawing commands while VNC encodes display updates as compressed pixel data.
To improve remote display performance, especially in environments where network bandwidth is limited, thin-client systems often employ three optimization techniques: compression, caching, and merging. With compression, algorithms such as zlib or run-length encoding can be applied to display updates to reduce bandwidth requirements with only limited additional processing overhead. With caching, a client cache can be used to store display elements such as fonts and bitmaps, so the client can obtain some frequently used display elements locally rather than repeatedly requesting them from the server. With merging, display updates are queued on the server and then merged before they are sent to the client. If two updates change the same screen region, merging will suppress the older update and only send the more recent update to the client. In merging, display updates are sent asynchronously with respect to application execution, decoupling application rendering of visual output on the server from the actual display of the output on the client. Depending on the merging policy and the network speed, there may be a significant time lapse between the time application rendering occurs on the server and the time the actual display occurs on the client. These optimizations, particularly merging, can make analyzing thin-client performance difficult.
The performance of a thin-client system should be judged by what the user experiences on the client. There are two main problems encountered when trying to analyze the performance of thin-client systems. The first problem is how to correctly measure performance of the overall system rather than simply the server's performance. The second, more subtle problem is how to objectively measure the resulting display quality, particularly given the display update optimizations that may be employed. Three methods that have been used to measure thin-client system performance are internal application measurements with standard benchmarks, client instrumentation, and network monitoring. We discuss each of these methods below and their limitations.
The first approach, commonly used for traditional desktop systems, is to simply run a standard benchmark on the system. For instance, a video playback application could be run that reports the frame rate as a measure of performance. However, this does not provide an accurate measure of overall thin-client performance because the application programs are executed entirely on the server. Since application execution is often decoupled from client display, the results reported using internal application measurements might not be an accurate reflection of user-perceived performance on the client. The benchmark program often runs on the server irrespective of the status of the display on the client. A video playback application, for example, would measure the frame rate as rendered on the server, but if many of the frames did not reach the client, the frame rate reported by the benchmark would give an overly optimistic view of performance.
A second, more accurate measurement method would be to directly instrument the client. If appropriate tracing mechanisms could be inserted into the thin-client system to log input and output display events on the client, very detailed measurements of thin-client performance could be performed. However, many thin-client systems are quite complex and instrumenting them effectively would not be easy. Furthermore, the information that could be obtained within these systems would still not provide a direct measure of user-perceived display quality. Running a video playback application, for example, would result in thousands of display updates. One would still be left with the problem of how to determine how those display updates translate into actual video frames to determine how many video frames were delivered to the client. A more practical problem is that many of the most popular thin-client systems, such as Citrix MetaFrame, Windows Terminal Services, SCO Tarantella, and Sun Ray, are all proprietary, closed systems. Even the specification of the remote display protocol used in these systems is not available.
A third measurement method is network monitoring. While just measuring application performance on the server can be inaccurate and direct thin-client instrumentation is often not possible, monitoring network activity between the client and server during the execution of an application can give us a closer approximation to the user-perceived performance of that application on the client-side. We can measure the latency of operations such as web page downloads as the time between the start and end of client-server communication in response to those operations. However, while this method enables us to more accurately measure the latency of display updates for such operations, we are still left with the question of determining the resulting display quality. Fast performance is naturally an important metric, but it is insufficient when considered in isolation. The user's interactive experience is equally determined by the visual quality of the updates, and in many cases platforms may achieve high speed screen refresh rates by discarding data, which does not necessarily lead to good interactive performance from the user's perspective.
In particular, thin-client systems that use display update merging may drop interim display updates if the client cannot keep up with the rate of display updates generated on the server. A thin-client platform that uses this kind of policy will appear to perform well on benchmarks measuring only the latency of display updates, even at very low bandwidths, because it will simply discard whatever data cannot be transmitted quickly enough. This problem is exacerbated by most standard benchmarks for measuring graphical performance, which typically execute a rapid sequence of tasks with frequent display changes. For instance, the standard industry benchmark i-Bench  from Ziff-Davis measures performance on web applications with a rapid-fire series of web page downloads, each page triggering the download of the next page when complete. This technique works for traditional desktop systems, but on a thin-client system, the server can end up finishing one page download and starting the next long before the client has finished displaying the first page. The server may even stop sending the display updates associated with the first page and send the client on to the second regardless of whether the client has finished its display.
