A Robust Fault-Tolerant and Scalable Cluster-wide Deduplication for Shared-Nothing Storage Systems

Deduplication has been largely employed in distributed storage systems to improve space efficiency. Traditional deduplication research ignores the design specifications of shared-nothing distributed storage systems such as no central metadata bottleneck, scalability, and storage rebalancing. Further, deduplication introduces transactional changes, which are prone to errors in the event of a system failure, resulting in inconsistencies in data and deduplication metadata. In this paper, we propose a robust, fault-tolerant and scalable cluster-wide deduplication that can eliminate duplicate copies across the cluster. We design a distributed deduplication metadata shard which guarantees performance scalability while preserving the design constraints of shared- nothing storage systems. The placement of chunks and deduplication metadata is made cluster-wide based on the content fingerprint of chunks. To ensure transactional consistency and garbage identification, we employ a flag-based asynchronous consistency mechanism. We implement the proposed deduplication on Ceph. The evaluation shows high disk-space savings with minimal performance degradation as well as high robustness in the event of sudden server failure.Comment: 6 Pages including reference

Development of Compact and High-efficient Scroll Compressor with Novel Bearing Structure

High-Side Shell(HSS) scroll compressors have been widely used for Variable Refrigerant Flow(VRF) system which is a powerful solution for the cooling and heating of commercial buildings. In order to improve the characteristics of the VRF system, a new HSS scroll compressor has been developed with a novel bearing structure. The core elements of the novel bearing structure are an outer-type bearing mounted on an orbiting scroll and a female-type eccentric journal inside of a shaft. The outer-type bush bearing which is made of engineering plastic without a back steel layer has been newly developed. The new HSS scroll compressor employing the novel bearing structure has a compact size, high efficiency, and low noise level compared to a conventional HSS scroll compressor. In order to confirm the advantages of the new HSS scroll compressor, basic tests and theoretical analysis have been performed in this study

An Analytical Model-based Capacity Planning Approach for Building CSD-based Storage Systems

The data movement in large-scale computing facilities (from compute nodes to data nodes) is categorized as one of the major contributors to high cost and energy utilization. To tackle it, in-storage processing (ISP) within storage devices, such as Solid-State Drives (SSDs), has been explored actively. The introduction of computational storage drives (CSDs) enabled ISP within the same form factor as regular SSDs and made it easy to replace SSDs within traditional compute nodes. With CSDs, host systems can offload various operations such as search, filter, and count. However, commercialized CSDs have different hardware resources and performance characteristics. Thus, it requires careful consideration of hardware, performance, and workload characteristics for building a CSD-based storage system within a compute node. Therefore, storage architects are hesitant to build a storage system based on CSDs as there are no tools to determine the benefits of CSD-based compute nodes to meet the performance requirements compared to traditional nodes based on SSDs. In this work, we proposed an analytical model-based storage capacity planner called CSDPlan for system architects to build performance-effective CSD-based compute nodes. Our model takes into account the performance characteristics of the host system, targeted workloads, and hardware and performance characteristics of CSDs to be deployed and provides optimal configuration based on the number of CSDs for a compute node. Furthermore, CSDPlan estimates and reduces the total cost of ownership (TCO) for building a CSD-based compute node. To evaluate the efficacy of CSDPlan, we selected two commercially available CSDs and 4 representative big data analysis workloads

Fast and Accurate Home Photo Categorization for Handheld Devices using MPEG-7 Descriptors

Home photo categorization has become an issue for practical use of photos taken with various devices. But it is a difficult task because of the semantic gap between physical images and human perception. Moreover, the object-based learning for overcoming this gap is hard to apply to handheld devices due to its computational overhead. We present an efficient image feature extraction method based on MPEG-7 descriptors and a learning structure constructed with multiple layers of Support Vector Machines for fast and accurate categorization of home photos. Experiments on diverse home photos demonstrate outstanding performance of our approach in terms of the categorization accuracy and the computational overhead

MicroRNA-143 and-145 modulate the phenotype of synovial fibroblasts in rheumatoid arthritis

Fibroblast-like synoviocytes (FLSs) constitute a major cell subset of rheumatoid arthritis (RA) synovia. Dysregulation of microRNAs (miRNAs) has been implicated in activation and proliferation of RA-FLSs. However, the functional association of various miRNAs with their targets that are characteristic of the RA-FLS phenotype has not been globally elucidated. In this study, we performed microarray analyses of miRNAs and mRNAs in RA-FLSs and osteoarthritis FLSs (OA-FLSs), simultaneously, to validate how dysregulated miRNAs may be associated with the RA-FLS phenotype. Global miRNA profiling revealed that miR-143 and miR-145 were differentially upregulated in RA-FLSs compared to OA-FLSs. miR-143 and miR-145 were highly expressed in independent RA-FLSs. The miRNA-target prediction and network model of the predicted targets identified insulin-like growth factor binding protein 5 (IGFBP5) and semaphorin 3A (SEMA3A) as potential target genes downregulated by miR-143 and miR-145, respectively. IGFBP5 level was inversely correlated with miR-143 expression, and its deficiency rendered RA-FLSs more sensitive to TNFα stimulation, promoting IL-6 production and NF-κB activity. Moreover, SEMA3A was a direct target of miR-145, as determined by a luciferase reporter assay, antagonizing VEGF165-induced increases in the survival, migration and invasion of RA-FLSs. Taken together, our data suggest that enhanced expression of miR-143 and miR-145 renders RA-FLSs susceptible to TNFα and VEGF165 stimuli by downregulating IGFBP5 and SEMA3A, respectively, and that these miRNAs could be therapeutic targets. © 2017 KSBMB4

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It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used

Urinary Metabolite Profiling Combined with Computational Analysis Predicts Interstitial Cystitis-Associated Candidate Biomarkers

Interstitial cystitis/painful bladder syndrome (IC) is a chronic syndrome of unknown etiology that presents with bladder pain, urinary frequency, and urgency. The lack of specific biomarkers and a poor understanding of underlying molecular mechanisms present challenges for disease diagnosis and therapy. The goals of this study were to identify noninvasive biomarker candidates for IC from urine specimens and to potentially gain new insight into disease mechanisms using a nuclear magnetic resonance (NMR)-based global metabolomics analysis of urine from female IC patients and controls. Principal component analysis (PCA) suggested that the urinary metabolome of IC and controls was clearly different, with 140 NMR peaks significantly altered in IC patients (FDR < 0.05) compared to that in controls. On the basis of strong correlation scores, fifteen metabolite peaks were nominated as the strongest signature of IC. Among those signals that were higher in the IC group, three peaks were annotated as tyramine, the pain-related neuromodulator. Two peaks were annotated as 2-oxoglutarate. Levels of tyramine and 2-oxoglutarate were significantly elevated in urine specimens of IC subjects. An independent analysis using mass spectrometry also showed significantly increased levels of tyramine and 2-oxoglutarate in IC patients compared to controls. Functional studies showed that 2-oxoglutarate, but not tyramine, retarded growth of normal bladder epithelial cells. These preliminary findings suggest that analysis of urine metabolites has promise in biomarker development in the context of IC