TRANSCODING CACHE FOR SMART PHONES

Transcription

1 TRANSCODING CACHE FOR SMART PHONES Pitiphong Phongpattranont Department of Computer Engineering Chulalongkorn University Bangkok, Thailand Abstract In this paper, we present Transcoding Cache (TC), a new way for caching data in smart phone application using web service. Transcoding Cache adapts the cache data for better utilization of limited space. Without changing the cache algorithm, TC works by transcoding the cache data in the disk cache. Our experiments show that TC performs better than the normal cache system in term of miss ratio and data loaded through the network. In particular, we can reduce the data transmitted through the mobile network up to 5% comparing to the legacy cache system. Keywords Mobile; Cache; Transcoding; Web Cache; Web; Image; Web Service I. INTRODUCTION In mobile environment, network access is one of the most expensive operations []. Caching data in the mobile devices can contribute to lower resources consumption. Network does not only cost for usage fee, but it also reduces the device s battery life as well. There exist many studies of cache algorithms. Recently, more focuses have moved to data that can be discarded by improving the choices of victims to be replaced. Adaptive algorithms and self-optimized algorithms [] are examples of such studies. These algorithms offer advantage to mobile environments where workloads depend on the usage patterns at the moment. Most mobile devices are used for content consumption. In several cases, users just skim through the content rather than looking at the whole details. In the real world, many web sites detect the screen size of clients devices and deliver the appropriate image size. This technique helps reducing the data transmitted through the network to the mobile devices. In this paper, we propose a new method for caching items. Our method can adapt to both users behaviors and limitation of mobile devices. The rest of this paper is organized as follows. Section II describes the related works. Section III describes the design of our Transcoding Cache System. Section IV describes the experimental results. Section V is conclusions and future works. II. RELATED WORKS In this section, we review Universal Mobile Caching and Least Recently Used. Krerk Piromsopa Department of Computer Engineering Chulalongkorn University Bangkok, Thailand A. Universal Mobile Caching Universal Mobile Caching (UMC) [] is caching algorithm policy which uses a set of simple criteria as a basic policy. Universal Mobile Caching algorithms are based on ranking objects by their cost of retrieval ( ( )) and replacing the objects with the lowest retrieval cost. The formula we use in this work is; ( ) ( ( ( )) ( ( )) ( ( )) UMC uses small set of objects properties as criteria which are ( ) is the access likelihood for the files ( ) is the cost of retrieval of the data ( ) is the benefit of evicting the cached data For our work, we selected the following properties as estimators. The frequency of accessing data as the access likelihood. ( ). The size of the image data as the cost of retrieving data. ( ). If the cached data is big, it will give more space to the cache system for caching data entry. The size of the cached data as the benefit from evicting data. ( ). If the image data is big, it will require more data transmission. B. Least Recently Used Least Recently Used (LRU) [2] is caching algorithm policy which discards the least recently used data. III. DESIGN Our observation of users behaviors found that users usually skimmed through their photos while browsing or viewing photos. In most cases, the images size is bigger than the screen size of mobile devices (as shown in Figure 5). If users just skim through the photos, we can resize image to fit the mobile device screen before caching it. With this method, we can save the cache data size by resizing the image data to fit the screen size of mobile device. This will reduce the space for caching data and help increasing the number of cache entry in the system. 4

2 Figure Original image Figure 3 Image resized to 64x96 pixels Figure 2 Converted to gray scale image In our work, we do not change the cache algorithm. However, we change the behavior of cache data. It is important to note that we are looking only at the images data in this work. Other caching systems, when user access data, will collect the criteria data for the replacement algorithm and cached the accessed data for later use. In our work, the system will cache the accessed data by transcoding the data before saving it for cache in order to improve caching performance. Transcoding process can be done in many ways. For image data, we can resize image, reduce image s color depth or change compression algorithm. For video data, we can resize the video size, change video compression algorithm. For text data, we can simply compress the text data. Figure 4 Image with higher compression ratio Figure to 4 show example images that are transcoded in many ways. Figure is the original image. Figure 2 is the image that is converted to gray scale color. Figure 3 is the image that is resized to the 64x96 mobile device s screen. Figure 4 is the image that is compressed with higher compression ratio. From these examples, we hardly see the difference between Figure and Figure 3 but we can see the difference between Figure and Figure 2 and we can notice the difference between Figure and Figure 4. In this work, we primarily focus on the image data. We choose image resizing and changing compression algorithm and compression ratio as the transcoding process. We choose these two techniques in this work because users can notice the color depth reducing easier than the change on the image size. The image is resized to fit the screen of the mobile device and compressing with JPEG lossy format with medium quality 4