Monitoring the amount of data transferred for display updates at different network bandwidths can help to determine when display update merging is occurring, but other problems remain. For one, simple network monitoring cannot quantify the amount of display updates still being discarded at the highest available bandwidths. In addition, because each thin-client platform encodes display updates in its own proprietary protocol, network monitoring alone cannot determine whether the thin-client platforms are all transmitting the same overall visual display data, making it impossible to effectively compare the performance of the platforms to each other. Monitoring network traffic at the client is an improvement over server-side application measurements, but we still cannot correctly measure overall performance in a way that accounts for both system response time and display quality.
<![if !supportLists]> 3 Slow-Motion Benchmarking
To provide a more effective method for measuring thin-client performance, we introduce slow-motion benchmarking. In slow-motion benchmarking, we use network packet traces to monitor the latency and data transferred between the client and the server, but we alter the benchmark application by introducing delays between the separate visual components of that benchmark, such as web pages or video frames, so that the display update for each component is fully completed on the client before the server begins processing the next one.
Figure 1 and Figure 2 illustrate the difference in network traffic between standard and slow-motion versions of an i-Bench web benchmark that downloads a sequence of web pages. The benchmark is described in Section 4.1. The data presented is from measurements for one thin-client platform, Citrix MetaFrame, with a 100 Mbps network connection between client and server. In the standard benchmark with no delays, the pages run together and cannot be separately identified, even at this high network bandwidth. In the slow-motion version of the benchmark with delays inserted between each page download, the display update data for each separate page is clearly demarcated.
Figure 1: KB transferred at one-second intervals during a sequence of web page downloads with no delays under Citrix MetaFrame at 100 Mbps.
Figure 2: KB transferred at one-second intervals during a slow-motion version of the same web page sequence. For visual clarity, only a subset of the full 109-page sequence represented by Figure 1 is shown here.
With slow-motion benchmarking, we process the network packet traces and use these gaps of idle time between components to break up the results on a per-component basis. This allows us to obtain the latency and data transferred for each visual component separately. We can then obtain overall results by taking the sum of these results. The amount of the delay used between visual components depends on the application workload and platform being tested. The necessary length of delay can be determined by monitoring the network traffic and making the delays long enough to achieve a clearly demarcated period between all the visual components where client-server communication drops to the idle level for that platform. This ensures that each visual component is discrete and generated completely.
Slow-motion benchmarking has many advantages. First and most importantly, it ensures that display events reliably complete on the client so that capturing them using network monitoring provides an accurate measure of system performance. Slow-motion benchmarking ensures that clients display all visual components in the same sequence, providing a common foundation for making comparisons among thin-client systems.
Second, slow-motion benchmarking does not require any invasive modification of thin-client systems, which is difficult even for open-source systems such as VNC and nearly impossible for proprietary systems. Additionally, since no invasive instrumentation is required, slow-motion benchmarking does not result in any additional performance overhead for the thin-client system being measured.
Third, slow-motion benchmarking provides an effective way to determine when display updates are being discarded. Since the modified benchmarks run at a slower rate, the resource requirements are reduced, which in turn reduces the likelihood that display updates will be discarded. We can then compare the amount of data transferred for the standard and slow-motion versions of a given application benchmark to determine whether the display updates are being discarded even at the highest network bandwidths. In Section 4.2, we show how this is particularly useful for measuring the performance of video applications on thin-client systems.
Fourth, slow-motion benchmarking is actually closer to standard user behavior for some applications, notably interactive activities such as web browsing. Unlike the behavior of web benchmarks such as those in i-Bench, most users do not click through a hundred web pages in near-instantaneous succession; they wait for a page to visually complete loading before they move on to the next one.
Finally, the delays introduced with slow-motion benchmarking allow us to obtain results with finer granularity at the level of individual visual components. This is very useful for studying the effects of different thin-client remote display mechanisms on different kinds of display data. For instance, some platforms may prove to be better than others at downloading text-only web pages, while others may be superior at graphics-heavy pages. These more detailed results enable us to make better judgments about the various design choices made in each thin-client platform.
In designing slow-motion benchmarking, we made three assumptions. First, we assumed that introducing the delays between visual components would not inherently change the type or amount of data that should be transferred between client and server in a thin-client system. As far as we know, none of the thin-client platforms fundamentally alter the way they encode and send updates based on the amount of time between visual components.
Second, we assumed that there would not be extraneous packets in the data stream that would change our measurements. In particular, we assumed that the delays introduced between visual components would be directly reflected in noticeable gaps in the network packet traces captured. For almost all thin-client platforms and application benchmarks that we measured, there were no packets during the idle periods. However, on certain platforms such as Sun Ray, some small packets were transmitted even during idle periods, possibly to make sure that the connection had not been lost. However, we found that a judicious filtering process based on the volume of idle-time data allowed us to successfully distinguish the data transferred for the pages from the overhead.
