Abstract-Random accesses are generally harmful to performance in hard disk drives due to more dramatic mechanical movement. This paper presents the design, implementation, and evaluation of Hot Random Off-loading (HRO), a self-optimizing hybrid storage system that uses a fast and small SSD as a bypassable cache to hard disks, with a goal to serve a majority of random I/O accesses from the fast SSD. HRO dynamically estimates the performance benefits based on history access patterns, especially the randomness and the hotness, of individual files, and then uses a 0-1 knapsack model to allocate or migrate files between the hard disks and the SSD. HRO can effectively identify files that are more frequently and randomly accessed and place these files on the SSD. We implement a prototype of HRO in Linux and our implementation is transparent to the rest of the storage stack, including applications and file systems. We evaluate its performance by directly replaying three real-world traces on our prototype. Experiments demonstrate that HRO improves the overall I/O throughput up to 39% and the latency up to 23%.

Abstract-Power consumption is an important issue for cluster supercomputers as it directly affects their running cost and cooling requirements. This paper investigates the memory energy efficiency of high-end data servers used for supercomputers. Emerging memory technologies allow memory devices to dynamically adjust their power states. To achieve maximum energy saving, the memory management on data servers needs to judiciously utilize these energy-aware devices. As we explore different management schemes under four real-world parallel I/O workloads, we find that the memory energy consumption is determined by a complex interaction among four important factors: (1) cache hit rates that may directly translate performance gain into energy saving, (2) cache populating schemes that perform buffer allocation and affect access locality at the chip level, (3) request clustering that aims to temporally align memory transfers from different buses into the same memory chips, and (4) access patterns in workloads that affect the first three factors.

Power consumption is an increasingly impressing concern for data servers as it directly affects running costs and system reliability. Prior studies have shown most memory space on data servers are used for buffer caching and thus cache replacement becomes critical. Temporally concentrating memory accesses to a smaller set of memory chips increases the chances of free riding through DMA overlapping and also enlarges the opportunities for other ranks to power down. This paper proposes a power and thermal-aware buffer cache replacement algorithm. It conjectures that the memory rank that holds the most amount of cold blocks are very likely to be accessed in the near future. Choosing the victim block from this rank can help reduce the number of memory ranks that are active simultaneously. We use three real-world I/O server traces, including TPC-C, LM-TBF and MSN-BEFS to evaluate our algorithm. Experimental results show that our algorithm can save up to 27% energy than LRU and reduce the temperature of memory up to 5.45 o C with little or no performance degradation.

In the presented paper, we proposed a common color model and designed the color judgment method, which is based on the HSV model. This method will translate the RGB values of the points in video images to HSV values, and use HSV values to recognize the color. After that, software of real-time video object recognition was developed based on color features, which is also based on their search of target color identification. Besides, the system is developed by VC based on OpenCV, which has achieved the goal of real-time video motion detection and object color recognition. Finally, the experimental results indicate that the algorithm is accurate and similar to human recognition of the moving objects in videos view, which demonstrates the good performance of the target identification and color judgment.

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