In recurring emergencies, frontline health care professionals (HCPs) have taken on a dual responsibility of not only serving as medical professionals for patients but also disseminating medical knowledge online for the public to alleviate their anxiety. They are expected to perform both online and offline organizational citizenship behavior (OCB). However, HCPs carry out their duties with high risks, inhibiting their OCB. This study aims to understand how to effectively and efficiently integrate online and offline support to facilitate HCP’s online and offline OCB in recurring emergencies. Data was collected from a survey among HCPs amid the second wave of the COVID-19 pandemic from March to April 2021. It was found that perceived support helps promote HCPs’ OCB through increasing optimism and resilience. Moreover, offline problem-focused support is more helpful than online emotion-focused support for HCPs with more knowledge of the emergency.

Enhancing low-light images improves both the visibility and quality of the images. Existing methods primarily focus on the enhancement process and heavily rely on the supervised learning strategy , where low/normal-light image pairs are used as the training dataset. In this paper, we propose a novel method called Learn Enhancement by Learning Degradation (LELD) to achieve efficient light adjustment and scene fidelity. We use a carefully designed degradation network (DNet) to guide the enhancement network (ENet). Specifically, the role of DNet is transforming normal-light images into low-light images. For better generalization ability , we employ an unsupervised learning strategy and a generative adversarial network framework. The training is totally dependent on unpaired datasets. Inspired by Retinex theory , we propose a fidelity loss to maintain color and detail during the degradation process . The ENet exhibits a straightforward architecture and achieves efficient enhancement. Experimental results demonstrate the advantages of our method over state-of-the-art methods in terms of visual quality and enhancement efficiency.

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 by-passable 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%.

Power consumption is an increasingly impressing concern for data servers as it directly affects running costs and system reliability. Prior studies have shown that most memory space on data servers is used for buffer caching and thus cache replacement becomes critical. Two conflicting factors of buffer caching impacts memory energy efficiency: (1) a higher hit rate reduces memory traffic and thus saves energy, (2) temporally concentrating memory accesses to a smaller set of memory chips increases the chances of "free riding" through DMA overlapping and also makes more memory chips have opportunities to power down. This paper investigates the tradeoff between these two interacting, sometimes conflicting factors and proposes three energy-aware buffer cache replacement algorithms: On a cache miss for a new block b in a file f, evict an victim block from (1)the most recently accessed memory chip, (2) the memory chip that is accessed most recently by file f, or (3) the memory chip that is accessed most recently by file f and whose last access block belongs to the same hot or cold categories as block b. Simulation results based on three real-world I/O traces, including TPC-R, MSN-BEFS and Exchange, show that our algorithms can save up to 24.9% energy with marginal degradation in hit rates. Our algorithms show degradation in response time in some experiments. We propose an off-line energy sub optimal replacement algorithm that serves as a theortical reference.

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°C with little or no performance degradation.

Indirect blocks, part of a file's metadata used for locating this file's data blocks, are typically treated indistinguishably from file's data blocks in buffer cache. This paper shows that this conventional approach will significantly detriment the overall energy efficiency of memory systems. Scattering small but frequently accessed indirected blocks over allmemory chips reduce the energy saving opportunities. We propose a new energy-efficient buffer cache management scheme, named MEEP, which separates indirect and datablocks into different memory chips. Our trace-driven simulation results show that our new scheme can save memory energy up to 16.8% and 15.4% in the I/O-intensive server workloads TPC-R and TPC-H, respectively.

Power consumption is an important issue for cluster supercomputers as it directly affects 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 and enable free rides by overlapping multiple DMA transfers from different I/O buses to the same memory device. 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 five real-world parallel I/O workloads, we find that the memory energy behavior 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 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.