Third, we assumed that monitoring network traffic generated by the slow-motion benchmarks was a valid measure of overall performance. Network monitoring allows us to completely measure network and server latency, but may not provide a complete end-to-end measure of client latency. Network monitoring does account for all client latency that occurs between the first and last packet generated for a visual component. However, this technique does not account for any client processing time required for displaying a visual component that occurs before the first network packet or after the last network packet for that component. The impact of this limitation depends on the importance of client latency to the overall performance. If, as we expected, network and server latency were the dominant factors in overall performance, the additional client latency would not be significant. However, if the client latency were a significant component of overall performance, network monitoring might not completely measure end-to-end performance. Client latency would typically be large if the client were heavily loaded. We therefore compensated for this limitation by monitoring client load with standard system monitoring tools such as perfmon and sysload to check whether the client was heavily loaded. We found that client load was generally not an issue and that the network was typically the primary performance bottleneck. The one instance in which this was not the case was for VNC running over high network bandwidths. However as we discuss in Section 5.2.2, we also instrumented VNC directly and found that the packet traces accounted for all client latency for this platform.
4 Examples of Slow-Motion Benchmarks
To illustrate how slow-motion benchmarking can be used in practice, we describe two examples of how application benchmarks can be modified to use slow-motion benchmarking. The two examples are taken from the Ziff-Davis i-Bench benchmark suite version 1.5 , a benchmarking tool that has been used by numerous computer companies and Ziff-Davis Labs for measuring the performance of a variety of desktop and thin-client systems. The i-Bench benchmarks used were the Web Text Page Load and MPEG1 Video benchmarks, which can be used to measure system performance on web-based and multimedia applications.
4.1 Web Text Page Load Benchmark
By using a slow-motion version of the Web Text Page Load benchmark modified along these lines, we can ensure that each web page is completely displayed on the client and measure performance on a per-page basis. As a result, we can conduct performance comparisons of different thin-client systems knowing that each of them is correctly displaying the same set of web pages. In addition, we can use the per-page measurements to determine how page download latency and the amount of data transferred varies with page content. By performing these measurements with various network bandwidths between client and server, we can determine how the response time of a thin-client system varies with network access bandwidth. Given a model of how frequently users move between web pages, we can use the slow-motion benchmarking measurements to determine whether a thin-client system can provide sufficient response time for a given network connection to ensure a good web browsing experience.
4.2 MPEG1 Video Benchmark
The MPEG1 Video benchmark measures the total time required to playback an MPEG1 video file containing a mix of news and entertainment programming. The video is a 34.75 second clip that consists of 834 352x240 pixel frames with an ideal frame rate of 24 frames per second (fps). The ideal frame rate is the rate a video player would use for playing the video file in the absence of resource limitations that would make this impossible. The total size of the video file is 5.11 MB. In running the video benchmark on a thin-client system, the video player would run on the server and decode and render the MPEG1 video on the server. The remote display protocol of the thin-client system would then send the resulting display updates to the client. Note that unlike streaming MPEG media systems that transmit MPEG video to the client for decoding, thin-client systems first decode the video on the server and then transmit display updates using their own remote display protocol.
There are two problems with using the unmodified MPEG1 Video benchmark for measuring thin-client performance. The first problem is that playback time alone is a poor measure of video performance. Some video players discard video frames when the system is not fast enough to decode and display every frame. The second problem is that in thin-client systems, the system itself may also drop video frames by discarding their corresponding display updates when the network between the client and server is congested. The resulting lower video quality is not properly accounted for by either the playback-time metric or the video player's accounting of dropped video frames.
We can address these problems by applying slow-motion benchmarking as follows. In this case, the visual components of the benchmark are the individual video frames. We can isolate these frames simply by reducing the video playback rate of the benchmark. The playback rate should be slow enough to ensure that there is enough time to decode and display each video frame before the next one needs to be processed. Although users would not actually watch video at such a greatly reduced playback rate, the measurements at this reduced playback rate can be used to establish the reference data transfer rate from the thin server to the client that corresponds to a "perfect" playback without discarded video frames. The data transfer rate can be calculated as the total data transferred divided by the total playback time. We can then compare the data transfer rate at the reduced playback rate with the corresponding full playback rate measurements to determine the video quality achieved at full playback rate. More specifically, we can use the following formula as a measure of video quality VQ at a given specified playback rate P:
For example, suppose playing a video at an ideal 24 fps rate takes half a minute and results in 10 MB of data being transferred while playing the video at a slow-motion ideal 1 fps rate takes 12 minutes and results in 20 MB of data being transferred. Then, based on the above formula, the resulting video quality VQ at 24 fps will be 0.5 or 50%, which is what one would expect since the 24 fps video playback discarded half of the video data. The effectiveness of this formula depends on the platform's ability to maintain the 1 fps frame rate. In our experiments, all of the platforms closely conformed to the frame rate.
