Data deduplication is a technology that eliminates redundant data to save storage space. Most previous studies on data deduplication target backup storage, where the deduplication ratio and throughput are important. However, data deduplication on primary storage has recently been receiving attention; in this case, I/O latency should be considered equally with the deduplication ratio. Unfortunately, data deduplication causes high sequential-read-latency problems. When a file is created, the file system allocates physically contiguous blocks to support low sequential-read latency. However, the data deduplication process rearranges the block mapping information to eliminate duplicate blocks. Because of this rearrangement, the physical sequentiality of blocks in a file is broken. This makes a sequential-read request slower because it operates like a random-read operation. In this paper, we propose a selective data deduplication scheme for primary storage systems. A selective scheme can achieve a high deduplication ratio and a low I/O latency by applying different data-chunking methods to the files, according to their file access characteristics. In the proposed system, file accesses are characterized by recent access time and the access frequency of each file. No chunking is applied to update-intensive files since they are meaningless in terms of data deduplication. For sequential-read-intensive files, we apply big chunking to preserve their sequentiality on the media. For random-read-intensive files, small chunking is used to increase the deduplication ratio. Experimental evaluation showed that the proposed method achieves a maximum of 86% of an ideal deduplication ratio and 97% of the sequential-read performance of a native file system.

With the availability of virtualization extension in mobile processors, e.g. ARM Cortex A-15, multiple virtual execution domains are efficiently supported in a mobile platform. Each execution domain requires high-performance graphics services for full-featured user interfaces such as smooth scrolling, background image blurring, and 3D images. However, graphics service is hard to be virtualized because multiple service components (e.g. ION and Fence) are involved. Moreover, the complexity of Graphical Processing Unit (GPU) device driver also makes harder virtualizing graphics service. In this paper, we propose a technique to virtualize the graphics architecture of Android mobile platform in KVM/ARM environment. The Android graphics architecture relies on underlying Linux kernel services such as the frame buffer memory allocator ION, the buffer synchronization service Fence, GPU device driver, and the display synchronization service VSync. These kernel services are provided as device files in Linux kernel. Our approach is to para-virtualize these device files based on a split device driver model. A major challenge is to translate guest-view of information into host-view of information, e.g. memory address translation, file descriptor management, and GPU Memory Management Unit (MMU) manipulation. The experimental results show that the proposed graphics virtualization technique achieved almost 84%-100% performance of native applications.