161 research outputs found
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
When Do Firms Add Digital Platforms? Organizational Status as an Enabler to Incumbents’ Platformization
Prior research has expanded our understanding of the platform business and its success factors, but scant attention has been paid to the launch of digital platforms by “pipeline” firms. Our study examines the effect of a firm’s status on the strategic decision to launch a digital platform and its consequences. By analyzing panel data of Fortune China 500 companies, we found that high-status incumbents are more likely to add a digital platform than their low-status counterparts, indicating that status can be seen as a promoter of launching digital platforms. However, once a digital platform is added, high-status firms are slower in improving performance than their low-status counterparts. Thus, status may serve as an inhibitor of a firm’s dedication to the new platform business. This research contributes to our understanding of the social contingency of digital transformation and the important constraints that must be overcome for incumbent firms to successfully transit
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A Synthetic Form of Frizzled 8-Associated Antiproliferative Factor Enhances p53 Stability through USP2a and MDM2
Frizzled 8-associated Antiproliferative Factor (APF) is a sialoglycopeptide urinary biomarker of interstitial cystitis/painful bladder syndrome (IC/PBS), a chronic condition of unknown etiology with variable symptoms that generally include pelvic and/or perineal pain, urinary frequency, and urgency. We previously reported that native human APF suppresses the proliferation of normal bladder epithelial cells through a mechanism that involves increased levels of p53. The goal of this study was to delineate the regulatory mechanism whereby p53 expression is regulated by APF. Two APF-responsive cell lines (T24 bladder carcinoma cells and the immortalized human bladder epithelial cell line, TRT-HU1) were treated with asialo-APF (as-APF), a chemically synthesized form of APF. Biochemical analysis revealed that as-APF increased p53 levels in two ways: by decreasing ubiquitin specific protease 2a (USP2a) expression leading to enhanced ubiquitination of murine double minute 2 E3 ubiquitin ligase (MDM2), and by suppressing association of p53 with MDM2, thus impairing p53 ubiquitination. Biological responses to as-APF were suppressed by increased expression of wild type, but not mutant USP2a, which enhanced cell growth via upregulation of a cell cycle mediator, cyclin D1, at both transcription and protein levels. Consistent with this, gene silencing of USP2a with siRNA arrested cell proliferation. Our findings suggest that APF upregulates cellular p53 levels via functional attenuation of the USP2a-MDM2 pathway, resulting in p53 accumulation and growth arrest. These data also imply that targeting USP2a, MDM2, p53 and/or complex formation by these molecules may be relevant in the development of novel therapeutic approaches to IC/PBS
Improving I/O Resource Sharing of Linux Cgroup for NVMe SSDs on Multi-core Systems
Abstract In container-based virtualization where multiple isolated containers share I/O resources on top of a single operating system, efficient and proportional I/O resource sharing is an important system requirement. Motivated by a lack of adequate support for I/O resource sharing in Linux Cgroup for high-performance NVMe SSDs, we developed a new weight-based dynamic throttling technique which can provide proportional I/O sharing for container-based virtualization solutions running on NUMA multi-core systems with NVMe SSDs. By intelligently predicting the future I/O bandwidth requirement of containers based on past I/O service rates of I/O-active containers, and modifying the current Linux Cgroup implementation for better NUMAscalable performance, our scheme achieves highly accurate I/O resource sharing while reducing wasted I/O bandwidth. Based on a Linux kernel 4.0.4 implementation running on a 4-node NUMA multi-core systems with NVMe SSDs, our experimental results show that the proposed technique can efficiently share the I/O bandwidth of NVMe SSDs among multiple containers according to given I/O weights
Scene-Adaptive Video Frame Interpolation via Meta-Learning
Video frame interpolation is a challenging problem because there are
different scenarios for each video depending on the variety of foreground and
background motion, frame rate, and occlusion. It is therefore difficult for a
single network with fixed parameters to generalize across different videos.
Ideally, one could have a different network for each scenario, but this is
computationally infeasible for practical applications. In this work, we propose
to adapt the model to each video by making use of additional information that
is readily available at test time and yet has not been exploited in previous
works. We first show the benefits of `test-time adaptation' through simple
fine-tuning of a network, then we greatly improve its efficiency by
incorporating meta-learning. We obtain significant performance gains with only
a single gradient update without any additional parameters. Finally, we show
that our meta-learning framework can be easily employed to any video frame
interpolation network and can consistently improve its performance on multiple
benchmark datasets.Comment: CVPR 202
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
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