While this metric provides a useful, non-invasive way to measure video quality, it only accounts for the amount of data discarded and does not account for the fact that some video data may be more important to the overall display quality of a given video sequence than other data. For instance, discarding display updates corresponding to a video frame that looks almost the same as both the previous and next video frames in a sequence would not change the perceived display quality as much as discarding updates corresponding to a video frame that is unlike any of its neighboring frames. At the same time though, as discussed in Section 2, many of the thin-client systems use some form of compression to reduce the data size of their display updates. Compression effectively reduces data size by removing redundant information in the data. If we assume that the amount of unique information in a display update is a measure of its importance, then a compressed display update could be viewed in a rough sense as being scaled according to its importance. In this case, the proposed measure of video quality based on the amount of discarded compressed data effectively accounts for the fact that different display data may be of different importance.
5 Experimental Results
To demonstrate the effectiveness of slow-motion benchmarking, we evaluated four popular thin-client platforms using the unmodified and slow-motion versions of the web and video benchmarks described in Section 4. The platforms we measured were Citrix MetaFrame, Windows Terminal Services, AT&T VNC, and Sun Ray. Section 5.1 describes our experimental design, including the hardware and software testbed we used, the thin-client platform configurations we tested, and the experiments we conducted. Sections 5.2 and 5.3 discuss our measurements and results comparing slow-motion benchmarking against using the standard unmodified benchmarks. The results also contrast the performance of different thin-client systems on web and video applications.
5.1 Experimental Design
Our testbed, shown in Figure 3, consisted of two pairs of client/server systems, a network simulator machine, a packet monitor machine, and a benchmark server. Only one client/server pair was active during any given test. The features of each system are summarized in Table 1, along with the SPEC95 performance numbers for each server system.
Figure 3: Testbed configuration.
The client/server systems included a Sun Ray thin client machine and a Sun server, and a PC client and server. The Sun thin server was used only for Sun Ray testing while the PC server was configured as a dual-boot machine to support the various Windows- and Linux-based thin-client systems. The Sun Ray client was considerably less powerful than the PC client, with only a 100 MHz uSPARC CPU and 8 MB of RAM compared to a 450 MHz Pentium II with 128 MB of RAM in the PC client. However, the large difference in client processing power was not a factor in our evaluations, as the client systems were not generally heavily loaded during testing.
As shown in Figure 3, the network simulator machine was placed between the thin client and thin server machines to control the bandwidth between them. This simulator ran a software package called The Cloud , which allowed us to vary the effective bandwidth between the two network interface cards installed in the system. The thin clients and thin servers were separated from one another on isolated 100 Mbps networks. The server-side network was then connected to one of the network interfaces in the network simulator PC, and the client-side network was connected to the other interface.
To ensure that this simulator did not itself introduce extra delay into our tests, we measured round-trip ping times from the client to the server at 100 Mbps, with and without the simulator inserted between the client and the server. There were no significant differences and round-trip ping times were roughly 0.6 ms in both cases.
To monitor the client-server network traffic, we used a PC running Etherpeek 4 , a software packet monitor that timestamps and records all packet traffic visible to the PC. As shown in Figure 3, we primarily used the packet monitor to observe client-side network traffic. In order to capture all packet traffic being sent in both directions between the thin client and server, we used hubs rather than switches in our testbed. Since traffic going through a hub is broadcast to all other machines connected to the hub, this enabled us to record network traffic between the client and server simply by connecting the packet monitor to the hub that the data was passing through.
A limitation of this network setup is that the hubs are half-duplex, so that traffic cannot be sent through the hub from client to server and from server to client concurrently. Since most data in these thin-client platforms is traveling from the server to the client in any case, it is unlikely that the half-duplex network added significant delay to our experiments.