3 Average number of data in cache Mobile Screen. 96x64 pixels Normal Cache Transcoding 32x48 Image 248x536 pixels Figure 5 Example of image compare to mobile device screen compression [3]. The main idea is to save the space used for caching the transcoded data. When a cached image is being accessed, the system will return the resized image. If a user requests for the original data (full image), the system will treat such access as a miss and retrieve the original data again without transcoding it. In typical disk cache, we usually set the limit of disk cache size. Transcoding Cache system, which transcode the caching Transcoding 64x96 Figure 6 Average numbers of data in cache data, reduces the cache data file size and makes room for more cache data entry. Therefore it reduces the miss ratio of the cache system. Subsequently, it also reduces the data transmitted through the network. For better result, we need the cache algorithm which works best on the mobile environments. We choose 2 algorithms for our evaluation. The algorithms are Universal Mobile Caching (UMC) cache algorithms and Least Recently Used (LRU). IV. EXPERIMENTAL RESULTS We have conducted simulation-based experiments based on the real usage of users to evaluate our transcoding cache. Our Miss Ratio 32x48 pixel Miss Ratio 64x96 pixel.5.5 Byte Miss Ratio 32x48 pixel Byte Miss Ratio 64x96 pixel.5.5 Figure 7 32x48 pixels miss ratio Figure 8 64x96 pixels miss ratio 42

4 Byte loaded 32x48 pixel Data Saved 32x48 LRU UMC Byte loaded 64x96 pixel 4 2 Data Saved 64x96 LRU UMC Figure 9 Data loaded simulation evaluated hit ratios, number of data transmission. We tested the performance of transcoded caching system and evaluated hit ratios and byte data transmission. We chose Least Recently Used and Universal Mobile Caching algorithms for caching algorithms in our experiment for comparing the performance of the transcoding cache with that of normal cache. We conduct our tests and compare the result of normal cache system with our transcoding cache system by controlling the disk cache size and the caching algorithms. A. Traces Traces are collected from the real world usage by users. We recorded the data accessing behavior from users. The trace contains 473 of images with 6 requests. All images are JPEG image files with resolution up to 248x248 pixels. Twenty percent of requests are the full images requests. The screen resolutions of mobile devices used in the experiments are 32x48 and 64x96 pixels. B. Results TABLE I CACHED FILE SIZE (%) Screen Size 32x48 64x96 Cached File Size (%) Table I shows the average file size of the transcoded data. For screen with resolution 32x48 pixels, Transcoding cache system Figure Bytes saved can reduce the caching data size by 93%. For screen with resolution 64x96 pixels, the system can reduce the caching data size by 78%. The average time for resize image is.79 second, which is very small resource consumption compare to data transmission through network. Figure 6 shows number of cache data in the system with fixed disk cache size. Transcoding Cache can help cache system to cache more data entry than that of normal cache system. Figure 7 shows the results for screen with resolution 32x48 pixels. Transcoding cache system performs better than normal cache system. This is due to the transcoding cache system has more cached entries than those of the normal cache system which helps the transcoding cache system decrease the miss ratio. The 6 cache size system has the most differences in term of performance and will reduce when the cache size is increased Figure 8 shows the results for screen with resolution 64x94 pixels. Transcoding cache system also performs better than normal system. The 64 cache size system has the most difference in term of performance. Figure 9 shows the number of data that the caches system used to loaded data through the network. In Figure, we show how many data transmitted is saved comparing to that of the normal cache systems. In the lower screen resolution, the result shows that the transcoding can save a lot of data transmitted 43

5 through the network up to 5 % for mobile device with screen resolution 32x48 pixels and up to 2 % for 64x96 pixels system. V. CONCLUSIONS AND FUTURE WORK Our major contribution in this paper is the Transcoding Cache for smart phones. We have demonstrated the ability to transcode the cached data that based on the users behavior for reducing the cached data size and making more room for cached data. In summary, we see the Transcoding Cache for smart phones perform better than the normal cache system. The comparison is based on the real-world usage images data. We can improve the performance of Transcoding Cache System by improving the cache algorithm to consider the transcoded data model. That should improve the performance of the caching system. In this work, we use resize image function built in the mobile OS. We can improve the result of the transcoded image by using the Seam carving for content-aware image resizing technique [4] or using the better image compression algorithms e.g. JPEG2 [5] which can reduce more file size while preserving the image quality [6]. [7] Raphaël Grosbois, Touradj Ebrahimi Diego Santa-Cruz, "JPEG 2 performance evaluation and assessment," Signal Processing: Image Communication, vol. 7, no., pp. 3-3, January 22. REFERENCES [] Gernot Heiser Aaron Carroll, "An Analysis of Power Consumption in a Smartphone," in USENIX Association Berkeley, CA, 2. [2] Ahmed Amer, Panos K. Chrysanthis Ganesh Santhanakrishnan, "Towards Universal Mobile Caching," in ACM, New York, 25, pp [3] Yuanyuan Zhou. (2, Apr.) Definitions of various cache algorithms. [Online]. HYPERLINK " u_html/node3.html" u_html/node3.html [4] Matthew J. Zukoski, Kenneth D. Hopper, and Paul L. Stagg Tunc A. lyriboz, "A comparison of wavelet and Joint Photographic Experts Group lossy compression methods applied to medical images," Journal of Digital Imaging, vol. 2, no., pp. 4-7, 999. [5] Ariel Shamir Shai Avidan, "Seam carving for content-aware image resizing," in SIGGRAPH, California, 27. [6] R. Colin Johnson. (999, December) JPEG2 wavelet compression spec approved. [Online]. HYPERLINK " wavelet-compression-spec-approved" wavelet-compression-spec-approved 44