Checkpointing is a key enabler of hibernation, live migration and fault-tolerance for virtual machines (VMs) in mobile devices. However, checkpointing a VM is usually heavyweight: the VM's entire memory needs to be dumped to storage, which induces a significant amount of (slow) I/O operations, degrading system performance and user experience. In this paper, we propose FLIC, a fast and lightweight checkpointing machinery for virtualized mobile devices by taking advantages of recent byte-addressable, non-volatile memory (NVRAM). Instead of saving the VM's entire memory to storage, we store its working set pages in NVRAM, avoiding accessing slow flash memory (compared to server-grade SSDs). To further reduce the write activities to flash memory, we propose an energy-efficient data deduplication to eliminate redundant data in VM snapshot and save storage space. Experimental results based on an Exynos 5250 SoC show that our approach can effectively improve the performance of checkpointing in mobile virutalization and save energy.

Sparsity Preserving Projections (SPP) is a well known approach for feature extraction and dimensionality reduction. Its success is mainly attributed to its high quality graph which is constructed by sparse representation. As an instance of graph embedding, SPP can be formulated as regression model. Thus we apply the idea of collaborative graph embedding, which reformulates SPP as a collaborative representation model via imposing a L2-norm constraint to projections from the perspective of linear regression, to further enhance SPP. We call this novel SPP method Collaborative Sparsity Preserving Projections (CSPP). Experiment results on four popular face datasets, namely Yale, ORL, FERET and AR, show the effectiveness in feature extraction and the improvement of CSPP over SPP.

This paper presents an improved two-pass hexagonal (TPA) algorithm constituted by linear hashtable motion estimation algorithm (LHMEA) and hexagonal search (HEXBS) for motion estimation. In the TPA, motion vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of macroblocks (MBs). The hashtable structure of LHMEA is improved compared to the original TPA and LHMEA. The evaluation of the algorithm considers the three important metrics being processing time, compression rate and PSNR. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.