Other options are possible, each with its disadvantages. One alternative would be to run a packet monitor on the thin client or thin server, but Etherpeek is highly resource-intensive and would undoubtedly adversely affect performance results. Furthermore, in the case of the Sun Ray thin client device, it is not possible to run a packet monitor locally on the client. Another alternative would be to use port-mirroring switches to support full-duplex network connections, but mirroring typically would only allow monitoring of either client to server traffic or vice versa, not both at the same time, as mirroring a duplex port in both directions simultaneously can result in packet loss .
Finally, we also had a separate benchmark server, which was used to run our modified version of the web page benchmark described in Section 4.1. To ensure that network traffic from the benchmark server did not interfere with the network connection between thin client and thin server, the benchmark server was connected to the testbed using a separate hub, as shown in Figure 3. Each thin server had two 100 Mbps network interfaces, one connected to the network simulator and through that to the client, the other connected to the benchmark server on a separate channel.
5.1.2 Thin-Client Platforms
The versions of the four thin-client systems tested are shown in the last column of Table 1. Citrix MetaFrame and Terminal Services were run with Windows 2000 servers while VNC and Sun Ray were run with UNIX servers, Linux and Solaris. It was necessary to use different server operating systems because Terminal Services is part of Windows 2000, VNC performs much better on UNIX than Windows , and Sun Ray only works with Solaris. However to minimize system differences across thin-client platforms, all platforms except for Sun Ray used the exact same server hardware and same client OS and hardware.
The thin-client platform configurations used for our experiments are listed in Table 2. To minimize application environment differences, we used common thin-client configuration options and common applications across all platforms whenever possible. Where it was not possible to configure all the platforms in the same way, we generally used default settings for the platforms in question.
For all of our experiments, the video resolution of the thin client was set to 1024x768 resolution with 8-bit color, as this was the lowest common denominator supported by all of the platforms. However, the Sun Ray client was set to 24-bit color, since the Sun Ray display protocol is based on a 24-bit color encoding. Displaying in 8-bit color requires the Sun Ray server to convert all pixels to a pseudo 8-bit color stored in 24 bits of information before they are sent over the network. As a result, displaying in 8-bit color reduces the display quality and increases the server overhead, but does not reduce the bandwidth requirements.
We ran the benchmarks described in Section 4 on each of the four thin-client platforms. We measured the platforms using both the standard unmodified benchmarks and their respective slow-motion versions. We used the network simulator to vary the network bandwidth between client and server to examine the impact of bandwidth limitations on thin-client performance. We measured performance at four network bandwidths, 128 Kbps, 1.5 Mbps, 10 Mbps, and 100 Mbps, roughly corresponding to ISDN, DSL/T1, and LAN network environments, respectively.
To run the Web Text Page Load benchmark, we used Netscape Navigator 4.72, as it is available on all the platforms under study. The browser's memory cache and disk cache were cleared before each test run. In all cases, the Netscape browser window was 1024x768 in size, so the region being updated was the same on each system. Nevertheless, Netscape on Windows 2000 performs somewhat differently from Netscape on Linux and Solaris. For instance, in the Unix version, fonts appear smaller by default and a blank gray page appears between page downloads. These effects would tend to increase the amount of data that would need to be transferred on screen updates using a Unix-based thin-client platform. Our experience with various thin-client platforms indicate that these effects are minor in general, but should be taken into account when considering small thin-client performance differences across Unix and Windows systems.
To run the MPEG1 Video benchmark, we used Microsoft Windows Media Player version 6.4.09.1109 for the Windows-based thin clients and MpegTV version 1.1 for the Unix-based thin clients. In order to facilitate a fair comparison between all platforms despite using two different players, we configured the two players so they had the same size video window and otherwise appeared as similar as possible. Since the only portion of the display that is updated is the video window, both Unix- and Windows-based thin clients are effectively performing the same tasks.
5.2 Web Benchmark Results
5.2.1 Standard Benchmark Results
Figure 4 and Figure 5 show the results of running the unmodified Web Text Page Load benchmark on each of the thin-client platforms. Figure 4 shows the total latency for the unmodified benchmark on each platform. To provide some context for these results, a per-page latency of less than one second has been shown to be desirable to ensure that the flow of a user's browsing experience is not interrupted . Given the 109 web pages in the Web Text Page Load benchmark, a total latency of less than 109 seconds is necessary for good performance.
Figure 4: Total latency for unmodified web benchmark. Using Sun Ray, the benchmark did not complete at 128 Kbps.
Figure 5: Total data transferred for unmodified web benchmark.
At first glance, it appears that VNC performs extremely well, maintaining the same low latency across all bandwidths and outperforming the other platforms, 46% faster than its nearest competitor, RDP, at 100 Mbps, while Sun Ray appears to perform much worse than the other platforms, 20% slower than RDP at 100 Mbps. In addition, both Citrix and VNC still appear to be performing well on the benchmark even at 128 Kbps with average per-page download speed of less than 1 second. However, examining the data transferred results in Figure 5 shows that VNC discards a substantial amount of display data at lower bandwidths, while the other platforms transmit a consistent amount of data and slow down playback as necessary.
This highlights the problems with the results from the standard benchmark. Because we do not know exactly how the data is being encoded and compressed under each platform, we have no way of establishing a baseline for how much data should be transferred to the client by each system. As a result, we have no way of knowing whether the pages are being fully transferred to the client, even at 100 Mbps. We also cannot be sure that each platform is transmitting updates corresponding to the same pages, so the data transfer results are not an accurate measure of the relative efficiency of the platforms. As a result, we cannot draw conclusions about the relative performance of the systems when they are effectively being tested on different sequences of pages.
Visual observation of the platforms during the course of the test revealed another weakness of the standard benchmark. The pages stream by at such a fast rate that the sequence is not a realistic model of web browsing behavior. Real users typically do not interact with a browser in a rapid-fire manner but rather wait for a page to load before clicking on to the next page. This rapid rate causes a "pipelining" effect that hides the latency that results when each page is loaded from a standing start, which would be experienced in typical use.
5.2.2 Slow-Motion Benchmark Results
Figure 6 and Figure 7 show the results of running the slow-motion version of the Web Text Page Load benchmark on the four thin-client platforms. Figure 6 shows the total latency for downloading the 109 web pages, calculated as the sum of the individual page download latencies. A progressive improvement in performance with increased bandwidth is now visible for all of the platforms, even VNC Linux, which showed exaggerated performance at lower bandwidth under the unmodified benchmark.
Figure 6: Total latency for slow-motion web benchmark.
Figure 7: Total data transferred for slow-motion web benchmark.
As shown in Figure 7, the amount of data transferred now remains almost constant for all of the platforms across all bandwidths. However, we note that VNC transmits slightly less data at lower bandwidths because it uses a client-pull update policy in which each display update is sent in response to an explicit client request. At low network bandwidths, each display update takes longer to transmit, resulting in the client sending fewer update requests and receiving fewer display updates. The unsent interim updates are merged by the server. This does not affect the overall results as we are only interested in the total per-page latency for displaying the entire viewable web page. The absence of interim updates received at high bandwidths when the client can send more update requests does not affect the final visual quality or per page download latency.
Comparing Figure 4 and Figure 6, the measurements show that the total latency for the slow-motion benchmark is from 10% (for Sun Ray) to 63% (for VNC) higher than for the standard unmodified benchmark. There are three reasons for the difference in latency. First, none of the thin-client platforms discard display updates for the slow-motion benchmark. A comparison of Figure 5 and Figure 7 shows that VNC no longer discards display updates for pages in the slow-motion benchmark as it did for the unmodified benchmark. VNC transfers more data in the slow-motion case even at 100 Mbps, indicating that VNC was discarding data even at the highest bandwidth when running the unmodified benchmark. Second, in using the slow-motion benchmark, each web page is downloaded from a standing start after the previous page is completely downloaded. None of the latency is hidden by "pipelining" page downloads. Third, for Citrix and RDP, there were two web pages, pages 23 and 49 in the second iteration of downloading the pages, that consistently took 3-4 seconds to download for the slow-motion benchmark that did not take as long in the unmodified benchmark. We discovered that the long delays were due to an unusual interaction between Netscape and these two thin-client platforms. While these extra delays were not present when using the unmodified benchmark, the slow-motion benchmark provides a more realistic measurement of web browsing performance.
Figure 6 shows that all of the thin-client platforms deliver acceptable web browsing performance at LAN network bandwidths and that all of the platforms except Sun Ray provide sub-second performance at 1.5 Mbps as well. As shown in Figure 7, since Sun Ray provides higher quality 24-bit display as opposed to the 8-bit displays of the other platforms, it consumes much more network bandwidth, resulting in lower performance at low bandwidths. Note that none of the platforms provide good response time at 128 Kbps, despite the claims made by Citrix and Microsoft that their thin-client platforms can deliver good performance even at dialup modem speeds.
Overall, VNC and Sun Ray were faster at higher network bandwidths while Citrix and RDP performed better at lower network bandwidths. This suggests that the more complex optimizations and higher-level encoding primitives used by Citrix and RDP are beneficial at lower network bandwidths when reducing the amount of data transferred significantly reduces network latency. However, the simpler architectures of VNC and Sun Ray have lower processing overhead and hence perform better when bandwidth is more plentiful and data transfer speed is not the dominant factor.
Slow-motion benchmarking also allows us to obtain actual per-page results. Figure 8 shows a subset of the per-page latency results for one of the platforms, VNC. Due to space limitations, we only include the latency, but the per-page data transferred can also be obtained. For all pages except one, VNC provides excellent web browsing performance with page download latencies well below a second. Much information about the way the different platforms handle different types of pages is hidden by the aggregate results, but with the standard unmodified benchmark it is impossible to obtain the per-page data.
Figure 8: Per-page latency for VNC Linux running the slow-motion benchmark at 100 Mbps.
To further validate the accuracy and appropriateness of the slow-motion benchmarking technique, we internally instrumented the open-source platform VNC. By instrumenting VNC, we could obtain end-to-end latency measurements that also completely include any client latency. We repeated the experiments with the instrumented version of VNC and compared the results with the packet capture data. The slow-motion results using network monitoring were verified to be within 4.3% of the instrumented VNC results in measuring the total data transferred and within 1.1% in recording the total latency. Furthermore, there was little variance in the results corresponding to each individual page across multiple runs. Of all the thin-client platforms measured, VNC had the highest client load and yet the slow-motion network monitoring results and internal instrumentation results showed little difference. The main reason for this is that the VNC client sends a message back to the server when it has finished processing the latest display update. As a result, the packet traces completely capture the client latency without direct client instrumentation.
An important benefit of slow-motion benchmarking for measuring interactive responsiveness is the reproducibility of the results. One way to measure interactive performance is to monitor actual user activity, but it is essentially impossible for a user to repeat the exact same set of experiments with the exact same timing characteristics. In contrast, slow-motion benchmarking can be used to provide better reproducibility of results. We gauged the reproducibility of the slow-motion benchmark data by calculating the standard deviation after five trials of each test. The largest standard deviation observed was 4.7% of the mean, but typically 3% or lower.
5.3 Video Benchmark Results
5.3.1 Standard Benchmark Results
Figure 9 and Figure 10 show the results of running the standard unmodified MPEG1 Video benchmark on the four thin-client platforms. Figure 9 shows the playback time for the MPEG video benchmark at the ideal frame rate of 24 fps. Unfortunately, playback time remained relatively static on all of the platforms and did not correspond with the subjective performance, which degraded rapidly at lower bandwidths.
Figure 9: Playback time for unmodified video benchmark. Using Sun Ray, the benchmark did not complete at 128 Kbps.
Figure 10: Total data transferred in unmodified video benchmark at 24 fps, and in the slow-motion video benchmark at 100 Mbps bandwidth and 1 fps. Sun Ray data transferred at 100 Mbps was equivalent at both frame rates.
This subjective observation is supported by the data transfer measurements. Figure 10 shows the data transferred during playback, which degrades rapidly at lower bandwidths even when playback time remains low. For instance, the data transferred by VNC at 100 Mbps is 30 times greater than that transferred at 128 Kbps despite the near-constant playback time. Clearly, we cannot use the playback time alone as a measure of the video quality because not all the frames are being fully displayed. The amount of data transferred must be incorporated into any metric of video quality.
We could represent the video quality as a percentage of the ideal data transfer rate. However, this ideal data transfer rate cannot be determined with the unmodified benchmark. If we assumed that the 100 Mbps rate was the ideal, we might conclude that all of the platforms perform well at both 100 Mbps and 10 Mbps: they maintain a high playback time and transmit roughly the same amount of data at both bandwidths. This does not correlate with the subjective performance: visually, only Sun Ray achieved good performance even at 100 Mbps.
5.3.2 Slow-Motion Benchmark Results
Slow-motion benchmarking again allows us to clarify the picture. Figure 10 also shows the amount of data transferred when the benchmark was run in slow-motion at a frame rate of 1 fps with network bandwidth of 100 Mbps. At this frame rate, bandwidth limitations were not an issue and each frame of the video was transmitted separately and fully displayed on the client before the subsequent frame was begun. This yields a baseline by which to measure the results from the standard benchmark, using the formula for video quality described in Section 4.2.
Figure 11 shows this measure of video quality for each of the platforms. We can now obtain a clearer picture of how well each of the platforms perform at high bandwidths and in comparison to each other, despite the nearly-level playback time seen in Figure 9.
Figure 11: Video quality as percentage of data transferred in the slow-motion video benchmark.
Out of all the thin-client platforms, Sun Ray alone achieves good performance, with 96% video quality at 100 Mbps despite the fact that it sends an order of magnitude more data than any other platform at 24 fps. None of the other platforms has good performance even at LAN bandwidths. The fact that Sun Ray sends much more data than any other platform indicates that the poor performance of these other platforms at 100 Mbps is not due to bandwidth limitations but is rather due to their display update mechanisms, which are poorly suited to video applications.
6 Related Work
In this paper, we have focused on thin-client systems in which both applications and the window system are completely executed on the server. These systems are the most popular thin-client systems today and many of them have been developed [4, 5, 15, 16, 24, 26, 27, 30, 32].
Three other types of systems that are sometimes referred to as thin-client systems are network window systems, browser-based systems, and remote control computing systems. The most notable example of a network window system is the X Window system . Unlike the systems discussed in this paper, X runs the window system on the client and as a result requires more substantial client resources in order to perform well. To run X applications over lower bandwidth networks, a low-bandwidth X (LBX) proxy server extension  was developed and released as part of X11R6.3. Browser-based systems employ a web browser client as a user interface to an application server. These systems require applications to be modified to support a web-based interface. Remote control computing systems such as Laplink  and PC Anywhere  enable users to remotely control a PC by sending screen updates to remote client PCs. They also run all application and window system logic on the server, but they do not support multiple users at the same time.
There have been several studies of thin-client performance that have focused on evaluating one or two systems. Danskin conducted an early study of the X protocol  and Schmidt, Lam, and Northcutt examined the performance of Sun Ray . Both of these studies relied on source code access for internal system instrumentation. Thin-client platform vendors such as Citrix and Microsoft have done internal performance testing of their products as well, but have not published any reliable experimental results [4, 16,17]. Wong and Seltzer studied the performance of Windows NT Terminal Server for office productivity tools and web browsing  by monitoring network traffic generated from a real user session. This provides a human measure of user-perceived performance, but makes repeatable results difficult. Tolly Research measured the performance of Citrix MetaFrame on various scripted application workloads , however the study suggests that problems in using standard scripted application workloads as described in this paper were not properly considered.
7 Conclusions and Future Work
We have introduced slow-motion benchmarking, a new measurement technique that requires no invasive instrumentation and yet provides accurate measurements for evaluating thin-client systems. Slow-motion benchmarking introduces delays into standard application benchmarks to isolate the visual components of those benchmarks. This ensures that the components are displayed correctly on the client when the benchmark is run, even when the client display is decoupled from the server processing as in many thin-client systems. Slow-motion benchmarking utilizes network traffic monitoring at the client rather than relying on application measurements at the server to provide a more complete measure of user-perceived performance at the client.
We have demonstrated the effectiveness of slow-motion benchmarking on a wide range of popular thin-client platforms. Our quantitative results show that slow-motion benchmarking provides far more accurate measurements than standard benchmarking approaches that have been used for evaluating thin-client systems. Our comparisons across different thin-client systems indicate that these systems have widely different performance on web and video applications. Our results suggest that current remote display mechanisms used in thin-client systems may be useful for web browsing at lower network bandwidths. However, these same mechanisms may adversely impact the ability of thin-client systems to support multimedia applications.
We are currently using slow-motion benchmarking to evaluate a wide range of thin-client platforms in different network environments. As ASPs continue to increase in popularity, one important area of research is evaluating the performance of thin-client computing in wide-area network environments. Slow-motion benchmarking provides a useful tool for characterizing and analyzing the design choices in thin-client systems to determine what mechanisms are best suited for supporting future wide-area computing services.
<![if !supportLists]> 8 Acknowledgements
We thank Haoqiang Zheng for developing the instrumented version of VNC used in our experiments. Haoqiang, Albert Lai, Rahul Joshi, and Carla Goldburg, all assisted with many of the thin-client performance measurements and helped set up the thin-client testbed. Allyn Vogel of Ziff-Davis Media, Inc. provided us with valuable information on i-Bench. We also thank the anonymous USENIX referees and our shepherd Vern Paxson, who provided helpful comments on earlier drafts of this paper. This work was supported in part by an NSF CAREER Award and Sun Microsystems.
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This paper was originally published in the
Proceedings of the 2001 USENIX Annual Technical Conference, June
25Ð30, 2001, Boston, Massachusetts, USA.
Last changed: 3 Jan. 2002 ml